Familial hypocalciuric hypercalcaemia as a differential diagnosis of hyperparathyroidism:studies in a large kindred and a review of surgical experience in the condition

We have studied 46 members of a large kindred with familial hypocalciuric hypercalcaemia (FHH) after a neck exploration failed to cure hypercalcaemia in an asymptomatic patient. Serum calcium, serum phosphate, plasma parathormone and vitamin D metabolites do not distinguish affected members from patients with hyperparathyroidism. Because of the continuing debate as to whether or not FHH is a variant of, or distinct from, hyperparathyroidism, we have carried out a review of surgical experience with subtotal parathyroidectomy in hyperparathyroidism secondary to parathyroid hyperplasia and in FHH. Whereas the procedure is successful in 90 per cent of the former cases only one case of FHH has been cured by it. This provides evidence for the two conditions being aetiologically distinct. Before patients with asymptomatic hypercalcaemia are referred for parathyroid surgery the calcium:creatinine clearance ratio should be measured using a 2 h urine sample collected after an overnight fast and a fasting blood sample. If this ratio is less than 0.01 then screening of first degree relations should be undertaken before any parathyroid surgery is performed. Unnecessary surgery can therefore be avoided.

TGFβ and CCN2/CTGF mediate actin related gene expression by differential E2F1/CREB activation

CCN2/CTGF is an established effector of TGFß driven responses in diabetic nephropathy. We have identified an interaction between CCN2 and TGFß leading to altered phenotypic differentiation and inhibited cellular migration. Here we determine the gene expression profile associated with this phenotype and define a transcriptional basis for differential actin related gene expression and cytoskeletal function.

Acquired differential regulation of caspase-8 in cisplatin-resistant non-small-cell lung cancer

Failure to efficiently induce apoptosis contributes to cisplatin resistance in non-small-cell lung cancer (NSCLC). Although BCL-2-associated X protein (BAX) and BCL-2 antagonist killer (BAK) are critical regulators of the mitochondrial apoptosis pathway, their requirement has not been robustly established in relation to cisplatin. Here, we show that cisplatin can efficiently bypass mitochondrial apoptosis block caused by loss of BAX and BAK, via activation of the extrinsic death receptor pathway in some model cell lines. Apoptosis resistance following cisplatin can only be observed when both extrinsic and intrinsic pathways are blocked, consistent with redundancy between mitochondrial and death receptor pathways in cisplatin-induced apoptosis. In H460 NSCLC cells, caspase-8 cleavage was shown to be induced by cisplatin and is dependent on death receptor 4, death receptor 5, Fas-associated protein with death domain, acid sphingomyelinase and ceramide synthesis. In contrast, cisplatin-resistant cells fail to activate caspase-8 via this pathway despite conserving sensitivity to death ligand-driven activation. Accordingly, caspase-8 activation block acquired during cisplatin resistance, can be bypassed by death receptor agonism. © 2012 Macmillan Publishers Limited

Learning Effective and Interpretable Semantic Models using Non-Negative Sparse Embedding

In this paper, we introduce an application of matrix factorization to produce corpus-derived, distributional
models of semantics that demonstrate cognitive plausibility. We find that word representations
learned by Non-Negative Sparse Embedding (NNSE), a variant of matrix factorization, are sparse,
effective, and highly interpretable. To the best of our knowledge, this is the first approach which
yields semantic representation of words satisfying these three desirable properties. Though extensive
experimental evaluations on multiple real-world tasks and datasets, we demonstrate the superiority
of semantic models learned by NNSE over other state-of-the-art baselines.

Strudel: A corpus-based semantic model based on properties and types

Computational models of meaning trained on naturally occurring text successfully model human performance on tasks involving simple similarity measures, but they characterize meaning in terms of undifferentiated bags of words or topical dimensions. This has led some to question their psychological plausibility (Murphy, 2002; Schunn, 1999). We present here a fully automatic method for extracting a structured and comprehensive set of concept descriptions directly from an English part-of-speech-tagged corpus. Concepts are characterized by weighted properties, enriched with concept-property types that approximate classical relations such as hypernymy and function. Our model outperforms comparable algorithms in cognitive tasks pertaining not only to concept-internal structures (discovering properties of concepts, grouping properties by property type) but also to inter-concept relations (clustering into superordinates), suggesting the empirical validity of the property-based approach. Copyright © 2009 Cognitive Science Society, Inc. All rights reserved.

EEG decoding of semantic category reveals distributed representations for single concepts

Achieving a clearer picture of categorial distinctions in the brain is essential for our understanding of the conceptual lexicon, but much more fine-grained investigations are required in order for this evidence to contribute to lexical research. Here we present a collection of advanced data-mining techniques that allows the category of individual concepts to be decoded from single trials of EEG data. Neural activity was recorded while participants silently named images of mammals and tools, and category could be detected in single trials with an accuracy well above chance, both when considering data from single participants, and when group-training across participants. By aggregating across all trials, single concepts could be correctly assigned to their category with an accuracy of 98%. The pattern of classifications made by the algorithm confirmed that the neural patterns identified are due to conceptual category, and not any of a series of processing-related confounds. The time intervals, frequency bands and scalp locations that proved most informative for prediction permit physiological interpretation: the widespread activation shortly after appearance of the stimulus (from 100. ms) is consistent both with accounts of multi-pass processing, and distributed representations of categories. These methods provide an alternative to fMRI for fine-grained, large-scale investigations of the conceptual lexicon. © 2010 Elsevier Inc.

The role of semantic interference in limiting memory for the details of visual scenes

Many studies suggest a large capacity memory for briefly presented pictures of whole scenes. At the same time, visual working memory (WM) of scene elements is limited to only a few items. We examined the role of retroactive interference in limiting memory for visual details. Participants viewed a scene for 5?s and then, after a short delay containing either a blank screen or 10 distracter scenes, answered questions about the location, color, and identity of objects in the scene. We found that the influence of the distracters depended on whether they were from a similar semantic domain, such as "kitchen" or "airport." Increasing the number of similar scenes reduced, and eventually eliminated, memory for scene details. Although scene memory was firmly established over the initial study period, this memory was fragile and susceptible to interference. This may help to explain the discrepancy in the literature between studies showing limited visual WM and those showing a large capacity memory for scenes.

Documents and Dependencies: an Exploration of Vector Space Models for Semantic Composition

In most previous research on distributional semantics, Vector Space Models (VSMs) of words are built either from topical information (e.g., documents in which a word is present), or from syntactic/semantic types of words (e.g., dependency parse links of a word in sentences), but not both. In this paper, we explore the utility of combining these two representations to build VSM for the task of semantic composition of adjective-noun phrases. Through extensive experiments on benchmark datasets, we find that even though a type-based VSM is effective for semantic composition, it is often outperformed by a VSM built using a combination of topic- and type-based statistics. We also introduce a new evaluation task wherein we predict the composed vector representation of a phrase from the brain activity of a human subject reading that phrase. We exploit a large syntactically parsed corpus of 16 billion tokens to build our VSMs, with vectors for both phrases and words, and make them publicly available.

Differential expression of liver fluke β-tubulin isotypes at selected life cycle stages

We have shown that Fasciola hepatica expresses at least six ß-tubulins in the adult stage of its life cycle, designated F.hep-ß-tub1-6 (Ryan et al., 2008). Here we show that different complements of tubulin isotypes are expressed in different tissues and at different life cycle stages; this information may inform the search for novel anthelmintics. The predominant (as judged by quantitative PCR) isotype transcribed at the adult stage was F.hep-ß-tub1 and immunolocalisation studies revealed that this isotype occurred mainly in mature spermatozoa and vitelline follicles. Quantitative PCR indicated that changes occurred in the transcription levels of ß-tubulin isotypes at certain life cycle stages and may be of importance in the efficacy of benzimidazole-based anthelmintic drugs, but there were no significant differences between the triclabendazole (TCBZ)-susceptible Leon isolate and the TCBZ-resistant Oberon isolate in the transcription levels of each of the isotypes. When three well-characterised isolates with differing susceptibilities to TCBZ were compared, only one amino acid change resulting from a homozygous coding sequence difference (Gly269Ser) in isotype 4 was observed. However, this change was not predicted to alter the overall structure of the protein. In conclusion, these findings indicate that there is tissue-specific expression of tubulin isotypes in the liver fluke but the development of resistance to TCBZ is not associated with changes in its presumed target molecule.

Gene Sets Net Correlations Analysis (GSNCA): a multivariate differential coexpression test for gene sets

Motivation: To date, Gene Set Analysis (GSA) approaches primarily focus on identifying differentially expressed gene sets (pathways). Methods for identifying differentially coexpressed pathways also exist but are mostly based on aggregated pairwise correlations, or other pairwise measures of coexpression. Instead, we propose Gene Sets Net Correlations Analysis (GSNCA), a multivariate differential coexpression test that accounts for the complete correlation structure between genes.

Results: In GSNCA, weight factors are assigned to genes in proportion to the genes' cross-correlations (intergene correlations). The problem of finding the weight vectors is formulated as an eigenvector problem with a unique solution. GSNCA tests the null hypothesis that for a gene set there is no difference in the weight vectors of the genes between two conditions. In simulation studies and the analyses of experimental data, we demonstrate that GSNCA, indeed, captures changes in the structure of genes' cross-correlations rather than differences in the averaged pairwise correlations. Thus, GSNCA infers differences in coexpression networks, however, bypassing method-dependent steps of network inference. As an additional result from GSNCA, we define hub genes as genes with the largest weights and show that these genes correspond frequently to major and specific pathway regulators, as well as to genes that are most affected by the biological difference between two conditions. In summary, GSNCA is a new approach for the analysis of differentially coexpressed pathways that also evaluates the importance of the genes in the pathways, thus providing unique information that may result in the generation of novel biological hypotheses.