Using Multi-Sense Vector Embeddings for Reverse Dictionaries

Popular word embedding methods such as word2vec and GloVe assign a single vector representation to each word, even if a word has multiple distinct meanings. Multi-sense embeddings instead provide different vectors for each sense of a word. However, they typically cannot serve as a drop-in replacement for conventional single-sense embeddings, because the correct sense vector needs to be selected for each word. In this work, we study the effect of multi-sense embeddings on the task of reverse dictionaries. We propose a technique to easily integrate them into an existing neural network architecture using an attention mechanism. Our experiments demonstrate that large improvements can be obtained when employing multi-sense embeddings both in the input sequence as well as for the target representation. An analysis of the sense distributions and of the learned attention is provided as well

Label-Descriptive Patterns and their Application to Characterizing Classification Errors

State-of-the-art deep learning methods achieve human-like performance on many tasks, but make errors nevertheless. Characterizing these errors in easily interpretable terms gives insight into whether a model is prone to making systematic errors, but also gives a way to act and improve the model. In this paper we propose a method that allows us to do so for arbitrary classifiers by mining a small set of patterns that together succinctly describe the input data that is partitioned according to correctness of prediction. We show this is an instance of the more general label description problem, which we formulate in terms of the Minimum Description Length principle. To discover good pattern sets we propose the efficient and hyperparameter-free Premise algorithm, which through an extensive set of experiments we show on both synthetic and real-world data performs very well in practice; unlike existing solutions it ably recovers ground truth patterns, even on highly imbalanced data over many unique items, or where patterns are only weakly associated to labels. Through two real-world case studies we confirm that Premise gives clear and actionable insight into the systematic errors made by modern NLP classifiers

Automated claustrum segmentation in human brain MRI using deep learning

In the last two decades, neuroscience has produced intriguing evidence for a central role of the claustrum in mammalian forebrain structure and function. However, relatively few in vivo studies of the claustrum exist in humans. A reason for this may be the delicate and sheet-like structure of the claustrum lying between the insular cortex and the putamen, which makes it not amenable to conventional segmentation methods. Recently, Deep Learning (DL) based approaches have been successfully introduced for automated segmentation of complex, subcortical brain structures. In the following, we present a multi-view DL-based approach to segment the claustrum in T1-weighted MRI scans. We trained and evaluated the proposed method in 181 individuals, using bilateral manual claustrum annotations by an expert neuroradiologist as reference standard. Cross-validation experiments yielded median volumetric similarity, robust Hausdorff distance, and Dice score of 93.3%, 1.41 mm, and 71.8%, respectively, representing equal or superior segmentation performance compared to human intra-rater reliability. The leave-one-scanner-out evaluation showed good transferability of the algorithm to images from unseen scanners at slightly inferior performance. Furthermore, we found that DL-based claustrum segmentation benefits from multi-view information and requires a sample size of around 75 MRI scans in the training set. We conclude that the developed algorithm allows for robust automated claustrum segmentation and thus yields considerable potential for facilitating MRI-based research of the human claustrum. The software and models of our method are made publicly available

Desulfurispira natronophila gen. nov. sp. nov.: an obligately anaerobic dissimilatory sulfur-reducing bacterium from soda lakes

Anaerobic enrichment cultures with elemental sulfur as electron acceptor and either acetate or propionate as electron donor and carbon source at pH 10 and moderate salinity inoculated with sediments from soda lakes in Kulunda Steppe (Altai, Russia) resulted in the isolation of two novel members of the bacterial phylum Chrysiogenetes. The isolates, AHT11 and AHT19, represent the first specialized obligate anaerobic dissimilatory sulfur respirers from soda lakes. They use either elemental sulfur/polysulfide or arsenate as electron acceptor and a few simple organic compounds as electron donor and carbon source. Elemental sulfur is reduced to sulfide through intermediate polysulfide, while arsenate is reduced to arsenite. The bacteria belong to the obligate haloalkaliphiles, with a pH growth optimum from 10 to 10.2 and a salt range from 0.2 to 3.0 M Na+ (optimum 0.4–0.6 M). According to the phylogenetic analysis, the two strains were close to each other, but distinct from the nearest relative, the haloalkaliphilic sulfur-reducing bacterium Desulfurispirillum alkaliphilum, which was isolated from a bioreactor. On the basis of distinct phenotype and phylogeny, the soda lake isolates are proposed as a new genus and species, Desulfurispira natronophila (type strain AHT11T = DSM22071T = UNIQEM U758T)

Desulfuribacillus alkaliarsenatis gen. nov. sp. nov., a deep-lineage, obligately anaerobic, dissimilatory sulfur and arsenate-reducing, haloalkaliphilic representative of the order Bacillales from soda lakes

An anaerobic enrichment culture inoculated with a sample of sediments from soda lakes of the Kulunda Steppe with elemental sulfur as electron acceptor and formate as electron donor at pH 10 and moderate salinity inoculated with sediments from soda lakes in Kulunda Steppe (Altai, Russia) resulted in the domination of a Gram-positive, spore-forming bacterium strain AHT28. The isolate is an obligate anaerobe capable of respiratory growth using elemental sulfur, thiosulfate (incomplete reduction) and arsenate as electron acceptor with H2, formate, pyruvate and lactate as electron donor. Growth was possible within a pH range from 9 to 10.5 (optimum at pH 10) and a salt concentration at pH 10 from 0.2 to 2 M total Na+ (optimum at 0.6 M). According to the phylogenetic analysis, strain AHT28 represents a deep independent lineage within the order Bacillales with a maximum of 90 % 16S rRNA gene similarity to its closest cultured representatives. On the basis of its distinct phenotype and phylogeny, the novel haloalkaliphilic anaerobe is suggested as a new genus and species, Desulfuribacillus alkaliarsenatis (type strain AHT28T = DSM24608T = UNIQEM U855T)

The CCG-domain-containing subunit SdhE of succinate:quinone oxidoreductase from Sulfolobus solfataricus P2 binds a [4Fe–4S] cluster

In type E succinate:quinone reductase (SQR), subunit SdhE (formerly SdhC) is thought to function as monotopic membrane anchor of the enzyme. SdhE contains two copies of a cysteine-rich sequence motif (CXnCCGXmCXXC), designated as the CCG domain in the Pfam database and conserved in many proteins. On the basis of the spectroscopic characterization of heterologously produced SdhE from Sulfolobus tokodaii, the protein was proposed in a previous study to contain a labile [2Fe–2S] cluster ligated by cysteine residues of the CCG domains. Using UV/vis, electron paramagnetic resonance (EPR), 57Fe electron–nuclear double resonance (ENDOR) and Mössbauer spectroscopies, we show that after an in vitro cluster reconstitution, SdhE from S. solfataricus P2 contains a [4Fe–4S] cluster in reduced (2+) and oxidized (3+) states. The reduced form of the [4Fe–4S]2+ cluster is diamagnetic. The individual iron sites of the reduced cluster are noticeably heterogeneous and show partial valence localization, which is particularly strong for one unique ferrous site. In contrast, the paramagnetic form of the cluster exhibits a characteristic rhombic EPR signal with gzyx = 2.015, 2.008, and 1.947. This EPR signal is reminiscent of a signal observed previously in intact SQR from S. tokodaii with gzyx = 2.016, 2.00, and 1.957. In addition, zinc K-edge X-ray absorption spectroscopy indicated the presence of an isolated zinc site with an S3(O/N)1 coordination in reconstituted SdhE. Since cysteine residues in SdhE are restricted to the two CCG domains, we conclude that these domains provide the ligands to both the iron–sulfur cluster and the zinc site

Constraint-based modeling analysis of the metabolism of two Pelobacter species

BACKGROUND: Pelobacter species are commonly found in a number of subsurface environments, and are unique members of the Geobacteraceae family. They are phylogenetically intertwined with both Geobacter and Desulfuromonas species. Pelobacter species likely play important roles in the fermentative degradation of unusual organic matters and syntrophic metabolism in the natural environments, and are of interest for applications in bioremediation and microbial fuel cells. RESULTS: In order to better understand the physiology of Pelobacter species, genome-scale metabolic models for Pelobacter carbinolicus and Pelobacter propionicus were developed. Model development was greatly aided by the availability of models of the closely related Geobacter sulfurreducens and G. metallireducens. The reconstructed P. carbinolicus model contains 741 genes and 708 reactions, whereas the reconstructed P. propionicus model contains 661 genes and 650 reactions. A total of 470 reactions are shared among the two Pelobacter models and the two Geobacter models. The different reactions between the Pelobacter and Geobacter models reflect some unique metabolic capabilities such as fermentative growth for both Pelobacter species. The reconstructed Pelobacter models were validated by simulating published growth conditions including fermentations, hydrogen production in syntrophic co-culture conditions, hydrogen utilization, and Fe(III) reduction. Simulation results matched well with experimental data and indicated the accuracy of the models. CONCLUSIONS: We have developed genome-scale metabolic models of P. carbinolicus and P. propionicus. These models of Pelobacter metabolism can now be incorporated into the growing repertoire of genome scale models of the Geobacteraceae family to aid in describing the growth and activity of these organisms in anoxic environments and in the study of their roles and interactions in the subsurface microbial community

Genomic Characterization of Methanomicrobiales Reveals Three Classes of Methanogens

BACKGROUND:Methanomicrobiales is the least studied order of methanogens. While these organisms appear to be more closely related to the Methanosarcinales in ribosomal-based phylogenetic analyses, they are metabolically more similar to Class I methanogens. METHODOLOGY/PRINCIPAL FINDINGS:In order to improve our understanding of this lineage, we have completely sequenced the genomes of two members of this order, Methanocorpusculum labreanum Z and Methanoculleus marisnigri JR1, and compared them with the genome of a third, Methanospirillum hungatei JF-1. Similar to Class I methanogens, Methanomicrobiales use a partial reductive citric acid cycle for 2-oxoglutarate biosynthesis, and they have the Eha energy-converting hydrogenase. In common with Methanosarcinales, Methanomicrobiales possess the Ech hydrogenase and at least some of them may couple formylmethanofuran formation and heterodisulfide reduction to transmembrane ion gradients. Uniquely, M. labreanum and M. hungatei contain hydrogenases similar to the Pyrococcus furiosus Mbh hydrogenase, and all three Methanomicrobiales have anti-sigma factor and anti-anti-sigma factor regulatory proteins not found in other methanogens. Phylogenetic analysis based on seven core proteins of methanogenesis and cofactor biosynthesis places the Methanomicrobiales equidistant from Class I methanogens and Methanosarcinales. CONCLUSIONS/SIGNIFICANCE:Our results indicate that Methanomicrobiales, rather than being similar to Class I methanogens or Methanomicrobiales, share some features of both and have some unique properties. We find that there are three distinct classes of methanogens: the Class I methanogens, the Methanomicrobiales (Class II), and the Methanosarcinales (Class III)

Insight into the proteome of the hyperthermophilic Crenarchaeon Ignicoccus hospitalis: the major cytosolic and membrane proteins

Ignicoccus hospitalis, a hyperthermophilic, chemolithoautotrophic Crenarchaeon, is the host of Nanoarchaeum equitans. Together, they form an intimate association, the first among Archaea. Membranes are of fundamental importance for the interaction of I. hospitalis and N. equitans, as they harbour the proteins necessary for the transport of macromolecules like lipids, amino acids, and cofactors between these organisms. Here, we investigated the protein inventory of I. hospitalis cells, and were able to identify 20 proteins in total. Experimental evidence and predictions let us conclude that 11 are soluble cytosolic proteins, eight membrane or membrane-associated proteins, and a single one extracellular. The quantitatively dominating proteins in the cytoplasm (peroxiredoxin; thermosome) antagonize oxidative and temperature stress which I. hospitalis cells are exposed to at optimal growth conditions. Three abundant membrane protein complexes are found: the major protein of the outer membrane, which might protect the cell against the hostile environment, forms oligomeric complexes with pores of unknown selectivity; two other complexes of the cytoplasmic membrane, the hydrogenase and the ATP synthase, play a key role in energy production and conversion