Metarhizium anisopliae infection alters feeding and trophallactic behavior in the ant Solenopsis invicta

In social insects, social behavior may be changed in a way that preventing the spread of pathogens. We infected workers of the ant Solenopsis invicta with an entomopathogenic fungus Metarhizium anisopliae and then videotaped and/or measured worker feeding and trophallactic behavior. Results showed that fungal infected S. invicta enhanced their preference for bitter alkaloid chemical quinine on 3 days after inoculation, which might be self-medication of S. invicta by ingesting more alkaloid substances in response to pathogenic infection. Furthermore, infected ants devoted more time to trophallactic behavior with their nestmates on 3 days post inoculation, in return receiving more food. Increased interactions between exposed ants and their naive nestmates suggest the existence of social immunity in S. invicta. Overall, our study indicates that S. invicta may use behavioral defenses such as self-medication and social immunity in response to a M. anisopliae infection. (C) 2016 Published by Elsevier Inc

(Z)-4-[(2-Amino­anilino)(phen­yl)methyl­idene]-3-methyl-1-phenyl-1H-pyrazol-5(4H)-one

The mol­ecule of the title compound, C23H20N4O, assumes a non-planar conformation in which the pyrazolone ring forms dihedral angles of 10.33 (11), 65.34 (11) and 63.52 (10)° with the three benzene rings. In the crystal, the mol­ecules are linked by inter­molecular N—H⋯N hydrogen bonds, generating chains parallel to the b axis. The secondary amino group is involved in an intra­molecular N—H⋯O hydrogen bond

ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads (Extended)

For efficient query processing, DBMS query optimizers have for decades relied on delicate cardinality estimation methods. In this work, we propose an Attention-based LEarned Cardinality Estimator (ALECE for short) for SPJ queries. The core idea is to discover the implicit relationships between queries and underlying dynamic data using attention mechanisms in ALECE's two modules that are built on top of carefully designed featurizations for data and queries. In particular, from all attributes in the database, the data-encoder module obtains organic and learnable aggregations which implicitly represent correlations among the attributes, whereas the query-analyzer module builds a bridge between the query featurizations and the data aggregations to predict the query's cardinality. We experimentally evaluate ALECE on multiple dynamic workloads. The results show that ALECE enables PostgreSQL's optimizer to achieve nearly optimal performance, clearly outperforming its built-in cardinality estimator and other alternatives.Comment: VLDB 202

DILI: A Distribution-Driven Learned Index

Targeting in-memory one-dimensional search keys, we propose a novel DIstribution-driven Learned Index tree (DILI), where a concise and computation-efficient linear regression model is used for each node. An internal node's key range is equally divided by its child nodes such that a key search enjoys perfect model prediction accuracy to find the relevant leaf node. A leaf node uses machine learning models to generate searchable data layout and thus accurately predicts the data record position for a key. To construct DILI, we first build a bottom-up tree with linear regression models according to global and local key distributions. Using the bottom-up tree, we build DILI in a top-down manner, individualizing the fanouts for internal nodes according to local distributions. DILI strikes a good balance between the number of leaf nodes and the height of the tree, two critical factors of key search time. Moreover, we design flexible algorithms for DILI to efficiently insert and delete keys and automatically adjust the tree structure when necessary. Extensive experimental results show that DILI outperforms the state-of-the-art alternatives on different kinds of workloads.Comment: PVLDB Volume 1

Time-varying Reliability Analysis of Long-span Continuous Rigid Frame bridge under Cantilever Construction Stage based on the Monitored Strain Data

Abstract: In general, the material properties, loads, resistance of the prestressed concrete continuous rigid frame bridge in different construction stages are time-varying. So, it is essential to monitor the internal force state when the bridge is in construction. Among them, how to assess the safety is one of the challenges. As the continuous monitoring over a long-term period can increase the reliability of the assessment, so, based on a large number of monitored strain data collected from the structural health monitoring system (SHMS) during construction, a calculation method of the punctiform time-varying reliability is proposed in this paper to evaluate the stress state of this type bridge in cantilever construction stage by using the basic reliability theory. At the same time, the optimal stress distribution function in the bridge mid-span base plate is determined when the bridge is closed. This method can provide basis and direction for the internal force control of this type bridge in construction process. So, it can reduce the bridge safety and quality accidents in construction stages

Poly[[aqua­(μ2-oxalato)(μ2-2-oxido­pyridinium-3-carboxylato)holmium(III)] monohydrate]

In the title complex, {[Ho(C2O4)(C6H4NO3)(H2O)]·(H2O)}n, the HoIII ion is coordinated by three O atoms from two 2-oxidopyridinium-3-carboxylate ligands, four O atoms from two oxalate ligands and one water mol­ecule in a distorted bicapped trigonal-prismatic geometry. The 2-oxidopyridin­ium-3-carboxylate and oxalate ligands link the HoIII ions into a layer in (100). These layers are further connected by inter­molecular O—H⋯O hydrogen bonds involving the coordinated water mol­ecules to assemble a three-dimensional supra­molecular network. The uncoordin­ated water mol­ecule is involved in N—H⋯O and O—H⋯O hydrogen bonds within the layer