A numerical simulation of cool/wet and warm/wet episodes in the western United States

Wintertime precipitation in the mountains of the western United States during a warm or cool period has a pronounced influence on streamflow. During a warm year, streamflow at intermediate elevations responds more immediately to precipitation events; during a cold year, much of the discharge is delayed until the snow melts in spring and summer. Previous efforts at studying these extremes have been hampered by a limited number and length of observational analyses. In this study, we augment this limited observational record by analyzing a simplified general circulation model.

Mechanism study on the frequent variations of cell-bound microcystins in cyanobacterial blooms in Lake Taihu: Implications for water quality monitoring and assessments

Although Microcystis-based toxins have been intensively studied, previous studies using laboratory cultures of Microcystis strains are difficult to explain the phenomenon that microcystin concentrations and toxin variants in natural blooms differ widely and frequently within a short-term period. The present study was designed to unravel the mechanisms for the frequent variations of intracellular toxins related to the differences in cyanobacterial colonies during bloom seasons in Lake Taihu, China. Monitoring of Microcystis colonies during warm seasons indicated that the variations in microcystins in both concentrations and toxin species were associated with the frequent alteration of Microcystis colonies in Lake Taihu. High concentration of microcystins in the blooms was always associated with two Microcystis colonies, Microcystis flos-aquae and Microcystis aeruginosa, whereas when Microcystis wesenbergii was the dominant colonial type, the toxin production of the blooms was low. Additionally, environmental factors such as temperature and nutrition were also shown to have an effect on the toxin production of the blooms, and may also potentially influence the Microcystis species present. The results of the present study provides insight into a new consideration for quick water quality monitoring, assessment and risk alert in cyanobacterium- and toxin-contaminated freshwaters, which will be beneficial not only for water agencies but also for public health. (C) 2009 Elsevier Ltd. All rights reserved.

Temperature dependence of surface quantum dots grown under frequent growth interruption

We grow In-GaAs quantum dot (QD) at low growth rate with 70 times insertion of growth interruption in MBE system. It is found that because of the extreme growth condition, QDs exhibit a thick wetting layer, large QD height value and special surface morphology which is attributed to the In segregation effect. Temperature dependence of photoluminescence measurement shows that this kind of QDs has a good thermal stability which is explained in terms of a "group coupling" model put forward by us. (C) 2007 Elsevier B.V. All rights reserved.

Temperature dependence of surface quantum dots grown under frequent growth interruption

We grow InGaAs quantum dot (QD) at low growth rate with 70 times insertion of growth interruption in MBE system. It is found that because of the extreme growth condition, QDs exhibit a thick wetting layer, large QD height value and special surface morphology which is attributed to the enhanced adatom surface diffusion and In-segregation effect. Temperature dependence of photoluminescence measurement from surface QD shows that this kind of QD has good thermal stability which is explained in terms of the presence of surface oxide. The special distribution of QD may also play a role in this thermal character. (c) 2006 Elsevier B.V. All rights reserved.

Salinity episodes and their reversal in the late pliocene of south-western Australia

IEECAS SKLLQG

maintaining only frequent itemsets to mine approximate frequent itemsets over online data streams

IEEE

McArdle disease: a unique study model in sports medicine

McArdle disease is arguably the paradigm of exercise intolerance in humans. This disorder is caused by inherited deficiency of myophosphorylase, the enzyme isoform that initiates glycogen breakdown in skeletal muscles. Because patients are unable to obtain energy from their muscle glycogen stores, this disease provides an interesting model of study for exercise physiologists, allowing insight to be gained into the understanding of glycogen-dependent muscle functions. Of special interest in the field of muscle physiology and sports medicine are also some specific (if not unique) characteristics of this disorder, such as the so-called 'second wind' phenomenon, the frequent exercise-induced rhabdomyolysis and myoglobinuria episodes suffered by patients (with muscle damage also occurring under basal conditions), or the early appearance of fatigue and contractures, among others. In this article we review the main pathophysiological features of this disorder leading to exercise intolerance as well as the currently available therapeutic possibilities.

Generalized Methods for Discovering Frequent Poly-Regions in DNA

The problem of discovering frequent poly-regions (i.e. regions of high occurrence of a set of items or patterns of a given alphabet) in a sequence is studied, and three efficient approaches are proposed to solve it. The first one is entropy-based and applies a recursive segmentation technique that produces a set of candidate segments which may potentially lead to a poly-region. The key idea of the second approach is the use of a set of sliding windows over the sequence. Each sliding window covers a sequence segment and keeps a set of statistics that mainly include the number of occurrences of each item or pattern in that segment. Combining these statistics efficiently yields the complete set of poly-regions in the given sequence. The third approach applies a technique based on the majority vote, achieving linear running time with a minimal number of false negatives. After identifying the poly-regions, the sequence is converted to a sequence of labeled intervals (each one corresponding to a poly-region). An efficient algorithm for mining frequent arrangements of intervals is applied to the converted sequence to discover frequently occurring arrangements of poly-regions in different parts of DNA, including coding regions. The proposed algorithms are tested on various DNA sequences producing results of significant biological meaning.

Discovering Frequent Poly-Regions in DNA Sequences

The problem of discovering frequent arrangements of regions of high occurrence of one or more items of a given alphabet in a sequence is studied, and two efficient approaches are proposed to solve it. The first approach is entropy-based and uses an existing recursive segmentation technique to split the input sequence into a set of homogeneous segments. The key idea of the second approach is to use a set of sliding windows over the sequence. Each sliding window keeps a set of statistics of a sequence segment that mainly includes the number of occurrences of each item in that segment. Combining these statistics efficiently yields the complete set of regions of high occurrence of the items of the given alphabet. After identifying these regions, the sequence is converted to a sequence of labeled intervals (each one corresponding to a region). An efficient algorithm for mining frequent arrangements of temporal intervals on a single sequence is applied on the converted sequence to discover frequently occurring arrangements of these regions. The proposed algorithms are tested on various DNA sequences producing results with significant biological meaning.

Constraint-based Mining of Frequent Arrangements of Temporal Intervals

The problem of discovering frequent arrangements of temporal intervals is studied. It is assumed that the database consists of sequences of events, where an event occurs during a time-interval. The goal is to mine temporal arrangements of event intervals that appear frequently in the database. The motivation of this work is the observation that in practice most events are not instantaneous but occur over a period of time and different events may occur concurrently. Thus, there are many practical applications that require mining such temporal correlations between intervals including the linguistic analysis of annotated data from American Sign Language as well as network and biological data. Two efficient methods to find frequent arrangements of temporal intervals are described; the first one is tree-based and uses depth first search to mine the set of frequent arrangements, whereas the second one is prefix-based. The above methods apply efficient pruning techniques that include a set of constraints consisting of regular expressions and gap constraints that add user-controlled focus into the mining process. Moreover, based on the extracted patterns a standard method for mining association rules is employed that applies different interestingness measures to evaluate the significance of the discovered patterns and rules. The performance of the proposed algorithms is evaluated and compared with other approaches on real (American Sign Language annotations and network data) and large synthetic datasets.