Capturing Truthiness: Mining Truth Tables in Binary Datasets

We introduce a new data mining problem: mining truth tables in binary datasets. Given a matrix of objects and the properties they satisfy, a truth table identifies a subset of properties that exhibit maximal variability (and hence, complete independence) in occurrence patterns over the underlying objects. This problem is relevant in many domains, e.g., bioinformatics where we seek to identify and model independent components of combinatorial regulatory pathways, and in social/economic demographics where we desire to determine independent behavioral attributes of populations. Besides intrinsic interest in such patterns, we show how the problem of mining truth tables is dual to the problem of mining redescriptions, in that a set of properties involved in a truth table cannot participate in any possible redescription. This allows us to adapt our algorithm to the problem of mining redescriptions as well, by first identifying regions where redescriptions cannot happen, and then pursuing a divide and conquer strategy around these regions. Furthermore, our work suggests dual mining strategies where both classes of algorithms can be brought to bear upon either data mining task. We outline a family of levelwise approaches adapted to mining truth tables, algorithmic optimizations, and applications to bioinformatics and political datasets

Tied to the worldly work of writing: parent as ethnographer

Parent narratives have contributed to ethnographic accounts of the lives of autistic children (Kelly, 2005) but there are fewer examples of parents producing their own autoethnographies. This paper explores the affordances of an online blog for enabling a parent of an autistic child to produce a written record of practice which may be considered 'autoethnographic'. Richardson’s (2005) framework for ethnography as Creative Analytic Process is applied to extracts from a blog post in order to consider its contribution; reflexivity; aesthetic merit; and impact. The paper addresses the methodological and ethical implications of reconceptualising parents as researchers and the potential contribution of new writing platforms to the development of auto/ethnography. Key words: Autism, Auto/ethnography, Blog, Disability, Mothe

The Effects of a High-Protein Diet on Markers of Muscle Damage Following Exercise in Active Older Adults: A Randomized, Controlled Trial.

PURPOSE:This study examined whether a higher protein diet following strenuous exercise can alter markers of muscle damage and inflammation in older adults. METHODS:Using a double-blind, independent group design, 10 males and eight females (age 57 ± 4 years; mass 72.3 ± 5.6 kg; height 1.7 ± 6.5 m) were supplied with a higher protein (2.50 g·kg-1·day-1) or moderate protein (1.25 g·kg-1·day-1) diet for 48 hr after 140 squats with 25% of their body mass. Maximal isometric voluntary contractions, muscle soreness, creatine kinase, Brief Assessment of Mood Adapted, and inflammatory markers were measured preexercise, and 24 hr and 48 hr postexercise. RESULTS:The maximal isometric voluntary contractions decreased postexercise (p = .001, ηp2=.421), but did not differ between groups (p = .822, ηp2=.012). Muscle soreness peaked at 24 hr post in moderate protein (44 ± 30 mm) and 48 hr post in higher protein (70 ± 46 mm; p = .005; ηp2=.282); however, no group differences were found (p = .585; ηp2=.083). Monocytes and lymphocytes significantly decreased postexercise, and eosinophils increased 24 hr postexercise (p .05). CONCLUSION:In conclusion, 2.50 g·kg-1·day-1 of protein is not more effective than 1.25 g·kg-1·day-1 for attenuating indirect markers of muscle damage and inflammation following strenuous exercise in older adults

Network-based functional enrichment

<p>Abstract</p> <p>Background</p> <p>Many methods have been developed to infer and reason about molecular interaction networks. These approaches often yield networks with hundreds or thousands of nodes and up to an order of magnitude more edges. It is often desirable to summarize the biological information in such networks. A very common approach is to use gene function enrichment analysis for this task. A major drawback of this method is that it ignores information about the edges in the network being analyzed, i.e., it treats the network simply as a set of genes. In this paper, we introduce a novel method for functional enrichment that explicitly takes network interactions into account.</p> <p>Results</p> <p>Our approach naturally generalizes Fisher’s exact test, a gene set-based technique. Given a function of interest, we compute the subgraph of the network induced by genes annotated to this function. We use the sequence of sizes of the connected components of this sub-network to estimate its connectivity. We estimate the statistical significance of the connectivity empirically by a permutation test. We present three applications of our method: i) determine which functions are enriched in a given network, ii) given a network and an interesting sub-network of genes within that network, determine which functions are enriched in the sub-network, and iii) given two networks, determine the functions for which the connectivity improves when we merge the second network into the first. Through these applications, we show that our approach is a natural alternative to network clustering algorithms.</p> <p>Conclusions</p> <p>We presented a novel approach to functional enrichment that takes into account the pairwise relationships among genes annotated by a particular function. Each of the three applications discovers highly relevant functions. We used our methods to study biological data from three different organisms. Our results demonstrate the wide applicability of our methods. Our algorithms are implemented in C++ and are freely available under the GNU General Public License at our supplementary website. Additionally, all our input data and results are available at <url>http://bioinformatics.cs.vt.edu/~murali/supplements/2011-incob-nbe/</url>.</p

Transdimensional inversion of receiver functions and surface wave dispersion

International audienceWe present a novel method for joint inversion of receiver functions and surface wave dispersion data, using a transdimensional Bayesian formulation. This class of algorithm treats the number of model parameters (e.g. number of layers) as an unknown in the problem. The dimension of the model space is variable and a Markov chain Monte Carlo (McMC) scheme is used to provide a parsimonious solution that fully quantifies the degree of knowledge one has about seismic structure (i.e constraints on the model, resolution, and trade-offs). The level of data noise (i.e. the covariance matrix of data errors) effectively controls the information recoverable from the data and here it naturally determines the complexity of the model (i.e. the number of model parameters). However, it is often difficult to quantify the data noise appropriately, particularly in the case of seismic waveform inversion where data errors are correlated. Here we address the issue of noise estimation using an extended Hierarchical Bayesian formulation, which allows both the variance and covariance of data noise to be treated as unknowns in the inversion. In this way it is possible to let the data infer the appropriate level of data fit. In the context of joint inversions, assessment of uncertainty for different data types becomes crucial in the evaluation of the misfit function. We show that the Hierarchical Bayes procedure is a powerful tool in this situation, because it is able to evaluate the level of information brought by different data types in the misfit, thus removing the arbitrary choice of weighting factors. After illustrating the method with synthetic tests, a real data application is shown where teleseismic receiver functions and ambient noise surface wave dispersion measurements from the WOMBAT array (South-East Australia) are jointly inverted to provide a probabilistic 1D model of shear-wave velocity beneath a given station

Concert recording 2016-11-15

[Track 1]. Subjugation. Connection [Track 2]. Captivation / Durgan Maxey -- [Track 3]. Fight / Bryce Owens -- [Track 4]. Overture to Stay / Joshua Bland -- [Track 5]. A cellist\u27s legacy. Part I [Track 6]. Part II / Eric Dreggors -- [Track 7]. Evening prayer / Robbie Baker -- [Track 8]. Elegy / Brandon Wade -- [Track 9]. The grotesques trio. Gargoyles [Track 10]. Chimera [Track 11]. Grotesques / Marissa Johnson -- [Track 12]. Crosshair / Joshua Bland -- [Track 13]. Nightwind sings / L. Coley Pitchford -- [Track 14]. Six reflections through poetry. Memories (Walt Whitman) [Track 15]. The musician\u27s wife (Weldon Kees) [Track 16]. The road not taken (Robert Frost) [Track 17]. Lessons (Whitman) [Track 18]. Stronger lessons (Whitman) [Track 19]. O me! O life! (Whitman) / Nick Vecchio -- [Tracks 20-21]. String quartet #1 / Jeremiah Flannery -- [Track 22]. Tides. Morning tide [Track 23]. Bore tide / Elizabeth Greener -- [Track 24]. Shepherd\u27s contemplation / Robbie Baker -- Green grass / arranged by Eva Martin -- [Track 25]. Urbe fracta est II. A prayer for Jerusalem / Joshua Bland

Using trained dogs and organic semi-conducting sensors to identify asymptomatic and mild SARS-CoV-2 infections: an observational study

BACKGROUND: A rapid, accurate, non-invasive diagnostic screen is needed to identify people with SARS-CoV-2 infection. We investigated whether organic semi-conducting (OSC) sensors and trained dogs could distinguish between people infected with asymptomatic or mild symptoms, and uninfected individuals, and the impact of screening at ports-of-entry. METHODS: Odour samples were collected from adults, and SARS-CoV-2 infection status confirmed using RT-PCR. OSC sensors captured the volatile organic compound (VOC) profile of odour samples. Trained dogs were tested in a double-blind trial to determine their ability to detect differences in VOCs between infected and uninfected individuals, with sensitivity and specificity as the primary outcome. Mathematical modelling was used to investigate the impact of bio-detection dogs for screening. RESULTS: About, 3921 adults were enrolled in the study and odour samples collected from 1097 SARS-CoV-2 infected and 2031 uninfected individuals. OSC sensors were able to distinguish between SARS-CoV-2 infected individuals and uninfected, with sensitivity from 98% (95% CI 95–100) to 100% and specificity from 99% (95% CI 97–100) to 100%. Six dogs were able to distinguish between samples with sensitivity ranging from 82% (95% CI 76–87) to 94% (95% CI 89–98) and specificity ranging from 76% (95% CI 70–82) to 92% (95% CI 88–96). Mathematical modelling suggests that dog screening plus a confirmatory PCR test could detect up to 89% of SARS-CoV-2 infections, averting up to 2.2 times as much transmission compared to isolation of symptomatic individuals only. CONCLUSIONS: People infected with SARS-CoV-2, with asymptomatic or mild symptoms, have a distinct odour that can be identified by sensors and trained dogs with a high degree of accuracy. Odour-based diagnostics using sensors and/or dogs may prove a rapid and effective tool for screening large numbers of people. Trial Registration NCT04509713 (clinicaltrials.gov)

Margarita de Sossa, Sixteenth-Century Puebla de los Ángeles, New Spain (Mexico)

Margarita de Sossa’s freedom journey was defiant and entrepreneurial. In her early twenties, still enslaved in Portugal, she took possession of her body; after refusing to endure her owner’s sexual demands, he sold her, and she was transported to Mexico. There, she purchased her freedom with money earned as a healer and then conducted an enviable business as an innkeeper. Sossa’s biography provides striking insights into how she conceptualized freedom in terms that included – but was not limited to – legal manumission. Her transatlantic biography offers a rare insight into the life of a free black woman (and former slave) in late sixteenth-century Puebla, who sought to establish various degrees of freedom for herself. Whether she was refusing to acquiesce to an abusive owner, embracing entrepreneurship, marrying, purchasing her own slave property, or later using the courts to petition for divorce. Sossa continued to advocate on her own behalf. Her biography shows that obtaining legal manumission was not always equivalent to independence and autonomy, particularly if married to an abusive husband, or if financial successes inspired the envy of neighbors

Pharmacology and therapeutic implications of current drugs for type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is a global epidemic that poses a major challenge to health-care systems. Improving metabolic control to approach normal glycaemia (where practical) greatly benefits long-term prognoses and justifies early, effective, sustained and safety-conscious intervention. Improvements in the understanding of the complex pathogenesis of T2DM have underpinned the development of glucose-lowering therapies with complementary mechanisms of action, which have expanded treatment options and facilitated individualized management strategies. Over the past decade, several new classes of glucose-lowering agents have been licensed, including glucagon-like peptide 1 receptor (GLP-1R) agonists, dipeptidyl peptidase 4 (DPP-4) inhibitors and sodium/glucose cotransporter 2 (SGLT2) inhibitors. These agents can be used individually or in combination with well-established treatments such as biguanides, sulfonylureas and thiazolidinediones. Although novel agents have potential advantages including low risk of hypoglycaemia and help with weight control, long-term safety has yet to be established. In this Review, we assess the pharmacokinetics, pharmacodynamics and safety profiles, including cardiovascular safety, of currently available therapies for management of hyperglycaemia in patients with T2DM within the context of disease pathogenesis and natural history. In addition, we briefly describe treatment algorithms for patients with T2DM and lessons from present therapies to inform the development of future therapies