Lipid storage changes in human skeletal muscle during detraining

Exercise training is known to increase intramuscular triglyceride content in both trained and untrained legs. The purpose of the study was to determine the changes of intramyocellular lipids (IMCL) and extramyocellular lipids (EMCL) of both trained and untrained legs during detraining. We measured both IMCL and EMCL levels in previously trained vs. untrained legs during 4-weeks of detraining after 6-weeks of strength training. Eight young men (aged 21.4 + / - 1.4 years) trained their vastus lateralis muscle in one leg using a dynamometer, whereas the contralateral leg served as untrained control. Muscle cross-sectional area (CSA), IMCL, EMCL, total creatine (creatine + phophocreatine) of extensor (vastus lateralis) muscles were assessed using magnetic resonance imaging (MRI) and proton magnetic resonance spectra (H-1-MRS) before training, 3 days after and 28 days after the last bout of training. CSA was increased in both legs by Day 3 after training, and was still high at Day 28 post-training; IMCL increased in both legs by Day 3 after training, then decreased at Day 28 post-training only in the untrained leg; EMCL shows no significant change by Day 3 after training, but at Day 28 post-training has increased in the trained leg and decreased in the untrained leg; total creatine did not change significantly. Conclusion: Decreases of IMCL and EMCL storages in previously untrained leg during detraining indicates an ectopic influence on tissue lipid storage by different metabolic demand among tissues in the same human body

Bis(2-amino­pyridine-κN 1)bis­(benzoato-κO)cobalt(II)

In the title compound, [Co(C7H5O2)2(C5H6N2)2], the CoII atom is hexa­coordinated by four O atoms from two benzoate anions, and two N atoms from two 2-amino­pyridine mol­ecules, resulting in a distorted octa­hedral geometry. Both benzoate anions act as bidentate ligands and both 2-amino­pyridine mol­ecules are coordinated to the metal through their pyridyl N atoms. The crystal packing is stabilized by inter­molecular N—H⋯O hydrogen bonds, C—H⋯π, and π–π stacking inter­actions involving benzoate anions and 2-amino­pyridine mol­ecules

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

3-(4-Chloro­phen­yl)-2-(diisopropyl­amino)-1-benzofuro[3,2-d]pyrimidin-4(3H)-one

In the mol­ecule of the title compound, C22H22ClN3O2, the three fused rings of the benzofuro[3,2-d]pyrimidine system are almost coplanar. This ring system is oriented with respect to the substituted benzene ring at a dihedral angle of 79.05 (3)°. Intra­molecular C—H⋯N hydrogen bonding results in the formation of a six-membered ring. In the crystal structure, π–π stacking inter­actions involving the furan, pyrimidinone and benzene rings are present [centroid-to-centroid distances in the range 3.258 (1)–3.870 (1) Å]

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