In recent years decentralized currencies developed through Blockchains are increasingly becoming popular because of their transparent nature and absence of a central controlling authority. Though a lot of computation power, disk space, and energy are being used to run this system, most of these resources are dedicated to just keeping the bad actors away by using Proof of Work, Proof of Stake, Proof of Space, etc., consensus. In this paper, we discuss a way to combine those consensus mechanism and modify the defense system to create actual values for the end-users by providing a solution for securely storing their data in a decentralized manner without compromising the integrity of the blockchain.
Abstract Background The T helper 17 (Th17)/T regulatory (Treg) cell imbalance is involved in the course of obesity and type 2 diabetes mellitus (T2DM). In the current study, the exact role of glucagon-like peptide-1 receptor agonist (GLP-1RA) exenatide on regulating the Th17/Treg balance and the underlying molecular mechanisms are investigated in obese diabetic mice model. Methods Metabolic parameters were monitored in db/db mice treated with/without exenatide during 8-week study period. The frequencies of Th17 and Treg cells in vivo and in vitro were assessed. The phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt)/Forkhead box O1 (FoxO1) pathway was detected by western blotting. Results Exenatide treatment obviously improved β-cell function and insulitis. Increased Th17 and decreased Treg cells in peripheral blood were present as diabetes progressed while exenatide corrected this imbalance. Progressive IL-17 + T cell infiltration of pancreatic islets was alleviated by exenatide intervention. In vitro study showed that palmitate could promote Th17 but suppress Treg differentiation along with down-regulating the phosphorylation of PI3K/Akt/FoxO1, which could be reversed by exenatide intervention. FoxO1 inhibitor AS1842856 could abrogate all these effects of exenatide against lipid stress. Conclusions Exenatide could protect β-cell function in db/db mice partially by restoring Th17/Treg balance via PI3K/Akt/FoxO1 pathway.
The question of how to get the best results out of the data we have is an everlasting problem in data science. The two main approaches to tackle the problem are top-k queries and skyline queries. Since their introduction, a new paradigm called flexible skylines has emerged. The aim of this survey is to provide a solid comparison between the new and the old approaches, understanding and exploring their differences and similarities.
As part of the investigation done by the IEEE Standards Association P2957 Working Group, called Big Data Governance and Metadata Management, the use of persistent identifiers (PIDs) is looked at for tackling the problem of reproducible research and science. This short paper proposes a preliminary method using PIDs to reproduce research results using time-series data. Furthermore, we feel it is possible to use the methodology and design for other types of datasets.
Multi-objective optimization is the problem of optimizing simultaneously multiple objective functions and several techniques exist to deal with this problem. This paper aims to present the main methods that can be used to solve this issue in the context of relational databases. In particular, this work examines Top-k query to get the k best result from a dataset and Skyline query that provides a more general overview of the best results. We also discuss Flexible-skyline, a new method designed to improve upon the previous techniques, mitigating their shortcomings. For each method, we describe the main characteristics and present an overview of the algorithms implementing such thecniques, while comparing advantages and disadvantages.
Skyline and ranking queries are two of the most used tools to manage large data sets. The former is based on non-dominance, while the latter on a scoring function. Despite their effectiveness, they have some drawbacks like the result size or the need for a utility function that must be taken into account. To do this, in the last years, new kinds of queries, called flexible skyline queries, have been developed. In the present article, a description of skyline and ranking queries, f-skyline queries and a comparison among them are provided to highlight the improvements achieved and how some limitations have been overcome.
Marcelo Arenas, Claudio Gutierrez, Juan F. Sequeda
Graphs have become the best way we know of representing knowledge. The computing community has investigated and developed the support for managing graphs by means of digital technology. Graph databases and knowledge graphs surface as the most successful solutions to this program. The goal of this document is to provide a conceptual map of the data management tasks underlying these developments, paying particular attention to data models and query languages for graphs.
Abstract Background Diabetic patients are prone to osteoporosis. Both zoledronic acid and metformin have certain anti-osteoporotic effects.Here we explore the anti-osteoporotic effect of the combination of two drugs. Methods 12-week-old DB/DB mice were divided into DB/DB group, zoledronic acid group,metformin group and zoledronic acid combined with metformin group, WT mice were treated as WT group alone. The mice were killed after ten weeks. Then using Micro-ct to scan the tibia and stain the contralateral lower limbs with HE. Results First, we find that the body weight of DB/DB mice treated with metforminre stable and their blood glucose reduce.Second, after HE staining,it is observed under light microscope that there are a large number of adipocytes, few bone trabeculae, few osteoblasts and osteoclasts in the bone marrow cavity in the DB/DB group compared with the WT group, while the number of bone trabeculae in the combined treatment group is higher than that in the zoledronic acid group or metformin group, and a large number of blood cells, blood vessels and adipocytes are found in the bone marrow cavity of the combined drug group compared with the zoledronic acid group. Last, the results of Micro-ct present that, comparing with the DB/DB group and the metformin group,SMI is significantly different(P < 0.05);comparing with the DB/DB group and the zoledronic acid group,Tb.N,Tb.Sp,Conn.D and SMI are significantly different(P < 0.05);BV/TV, Conn.D, SMI, BMD, Tb.N and Tb.Sp in the combination treatment group are significantly difference than those in the DB/DB group(P < 0.05). Conclusion In term of the bone mass lose of DB/DB mice, the treatment of the zoledronic acid combined with metformin outperforms that using the zoledronic acid or metformin only.
We describe some approaches to explanations for observed outcomes in data management and machine learning. They are based on the assignment of numerical scores to predefined and potentially relevant inputs. More specifically, we consider explanations for query answers in databases, and for results from classification models. The described approaches are mostly of a causal and counterfactual nature. We argue for the need to bring domain and semantic knowledge into score computations; and suggest some ways to do this.
Normalized relational databases are a common method for storing data, but pulling out usable denormalized data for consumption generally requires either direct access to the source data or creation of an appropriate view or table by a database administrator. End-users are thus limited in their ability to explore and use data that is stored in this manner. Presented here is a method for performing automated denormalization of relational databases at run-time, without requiring access to source data or ongoing intervention by a database administrator. Furthermore, this method does not require a restructure of the database itself and so it can be flexibly applied as a layer on top of already existing databases.
Numerous fundamental database and reasoning problems are known to be NP-hard in general but tractable on instances where the underlying hypergraph structure is $β$-acyclic. Despite the importance of many of these problems, there has been little success in generalizing these results beyond acyclicity. In this paper, we take on this challenge and propose nest-set width, a novel generalization of hypergraph $β$-acyclicity. We demonstrate that nest-set width has desirable properties and algorithmic significance. In particular, evaluation of boolean conjunctive queries with negation is tractable for classes with bounded nest-set width. Furthermore, propositional satisfiability is fixed-parameter tractable when parameterized by nest-set width.
Log-Structured Merge (LSM) Trees provide a tiered data storage and retrieval paradigm that is attractive for write-optimized data systems. Maintaining an efficient buffer in memory and deferring updates past their initial write-time, the structure provides quick operations over hot data. Because each layer of the structure is logically separate from the others, the structure is also conducive to opportunistic and granular optimization. In this paper, we introduce the Skiplist-Based LSM Tree (sLSM), a novel system in which the memory buffer of the LSM is composed of a sequence of skiplists. We develop theoretical and experimental results that demonstrate that the breadth of tuning parameters inherent to the sLSM allows it broad flexibility for excellent performance across a wide variety of workloads.
Computers play a crucial role in modern ancestry management, they are used to collect, store, analyze, sort and display genealogical data. However, current applications do not take into account the kinship structure of a natural language. In this paper we propose a new domain-specific language KISP which is based on a formalization of English kinship system, for accessing and querying traditional genealogical trees. KISP is a dynamically typed LISP-like programming language with a rich set of features, such as kinship term reduction and temporal information expression. Our solution provides a user with a coherent genealogical framework that allows for a natural navigation over any traditional family tree.
A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graph-oriented operations and type constructors. In this article we present the basic notions of graph databases, give an historical overview of its main development, and study the main current systems that implement them.
The Apriori algorithm is a classical algorithm for the frequent itemset mining problem. A significant bottleneck in Apriori is the number of I/O operation involved, and the number of candidates it generates. We investigate the role of LSH techniques to overcome these problems, without adding much computational overhead. We propose randomized variations of Apriori that are based on asymmetric LSH defined over Hamming distance and Jaccard similarity.
Regular path query languages for data graphs are essentially \emph{untyped}. The lack of type information greatly limits the optimization opportunities for query engines and makes application development more complex. In this paper we discuss a simple, yet expressive, schema language for edge-labelled data graphs. This schema language is, then, used to define a query type inference approach with good precision properties.
Data science is gaining more and more and widespread attention, but no consensus viewpoint on what data science is has emerged. As a new science, its objects of study and scientific issues should not be covered by established sciences. Data in cyberspace have formed what we call datanature. In the present paper, data science is defined as the science of exploring datanature.