bluuebunny/crossref_metadata_2025 huggingface dataset:
Hybrid storage management for database systems
Authors: Liu Xin , Salem Kenneth
Published: June 2014

The use of flash-based solid state drives (SSDs) in storage systems is growing. Adding SSDs to a storage system not only raises the question of how to manage the SSDs, but also raises the question of whether current buffer pool algorithms will still work effectively. We are interested in the use of hybrid storage systems, consisting of SSDs and hard disk drives (HDDs), for database management. We present cost-aware replacement algorithms, which are aware of the difference in performance between SSDs and HDDs, for both the DBMS buffer pool and the SSDs. In hybrid storage systems, the physical access pattern to the SSDs depends on the management of the DBMS buffer pool. We studied the impact of buffer pool caching policies on SSD access patterns. Based on these studies, we designed a cost-adjusted caching policy to effectively manage the SSD. We implemented these algorithms in MySQL's InnoDB storage engine and used the TPC-C workload to demonstrate that these cost-aware algorithms outperform previous algorithms.
Ratio threshold queries over distributed data sources
Authors: Gupta Rajeev , Ramamritham Krithi , Mohania Mukesh
Published: June 2014

Continuous aggregation queries over dynamic data are used for real time decision making and timely business intelligence. In this paper we consider queries where a client wants to be notified if the ratio of two aggregates over distributed data crosses a specified threshold. Consider these scenarios: a mechanism designed to defend against distributed denial of service attacks may be triggered when the fraction of packets arriving to a subnet is more than 5% of the total packets; or a distributed store chain withdraws its discount on luxury goods when sales of luxury goods constitute more than 20% of the overall sales. The challenge in executing such ratio threshold queries (RTQs) lies in incurring the minimal amount of communication necessary for propagation of updates from data sources to the aggregator node where the client query is executed. We address this challenge by proposing schemes for converting the client ratio threshold condition into conditions on individual distributed data sources. Whenever the condition associated with a source is violated, the source pushes its data values to the aggregator, which in turn pulls data values from other sources to determine whether the client threshold condition is indeed violated. We present algorithms to minimize the number of source condition violations (i.e., the number of pushes) while ensuring that no violation of the client threshold condition is missed. Further, in case of a source condition violation, we propose efficient selective pulling algorithms for intelligently choosing additional sources whose data should be pulled by the aggregator. Using performance evaluation on synthetic and real traces of data updates we show that our algorithms result in up to an order of magnitude less number of messages compared to existing approaches in the literature.
Sharing data and work across concurrent analytical queries
Authors: Psaroudakis Iraklis , Athanassoulis Manos , Ailamaki Anastasia
Published: June 2014

Today's data deluge enables organizations to collect massive data, and analyze it with an ever-increasing number of concurrent queries. Traditional data warehouses (DW) face a challenging problem in executing this task, due to their query-centric model: each query is optimized and executed independently. This model results in high contention for resources. Thus, modern DW depart from the query-centric model to execution models involving sharing of common data and work. Our goal is to show when and how a DW should employ sharing. We evaluate experimentally two sharing methodologies, based on their original prototype systems, that exploit work sharing opportunities among concurrent queries at run-time: Simultaneous Pipelining (SP), which shares intermediate results of common sub-plans, and Global Query Plans (GQP), which build and evaluate a single query plan with shared operators.

First, after a short review of sharing methodologies, we show that SP and GQP are orthogonal techniques. SP can be applied to shared operators of a GQP, reducing response times by 20%-48% in workloads with numerous common sub-plans. Second, we corroborate previous results on the negative impact of SP on performance for cases of low concurrency. We attribute this behavior to a bottleneck caused by the push-based communication model of SP. We show that pull-based communication for SP eliminates the overhead of sharing altogether for low concurrency, and scales better on multi-core machines than push-based SP, further reducing response times by 82%-86% for high concurrency. Third, we perform an experimental analysis of SP, GQP and their combination, and show when each one is beneficial. We identify a trade-off between low and high concurrency. In the former case, traditional query-centric operators with SP perform better, while in the latter case, GQP with shared operators enhanced by SP give the best results.
Skyline operator on anti-correlated distributions
Authors: Shang Haichuan , Kitsuregawa Masaru
Published: June 2014

Finding the skyline in a multi-dimensional space is relevant to a wide range of applications. The skyline operator over a set of
d
-dimensional points selects the points that are not dominated by any other point on all dimensions. Therefore, it provides a minimal set of candidates for the users to make their personal trade-off among all optimal solutions.

The existing algorithms establish both the worst case complexity by discarding distributions and the average case complexity by assuming dimensional independence. However, the data in the real world is more likely to be anti-correlated. The cardinality and complexity analysis on dimensionally independent data is meaningless when dealing with anti-correlated data. Furthermore, the performance of the existing algorithms becomes impractical on anti-correlated data.

In this paper, we establish a cardinality model for anti-correlated distributions. We propose an accurate polynomial estimation for the expected value of the skyline cardinality. Because the high skyline cardinality downgrades the performance of most existing algorithms on anti-correlated data, we further develop a determination and elimination framework which extends the well-adopted elimination strategy. It achieves remarkable effectiveness and efficiency. The comprehensive experiments on both real datasets and benchmark synthetic datasets demonstrate that our approach significantly outperforms the state-of-the-art algorithms under a wide range of settings.
Distribution-based query scheduling
Authors: Chi Yun , Hacígümüş Hakan , Hsiung Wang-Pin , Naughton Jeffrey F.
Published: June 2014

Query scheduling, a fundamental problem in database management systems, has recently received a renewed attention, perhaps in part due to the rise of the "database as a service" (DaaS) model for database deployment. While there has been a great deal of work investigating different scheduling algorithms, there has been comparatively little work investigating what the scheduling algorithms can or should know about the queries to be scheduled. In this work, we investigate the efficacy of using histograms describing the distribution of likely query execution times as input to the query scheduler. We propose a novel distribution-based scheduling algorithm, Shepherd, and show that Shepherd substantially outperforms state-of-the-art point-based methods through extensive experimentation with both synthetic and TPC workloads.
Improving flash write performance by using update frequency
Authors: Stoica Radu , Ailamaki Anastasia
Published: June 2014

Solid-state drives (SSDs) are quickly becoming the default storage medium as the cost of NAND flash memory continues to drop. However, flash memory introduces new challenges, as data cannot be eciently updated in-place. To overcome the technology's limitations, SSDs incorporate a software Flash Translation Layer (FTL) that implements out-of-place updates, typically by storing data in a log-structured fashion. Despite a large number of existing FTL algorithms, SSD performance, predictability, and lifetime remain an issue, especially for the write-intensive workloads specific to database applications.

In this paper, we show how to design FTLs that are more efficient by using the I/O write skew to guide data placement on flash memory. We model the relationship between data placement and write performance for basic I/O write patterns and detail the most important concepts of writing to flash memory: i) the trade-o between the extra capacity available and write overhead, ii) the benefit of adapting data placement to write skew, iii) the impact of the cleaning policy, and iv) how to estimate the best achievable write performance for a given I/O workload. Based on the findings of the theoretical model, we propose a new principled data placement algorithm that can be incorporated into existing FTLs. We show the benefits of our data placement algorithm when running micro-benchmarks and real database I/O traces: our data placement algorithm reduces write overhead by 20% - 75% when compared to state-of-art techniques.
Discovering longest-lasting correlation in sequence databases
Authors: Li Yuhong , U Leong Hou , Yiu Man Lung , Gong Zhiguo
Published: June 2014

Most existing work on sequence databases use correlation (e.g., Euclidean distance and Pearson correlation) as a core function for various analytical tasks. Typically, it requires users to set a length for the similarity queries. However, there is no steady way to define the proper length on different application needs. In this work we focus on discovering longest-lasting highly correlated subsequences in sequence databases, which is particularly useful in helping those analyses without prior knowledge about the query length. Surprisingly, there has been limited work on this problem. A baseline solution is to calculate the correlations for every possible subsequence combination. Obviously, the brute force solution is not scalable for large datasets. In this work we study a space-constrained index that gives a tight correlation bound for subsequences of similar length and offset by intra-object grouping and inter-object grouping techniques. To the best of our knowledge, this is the first index to support normalized distance metric of arbitrary length subsequences. Extensive experimental evaluation on both real and synthetic sequence datasets verifies the efficiency and effectiveness of our proposed methods.
On the embeddability of random walk distances
Authors: Zhao Xiaohan , Chang Adelbert , Sarma Atish Das , Zheng Haitao , Zhao Ben Y.
Published: June 2014

Analysis of large graphs is critical to the ongoing growth of search engines and social networks. One class of queries centers around node affinity, often quantified by random-walk distances between node pairs, including hitting time, commute time, and personalized PageRank (PPR). Despite the potential of these "metrics," they are rarely, if ever, used in practice, largely due to extremely high computational costs.

In this paper, we investigate methods to scalably and efficiently compute random-walk distances, by "embedding" graphs and distances into points and distances in geometric coordinate spaces. We show that while existing graph coordinate systems (GCS) can accurately estimate shortest path distances, they produce significant errors when embedding random-walk distances. Based on our observations, we propose a new graph embedding system that explicitly accounts for per-node graph properties that affect random walk. Extensive experiments on a range of graphs show that our new approach can accurately estimate both symmetric and asymmetric random-walk distances. Once a graph is embedded, our system can answer queries between any two nodes in 8 microseconds, orders of magnitude faster than existing methods. Finally, we show that our system produces estimates that can replace ground truth in applications with minimal impact on application output.
Adaptive range filters for cold data
Authors: Alexiou Karolina , Kossmann Donald , Larson Per-Åke
Published: June 2014

Bloom filters are a great technique to test whether a key is not in a set of keys. This paper presents a novel data structure called ARF. In a nutshell, ARFs are for range queries what Bloom filters are for point queries. That is, an ARF can determine whether a set of keys does not contain any keys that are part of a specific range. This paper describes the principles and methods for efficient implementation of ARFs and presents the results of comprehensive experiments that assess the precision, space, and latency of ARFs. Furthermore, this paper shows how ARFs can be applied to a commercial database system that partitions data into hot and cold regions to optimize queries that involve only hot data.
Scalable progressive analytics on big data in the cloud
Authors: Chandramouli Badrish , Goldstein Jonathan , Quamar Abdul
Published: June 2014

Analytics over the increasing quantity of data stored in the Cloud has become very expensive, particularly due to the pay-as-you-go Cloud computation model. Data scientists typically manually extract samples of increasing data size (progressive samples) using domain-specific sampling strategies for exploratory querying. This provides them with user-control, repeatable semantics, and result provenance. However, such solutions result in tedious workflows that preclude the reuse of work across samples. On the other hand, existing approximate query processing systems report early results, but do not offer the above benefits for complex ad-hoc queries. We propose a new progressive analytics system based on a progress model called Prism that (1) allows users to communicate progressive samples to the system; (2) allows efficient and deterministic query processing over samples; and (3) provides repeatable semantics and provenance to data scientists. We show that one can realize this model for atemporal relational queries using an unmodified temporal streaming engine, by re-interpreting temporal event fields to denote progress. Based on Prism, we build Now!, a progressive data-parallel computation framework for Windows Azure, where progress is understood as a first-class citizen in the framework. Now! works with "progress-aware reducers"- in particular, it works with streaming engines to support progressive SQL over big data. Extensive experiments on Windows Azure with real and synthetic workloads validate the scalability and benefits of Now! and its optimizations, over current solutions for progressive analytics.
Understanding insights into the basic structure and essential issues of table placement methods in clusters
Authors: Huai Yin , Ma Siyuan , Lee Rubao , O'Malley Owen , Zhang Xiaodong
Published: June 2014

A table placement method is a critical component in big data analytics on distributed systems. It determines the way how data values in a two-dimensional table are organized and stored in the underlying cluster. Based on Hadoop computing environments, several table placement methods have been proposed and implemented. However, a comprehensive and systematic study to understand, to compare, and to evaluate different table placement methods has not been done. Thus, it is highly desirable to gain important insights into the basic structure and essential issues of table placement methods in the context of big data processing infrastructures.

In this paper, we present such a study. The basic structure of a data placement method consists of three core operations: row reordering, table partitioning, and data packing. All the existing placement methods are formed by these core operations with variations made by the three key factors: (1) the size of a horizontal logical subset of a table (or the size of a row group), (2) the function of mapping columns to column groups, and (3) the function of packing columns or column groups in a row group into physical blocks. We have designed and implemented a benchmarking tool to provide insights into how variations of each factor affect the I/O performance of reading data of a table stored by a table placement method. Based on our results, we give suggested actions to optimize table reading performance. Results from large-scale experiments have also confirmed that our findings are valid for production workloads. Finally, we present ORC File as a case study to show the effectiveness of our findings and suggested actions.
Supporting keyword search in product database
Authors: Duan Huizhong , Zhai ChengXiang , Cheng Jinxing , Kumar Rohit
Published: June 2014

The ability to let users search for products conveniently in product database is critical to the success of e-commerce. Although structured query languages (e.g. SQL) can be used to effectively access the product database, it is very difficult for end users to learn and use. In this paper, we study how to optimize search over structured product entities (represented by specifications) with keyword queries such as "cheap gaming laptop". One major difficulty in this problem is the vocabulary gap between the specifications of products in the database and the keywords people use in search queries. To solve the problem, we propose a novel probabilistic entity retrieval model based on query generation, where the entities would be ranked for a given keyword query based on the likelihood that a user who likes an entity would pose the query. Different ways to estimate the model parameters would lead to different variants of ranking functions. We start with simple estimates based on the specifications of entities, and then leverage user reviews and product search logs to improve the estimation. Multiple estimation algorithms are developed based on Maximum Likelihood and Maximum a Posteriori estimators. We evaluate the proposed product entity retrieval models on two newly created product search test collections. The results show that the proposed model significantly outperforms the existing retrieval models, benefiting from the modeling of attribute-level relevance. Despite the focus on product retrieval, the proposed modeling method is general and opens up many new opportunities in analyzing structured entity data with unstructured text data. We show the proposed probabilistic model can be easily adapted for many interesting applications including facet generation and review annotation.
A sampling algebra for aggregate estimation
Authors: Nirkhiwale Supriya , Dobra Alin , Jermaine Christopher
Published: June 2014

As of 2005, sampling has been incorporated in all major database systems. While efficient sampling techniques are realizable, determining the accuracy of an estimate obtained from the sample is still an unresolved problem. In this paper, we present a theoretical framework that allows an elegant treatment of the problem. We base our work on generalized uniform sampling (GUS), a class of sampling methods that subsumes a wide variety of sampling techniques. We introduce a key notion of equivalence that allows GUS sampling operators to commute with selection and join, and derivation of confidence intervals. We illustrate the theory through extensive examples and give indications on how to use it to provide meaningful estimates in database systems.
A temporal-probabilistic database model for information extraction
Authors: Dylla Maximilian , Miliaraki Iris , Theobald Martin
Published: June 2014

Temporal annotations of facts are a key component both for building a high-accuracy knowledge base and for answering queries over the resulting temporal knowledge base with high precision and recall. In this paper, we present a temporal-probabilistic database model for cleaning uncertain temporal facts obtained from information extraction methods. Specifically, we consider a combination of temporal deduction rules, temporal consistency constraints and probabilistic inference based on the common possible-worlds semantics with data lineage, and we study the theoretical properties of this data model. We further develop a query engine which is capable of scaling to very large temporal knowledge bases, with nearly interactive query response times over millions of uncertain facts and hundreds of thousands of grounded rules. Our experiments over two real-world datasets demonstrate the increased robustness of our approach compared to related techniques based on constraint solving via Integer Linear Programming (ILP) and probabilistic inference via Markov Logic Networks (MLNs). We are also able to show that our runtime performance is more than competitive to current ILP solvers and the fastest available, probabilistic but non-temporal, database engines.
Counter strike
Authors: Fender Pit , Moerkotte Guido
Published: June 2014

Finding the optimal execution order of join operations is a crucial task of today's cost-based query optimizers. There are two approaches to identify the best plan: bottom-up and top-down join enumeration. But only the top-down approach allows for branch-and-bound pruning, which can improve compile time by several orders of magnitude while still preserving optimality. For both optimization strategies, efficient enumeration algorithms have been published. However, there are two severe limitations for the top-down approach: The published algorithms can handle only (1) simple (binary) join predicates and (2) inner joins. Since real queries may contain complex join predicates involving more than two relations, and outer joins as well as other non-inner joins, efficient top-down join enumeration cannot be used in practice yet. We develop a novel top-down join enumeration algorithm that overcomes these two limitations. Furthermore, we show that our new algorithm is competitive when compared to the state of the art in bottom-up processing even without playing out its advantage by making use of its branch-and-bound pruning capabilities.
Efficient bulk updates on multiversion B-trees
Authors: Achakeev Daniar , Seeger Bernhard
Published: June 2014

Partial persistent index structures support efficient access to current and past versions of objects, while updates are allowed on the current version. The Multiversion B-Tree (MVBT) represents a partially persistent index-structure with both, asymptotic worst-case performance and excellent performance in real life applications. Updates are performed tuple-by-tuple with the same asymptotic performance as for standard B+trees. To the best of our knowledge, there is no efficient algorithm for bulk loading and bulk update of MVBT and other partially persistent index structures. In this paper, we propose the first loading algorithm for MVBT that meets the lower-bound of external sorting. In addition, our approach is also applicable to bulk updates. This is achieved by combining two basic technologies, weight balancing and buffer tree. Our extensive set of experiments confirm the theoretical findings: Our loading algorithm runs considerably faster than performing updates tuple-by-tuple.
Expressiveness and complexity of order dependencies
Authors: Szlichta Jaroslaw , Godfrey Parke , Gryz Jarek , Zuzarte Calisto
Published: June 2014

Dependencies play an important role in databases. We study order dependencies (ODs)--and unidirectional order dependencies (UODs), a proper sub-class of ODs--which describe the relationships among lexicographical orderings of sets of tuples. We consider lexicographical ordering, as by the order-by operator in SQL, because this is the notion of order used in SQL and within query optimization. Our main goal is to investigate the inference problem for ODs, both in theory and in practice. We show the usefulness of ODs in query optimization. We establish the following theoretical results: (i) a hierarchy of order dependency classes; (ii) a proof of co-NP-completeness of the inference problem for the subclass of UODs (and ODs); (iii) a proof of co-NP-completeness of the inference problem of functional dependencies (FDs) from ODs in general, but demonstrate linear time complexity for the inference of FDs from UODs; (iv) a sound and complete elimination procedure for inference over ODs; and (v) a sound and complete polynomial inference algorithm for sets of UODs over restricted domains.
Streaming similarity search over one billion tweets using parallel locality-sensitive hashing
Authors: Sundaram Narayanan , Turmukhametova Aizana , Satish Nadathur , Mostak Todd , Indyk Piotr , Madden Samuel , Dubey Pradeep
Published: June 2014

Finding nearest neighbors has become an important operation on databases, with applications to text search, multimedia indexing, and many other areas. One popular algorithm for similarity search, especially for high dimensional data (where spatial indexes like kd-trees do not perform well) is Locality Sensitive Hashing (LSH), an approximation algorithm for finding similar objects.

In this paper, we describe a new variant of LSH, called Parallel LSH (PLSH) designed to be extremely efficient, capable of scaling out on multiple nodes and multiple cores, and which supports high-throughput streaming of new data. Our approach employs several novel ideas, including: cache-conscious hash table layout, using a 2-level merge algorithm for hash table construction; an efficient algorithm for duplicate elimination during hash-table querying; an insert-optimized hash table structure and efficient data expiration algorithm for streaming data; and a performance model that accurately estimates performance of the algorithm and can be used to optimize parameter settings. We show that on a workload where we perform similarity search on a dataset of > 1 Billion tweets, with hundreds of millions of new tweets per day, we can achieve query times of 1-2.5 ms. We show that this is an order of magnitude faster than existing indexing schemes, such as inverted indexes. To the best of our knowledge, this is the fastest implementation of LSH, with table construction times up to 3.7× faster and query times that are 8.3× faster than a basic implementation.
Anti-caching
Authors: DeBrabant Justin , Pavlo Andrew , Tu Stephen , Stonebraker Michael , Zdonik Stan
Published: June 2014

The traditional wisdom for building disk-based relational database management systems (DBMS) is to organize data in heavily-encoded blocks stored on disk, with a main memory block cache. In order to improve performance given high disk latency, these systems use a multi-threaded architecture with dynamic record-level locking that allows multiple transactions to access the database at the same time. Previous research has shown that this results in substantial overhead for on-line transaction processing (OLTP) applications [15].

The next generation DBMSs seek to overcome these limitations with architecture based on main memory resident data. To overcome the restriction that all data fit in main memory, we propose a new technique, called anti-caching, where cold data is moved to disk in a transactionally-safe manner as the database grows in size. Because data initially resides in memory, an anti-caching architecture reverses the traditional storage hierarchy of disk-based systems. Main memory is now the primary storage device.

We implemented a prototype of our anti-caching proposal in a high-performance, main memory OLTP DBMS and performed a series of experiments across a range of database sizes, workload skews, and read/write mixes. We compared its performance with an open-source, disk-based DBMS optionally fronted by a distributed main memory cache. Our results show that for higher skewed workloads the anti-caching architecture has a performance advantage over either of the other architectures tested of up to 9× for a data size 8× larger than memory.
Understanding hierarchical methods for differentially private histograms
Authors: Qardaji Wahbeh , Yang Weining , Li Ninghui
Published: June 2014

In recent years, many approaches to differentially privately publish histograms have been proposed. Several approaches rely on constructing tree structures in order to decrease the error when answer large range queries. In this paper, we examine the factors affecting the accuracy of hierarchical approaches by studying the mean squared error (MSE) when answering range queries. We start with one-dimensional histograms, and analyze how the MSE changes with different branching factors, after employing constrained inference, and with different methods to allocate the privacy budget among hierarchy levels. Our analysis and experimental results show that combining the choice of a good branching factor with constrained inference outperform the current state of the art. Finally, we extend our analysis to multi-dimensional histograms. We show that the benefits from employing hierarchical methods beyond a single dimension are significantly diminished, and when there are 3 or more dimensions, it is almost always better to use the Flat method instead of a hierarchy.
Simple, fast, and scalable reachability oracle
Authors: Jin Ruoming , Wang Guan
Published: June 2014

A reachability oracle (or hop labeling) assigns each vertex
v
two sets of vertices:

L
out
(v)

and

L
in
(v)

, such that
u
reaches
v
iff

L
out
(u) ∩ L
in
(v)

≠ 0. Despite their simplicity and elegance, reachability oracles have failed to achieve efficiency in more than ten years since their introduction: The main problem is high construction cost, which stems from a set-cover framework and the need to materialize transitive closure. In this paper, we present two simple and efficient labeling algorithms, Hierarchical-Labeling and Distribution-Labeling, which can work onmassive real-world graphs: Their construction time is an order of magnitude faster than the set-cover based labeling approach, and transitive closure materialization is not needed. On large graphs, their index sizes and their query performance can now beat the state-of-the-art transitive closure compression and online search approaches.
Aggregation and ordering in factorised databases
Authors: Bakibayev Nurzhan , Kočiský Tomáš , Olteanu Dan , Závodný Jakub
Published: June 2014

A common approach to data analysis involves understanding and manipulating succinct representations of data. In earlier work, we put forward a succinct representation system for relational data called factorised databases and reported on the main-memory query engine FDB for select-project-join queries on such databases.

In this paper, we extend FDB to support a larger class of practical queries with aggregates and ordering. This requires novel optimisation and evaluation techniques. We show how factorisation coupled with partial aggregation can effectively reduce the number of operations needed for query evaluation. We also show how factorisations of query results can support enumeration of tuples in desired orders as efficiently as listing them from the unfactorised, sorted results.

We experimentally observe that FDB can outperform off-the-shelf relational engines by orders of magnitude.
Fast iterative graph computation with block updates
Authors: Xie Wenlei , Wang Guozhang , Bindel David , Demers Alan , Gehrke Johannes
Published: June 2014

Scaling iterative graph processing applications to large graphs is an important problem. Performance is critical, as data scientists need to execute graph programs many times with varying parameters. The need for a high-level, high-performance programming model has inspired much research on graph programming frameworks.

In this paper, we show that the important class of computationally light graph applications - applications that perform little computation per vertex - has severe scalability problems across multiple cores as these applications hit an early "memory wall" that limits their speedup. We propose a novel block-oriented computation model, in which computation is iterated locally over blocks of highly connected nodes, significantly improving the amount of computation per cache miss. Following this model, we describe the design and implementation of a block-aware graph processing runtime that keeps the familiar vertex-centric programming paradigm while reaping the benefits of block-oriented execution. Our experiments show that block-oriented execution significantly improves the performance of our framework for several graph applications.
Regulation of meiotic entry and gonadal sex differentiation in the human: normal and disrupted signaling
Authors: Jørgensen Anne , Rajpert-De Meyts Ewa
Published: June 2014

Meiosis is a unique type of cell division that is performed only by germ cells to form haploid gametes. The switch from mitosis to meiosis exhibits a distinct sex-specific difference in timing, with female germ cells entering meiosis during fetal development and male germ cells at puberty when spermatogenesis is initiated. During early fetal development, bipotential primordial germ cells migrate to the forming gonad where they remain sexually indifferent until the sex-specific differentiation of germ cells is initiated by cues from the somatic cells. This irreversible step in gonadal sex differentiation involves the initiation of meiosis in fetal ovaries and prevention of meiosis in the germ cells of fetal testes. During the last decade, major advances in the understanding of meiosis regulation have been accomplished, with the discovery of retinoic acid as an inducer of meiosis being the most prominent finding. Knowledge about the molecular mechanisms regulating meiosis signaling has mainly been established by studies in rodents, while this has not yet been extensively investigated in humans. In this review, the current knowledge about the regulation of meiosis signaling is summarized and placed in the context of fetal gonad development and germ cell differentiation, with emphasis on results obtained in humans. Furthermore, the consequences of dysregulated meiosis signaling in humans are briefly discussed in the context of selected pathologies, including testicular germ cell cancer and some forms of male infertility.
Decomposable Leavitt path algebras for arbitrary graphs
Authors: Aranda Pino Gonzalo , Nasr-Isfahani Alireza
Published: June 2014

For any field K and for a completely arbitrary graph E, we characterize the Leavitt path algebras L

K
(E) that are indecomposable (as a direct sum of two-sided ideals) in terms of the underlying graph. When the algebra decomposes, it actually does so as a direct sum of Leavitt path algebras for some suitable graphs. Under certain finiteness conditions, a unique indecomposable decomposition exists.
Response of Sunflower Cultivars to Deficit Irrigation
Authors: Safahani Langeroodi Ali Reza , Kamkar Behnam , Teixeira da Silva Jaime A. , Ataei Mehrdad
Published: June 2014

A 2-year study was conducted to determine the response of four sunflower cultivars (Master, Lakumka, Favorite and Berezanski) to different irrigation regimes: 45 (control), 60, 75 and 90% (I1–I4) of maximum allowable depletion (MAD) of available soil water (ASW). Severe deficit irrigation (I3 and I4) significantly decreased water-use efficiency, radiation use efficiency, yield and yield-related components. The results also showed that water deficit, compared to full-irrigated treatment (I1), increased the activity of all antioxidant enzymes in leaves. Master had the highest seed yield in full and limited irrigation as well as the highest antioxidant enzymes content. Sunflower plants extracted most of the soil moisture from 0 to 50 cm soil layer. Therefore, it is recommended that only the 0–50 cm soil profile be considered for scheduling irrigation. Measured and simulated results revealed a special irrigation regime threshold for each cultivar with respect to seed yield. The response rate to irrigation regime ceased in higher irrigation rates as I
join (65, 63, 65 and 70% MAD for Master, Lakumka, Favorite and Berezanski, respectively). Moreover, all estimated I
join for all cultivars were higher than the current recommendation of 50% MAD. This shows the ability to sustain the yield of these sunflower cultivars in the study region under deficit irrigation. The seed yield of all four cultivars tended to respond similarly to irrigation in both years, but Master was more efficient than the remaining three cultivars with respect to the response to diverse treatments.
Comparison of Heat Treatments on the Toughness of 1.7Ni-1.5Cu-0.5Mo Pre-alloyed P/M Steels
Authors: Güral Ahmet , Türkan Mustafa
Published: June 2014

Effects of quenching plus tempering and intercritical annealing plus quenching heat treatments on the impact toughness properties of 1.7Ni-1.5Cu-0.5Mo pre-alloyed powder metallurgy steels with 0.2 (in mass%) graphite were investigated. Specimens were prepared by pressing at 670 MPa and sintering at 1250 °C. Different heat treatments, namely quenching and tempering, directly intercritically annealing and fully austenization plus intercritically annealing were carried out on the sintered specimens. The results showed that the impact toughness decreased with increasing intercritical annealing temperature in ferrite plus martensite dual phase powder metallurgy steels and with increasing tempering temperature in the quenched plus tempered specimens. Besides, impact toughness of fully austenizated plus intercritically annealed specimens was higher than those of quenched plus tempered and directly intercritically annealed specimens at the same hardness levels.
Crystal structure of N-cyclopentylidene-N'-thiazolo[4,5-b]pyridin-2-ylhydrazine, C11H12N4S
Authors: Zhao Yan , Zhu Mei
Published: June 2014

C
Crystal structure of (E)-methyl-N'-(2,5-dimethoxybenzylidene)- hydrazinecarboxylate, C11H14N2O4
Authors: Lv Lu-Ping , Tong Guo-Tong , Xia Cheng-Cai , Liu Yan-Liang
Published: June 2014

C
Deadenylation—a piece of PANcake
Authors: Stubbs Milton T , Wahle Elmar
Published: June 2014

Pan is a poly(A)‐specific 3′ exoribonuclease that, together with the CCR4‐NOT complex, is responsible for initiating and controlling mRNA decay by degradation of the poly(A) tail. Now, more than twenty years after the enzyme's discovery, a surge of recent papers, including one in this issue of The EMBO Journal (Wolf et al, ) has revealed details of its unusual asymmetric structure and aspects of its mode of substrate binding.
Acute Concussion Symptom Severity and Delayed Symptom Resolution
Authors: Grubenhoff Joseph A. , Deakyne Sara J. , Brou Lina , Bajaj Lalit , Comstock R. Dawn , Kirkwood Michael W.
Published: June 2014

BACKGROUND AND OBJECTIVES:

Up to 30% of children who have concussion initially evaluated in the emergency department (ED) display delayed symptom resolution (DSR). Greater initial symptom severity may be an easily quantifiable predictor of DSR. We hypothesized that greater symptom severity immediately after injury increases the risk for DSR.

METHODS:

We conducted a prospective longitudinal cohort study of children 8 to 18 years old presenting to the ED with concussion. Acute symptom severity was assessed using a graded symptom inventory. Presence of DSR was assessed 1 month later. Graded symptom inventory scores were tested for association with DSR by sensitivity analysis. We conducted a similar analysis for post-concussion syndrome (PCS) as defined by the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Potential symptoms characteristic of DSR were explored by using hierarchical cluster analysis.

RESULTS:

We enrolled 234 subjects; 179 (76%) completed follow-up. Thirty-eight subjects (21%) experienced DSR. Initial symptom severity was not significantly associated with DSR 1 month after concussion. A total of 22 subjects (12%) had PCS. Scores >10 (possible range, 0–28) were associated with an increased risk for PCS (RR, 3.1; 95% confidence interval 1.2–8.0). Three of 6 of the most characteristic symptoms of DSR were also most characteristic of early symptom resolution. However, cognitive symptoms were more characteristic of subjects reporting DSR.

CONCLUSIONS:

Greater symptom severity measured at ED presentation does not predict DSR but is associated with PCS. Risk stratification therefore depends on how the persistent symptoms are defined. Cognitive symptoms may warrant particular attention in future study. Follow-up is recommended for all patients after ED evaluation of concussion to monitor for DSR.
Early-Onset Basal Cell Carcinoma and Indoor Tanning: A Population-Based Study
Authors: Karagas Margaret R. , Zens M. Scot , Li Zhigang , Stukel Therese A. , Perry Ann E. , Gilbert-Diamond Diane , Sayarath Vicki , Stephenson Rita S. , Barton Dorothea , Nelson Heather H. , Spencer Steven K.
Published: June 2014

OBJECTIVE:

Indoor tanning with UV radiation–emitting lamps is common among adolescents and young adults. Rising incidence rates of basal cell carcinoma (BCC) have been reported for the United States and elsewhere, particularly among those diagnosed at younger ages. Recent epidemiologic studies have raised concerns that indoor tanning may be contributing to early occurrence of BCC, and younger people may be especially vulnerable to cancer risk associated with this exposure. Therefore, we sought to address these issues in a population-based case–control study from New Hampshire.

METHODS:

Data on indoor tanning were obtained on 657 cases of BCC and 452 controls ≤50 years of age.

RESULTS:

Early-onset BCC was related to indoor tanning, with an adjusted odds ratio (OR) of 1.6 (95% confidence interval, 1.3–2.1). The strongest association was observed for first exposure as an adolescent or young adult, with a 10% increase in the OR with each age younger at first exposure (OR per year of age ≤23 = 1.1; 95% confidence interval, 1.0–1.2). Associations were present for each type of device examined (ie, sunlamps, tanning beds, and tanning booths).

CONCLUSIONS:

Our findings suggest early exposure to indoor tanning increases the risk of early development of BCC. They also underscore the importance of counseling adolescents and young adults about the risks of indoor tanning and for discouraging parents from consenting minors to this practice.
Efficacy and Safety of Saccharomyces boulardii for Acute Diarrhea
Authors: Feizizadeh Sahar , Salehi-Abargouei Amin , Akbari Vajihe
Published: June 2014

BACKGROUND AND OBJECTIVE:

The efficacy of Saccharomyces boulardii for treatment of childhood diarrhea remains unclear. Our objective was to systematically review data on the effect of S. boulardii on acute childhood diarrhea.

METHODS:

Our data sources included Medline, Embase, CINAHL, Scopus, and The Cochrane Library up to September 2013 without language restrictions. Randomized controlled trials and non-randomized trials that evaluated effectiveness of S. boulardii for treatment of acute diarrhea in children were included. Two reviewers independently evaluated studies for eligibility and quality and extracted the data.

RESULTS:

In total, 1248 articles were identified, of which 22 met the inclusion criteria. Pooling data from trials showed that S. boulardii significantly reduced the duration of diarrhea (mean difference [MD], −19.7 hours; 95% confidence interval [CI], −26.05 to −13.34), stool frequency on day 2 (MD, −0.74; 95% CI, −1.38 to −0.10) and day 3 (MD, −1.24; 95% CI, −2.13 to −0.35), the risk for diarrhea on day 3 (risk ratio [RR], 0.41; 95% CI, 0.27 to 0.60) and day 4 (RR, 0.38; 95% CI, 0.24 to 0.59) after intervention compared with control. The studies included in this review were varied in the definition of diarrhea, the termination of diarrhea, inclusion and exclusion criteria, and their methodological quality.

CONCLUSIONS:

This review and meta-analysis show that S. boulardii is safe and has clear beneficial effects in children who have acute diarrhea. However, additional studies using head-to-head comparisons are needed to define the best dosage of S. boulardii for diarrhea with different causes.
Death of a Child in the Emergency Department
Authors:   AMERICAN ACADEMY OF PEDIATRICS Committee on Pediatric Emergency Medicine,   AMERICAN COLLEGE OF EMERGENCY PHYSICIANS Pediatric Emergency Medicine Committee,   EMERGENCY NURSES ASSOCIATION Pediatric Committee, O’Malley Patricia J. , Barata Isabel A. , Snow Sally K. , Shook Joan E. , Ackerman Alice D. , Chun Thomas H. , Conners Gregory P. , Dudley Nanette C. , Fuchs Susan M. , Gorelick Marc H. , Lane Natalie E. , Moore Brian R. , Wright Joseph L. , Benjamin Lee S. , Alade Kiyetta , Arms Joseph , Avarello Jahn T. , Baldwin Steven , Brown Kathleen , Cantor Richard M. , Cohen Ariel , Dietrich Ann Marie , Eakin Paul J. , Gausche-Hill Marianne , Gerardi Michael , Graham Charles J. , Holtzman Doug K. , Hom Jeffrey , Ishimine Paul , Jinivizian Hasmig , Joseph Madeline , Mehta Sanjay , Ojo Aderonke , Paul Audrey Z. , Pauze Denis R. , Pearson Nadia M. , Rosen Brett , Russell W. Scott , Saidinejad Mohsen , Sloas Harold A. , Schwartz Gerald R. , Swenson Orel , Valente Jonathan H. , Waseem Muhammad , Whiteman Paula J. , Woolridge Dale , Vicioso Michael , Nagle Jason T. , Renaker Anne M. , Tomoyasu Flora , Cadwell Sue , Herrin Shari , Brecher Deena
Published: June 2014

This Policy Statement was reaffirmed September 2019 and November 2024.

The American Academy of Pediatrics, American College of Emergency Physicians, and Emergency Nurses Association have collaborated to identify practices and principles to guide the care of children, families, and staff in the challenging and uncommon event of the death of a child in the emergency department in this policy statement and in an accompanying technical report.
Magnetostrictive Materials
Authors: Hathaway Kristl B. , Clark Arthur E.
Published: June 2014

Smart materials combine sensors, intelligence, and actuators to allow a material to respond to its environment. Magnetostrictive materials can be used as both the sensors and actuators in such materials. High-power magnetostrictive actuators can deliver forces greater than 50 MPa with strains of up to 0.6%, while other magnetostrictive sensor materials can provide hundreds of times the sensitivity of semiconductor strain gages. Magnetoelastic materials also have adaptable elastic moduli which may be varied by external magnetic fields.

Magnetostriction is the change in any dimension of a magnetic material caused by a change in its magnetic state. In this article we concentrate on ferromagnetic materials exhibiting Joule magnetostriction, which is a change in linear dimension parallel to an applied magnetic field (see Figure 1), and the reciprocal effect in which the material changes its magnetic state under the influence of applied stress.

The phenomenon of magnetostriction has been known for well over a century, since Joule discovered in 1847 the change in length of an iron rod when magnetized. The modern era of magnetostrictive materials began in 1963 with the measurement of nearly 1% magnetostrictive strains at low temperatures in the basal planes of Dy and Tb. A search for magnetostrictive materials with high magnetostriction at room temperature led to the alloying of rare earths with transition metals, culminating in the discovery in 1971 of giant room-temperature magnetostriction in the Laves phase compound TbFe2.