Disposition of Federally Owned Surpluses

PDZ domains are scaffolding modules in protein-protein interactions that mediate numerous physiological functions by interacting canonically with the C-terminus or non-canonically with an internal motif of protein ligands. A conserved carboxylate-binding site in the PDZ domain facilitates binding via backbone hydrogen bonds; however, little is known about the role of these hydrogen bonds due to experimental challenges with backbone mutations. Here we address this interaction by generating semisynthetic PDZ domains containing backbone amide-to-ester mutations and evaluating the importance of individual hydrogen bonds for ligand binding. We observe substantial and differential effects upon amide-to-ester mutation in PDZ2 of postsynaptic density protein 95 and other PDZ domains, suggesting that hydrogen bonding at the carboxylate-binding site contributes to both affinity and selectivity. In particular, the hydrogen-bonding pattern is surprisingly different between the non-canonical and canonical interaction. Our data provide a detailed understanding of the role of hydrogen bonds in protein-protein interactions

Ultranationalism, democracy and the law: insights from Côte d'Ivoire

Although much has been written about the ideology of Laurent Gbagbo's Front Populaire Ivoirien in Côte d'Ivoire and its impact on the Ivorian politico-military crisis, little attention has been paid to the ubiquitous role of the law in the discourse and political strategy of the pro-Gbagbo elite. The Ivorian case may provide important insights about the connection between ultranationalist ideology and a legalist, formalist conception of democracy and national sovereignty. The article analyses the circumstances of the emergence of ‘legalist nationalism’ in Côte d'Ivoire by looking at key episodes of the Ivorian transition between 2002 and 2012. The article discusses the relevance of Pierre Englebert's concept of ‘legal command’ and the turbulences of democratic transitions in accounting for the prominence of legalism in Ivorian politics. It explores the implications of the Ivorian case for understanding the connection between law and politics in Africa

Moody Music Generator: Characterising Control Parameters Using Crowdsourcing.

Abstract. We characterise the expressive effects of a music generator capable of varying its moods through two control parameters. The two control parameters were constructed on the basis of existing work on va-lence and arousal in music, and intended to provide control over those two mood factors. In this paper we conduct a listener study to determine how people actually perceive the various moods the generator can produce. Rather than directly attempting to validate that our two control param-eters represent arousal and valence, instead we conduct an open-ended study to crowd-source labels characterising different parts of this two-dimensional control space. Our aim is to characterise perception of the generator’s expressive space, without constraining listeners ’ responses to labels specifically aimed at validating the original arousal/valence moti-vation. Subjects were asked to listen to clips of generated music over the Internet, and to describe the moods with free-text labels. We find that the arousal parameter does roughly map to perceived arousal, but that the nominal “valence ” parameter has strong interaction with the arousal parameter, and produces different effects in different parts of the con-trol space. We believe that the characterisation methodology described here is general and could be used to map the expressive range of other parameterisable generators.

Practical mammography

‘Digital health’ is an overarching concept that currently lacks theoretical definition and common terminology. For instance, this broad and emerging field includes all of the following terms within its lexicon: mHealth, Wireless Health, Health 2.0, eHealth, e-Patient(s), Healthcare IT/Health IT, Big Data, Health Data, Cloud Computing, Quantified Self, Wearable Computing, Gamification, and Telehealth/Telemedicine [1]. However, whilst a definition is difficult to provide, in this overview it is considered that digital health is the use of digital media to transform the way healthcare provision is conceived and delivered. We consider it does this through three basic features

The significance of measuring monocyte tissue factor activity in patients with breast and colorectal cancer

Monocytes express tissue factor (mTF) in several conditions including cancer where levels may be valuable in assessing tumour presence and progression. Using a two-stage kinetic chromogenic assay (KCA), mTF levels were measured in controls [normal subjects (n = 60) and patients undergoing hernia repair or cholecystectomy (n = 60)], in patients with benign and malignant disease of the breast (n = 83) and of the large bowel (n = 62). This was performed under fresh (resting) conditions and after incubation for 6 h without (unstimulated) and with (stimulated) Escherichia coli endotoxin. The malignant groups showed higher mTF levels than each of the three controls for resting (P < 0.05 breast, P < 0.05 colorectal) unstimulated (P < 0.05 breast, P < 0.05 colorectal) and stimulated cells (P < 0.001 breast, P < 0.01 colorectal). Similarly, the benign inflammatory groups had higher mTF levels than controls for resting (P < 0.05 colorectal), unstimulated (P < 0.05 colorectal) and stimulated cells (P < 0.01 breast, P < 0.01 colorectal). There was no significant difference between malignant and benign inflammatory groups in each organ. mTF levels showed an increase corresponding to that of histological tumour progression and were higher in non-surviving patients. In conclusion, mTF levels are raised in malignant and inflammatory disease compared to controls and patients with non-inflammatory conditions. Stimulated cells give better discrimination between the groups and may be of value in identifying high risk individuals. mTF levels showed an association with tumour grade or stage and the patients' survival time

Blocking human fear memory with the matrix metalloproteinase inhibitor doxycycline

Learning to predict threat is a fundamental ability of many biological organisms, and a laboratory model for anxiety disorders. Interfering with such memories in humans would be of high clinical relevance. On the basis of studies in cell cultures and slice preparations, it is hypothesised that synaptic remodelling required for threat learning involves the extracellular enzyme matrix metalloproteinase (MMP) 9. However, in vivo evidence for this proposal is lacking. Here we investigate human Pavlovian fear conditioning under the blood-brain barrier crossing MMP inhibitor doxycyline in a pre-registered, randomised, double-blind, placebo-controlled trial. We find that recall of threat memory, measured with fear-potentiated startle 7 days after acquisition, is attenuated by ~60% in individuals who were under doxycycline during acquisition. This threat memory impairment is also reflected in increased behavioural surprise signals to the conditioned stimulus during subsequent re-learning, and already late during initial acquisition. Our findings support an emerging view that extracellular signalling pathways are crucially required for threat memory formation. Furthermore, they suggest novel pharmacological methods for primary prevention and treatment of posttraumatic stress disorder.Molecular Psychiatry advance online publication, 4 April 2017; doi:10.1038/mp.2017.65

The impact of maternal separation on adult mouse behaviour and on the total neuron number in the mouse hippocampus

The maternal separation paradigm has been applied to C57BL/6J mice as an animal developmental model for understanding structural deficits leading to abnormal behaviour. A maternal separation (MS) model was used on postnatal day (PND) 9, where the pups were removed from their mother for 24 h (MS24). When the pups were 10 weeks old, the level of anxiety and fear was measured with two behavioural tests; an open field test and an elevated plus maze test. The Barnes platform maze was used to test spatial learning, and memory by using acquisition trials followed by reverse trial sessions. The MS24 mice spent more time in the open arms of the elevated plus maze compared to controls, but no other treatment differences were found in the emotional behavioural tests. However, in the reverse trial for the Barnes maze test there was a significant difference in the frequency of visits to the old goal, the number of errors made by the MS24 mice compared to controls and in total distance moved. The mice were subsequently sacrificed and the total number of neurons estimated in the hippocampus using the optical fractionator. We found a significant loss of neurons in the dentate gyrus in MS mice compared to controls. Apparently a single maternal separation can impact the number of neurons in mouse hippocampus either by a decrease of neurogenesis or as an increase in neuron apoptosis. This study is the first to assess the result of maternal separation combining behaviour and stereology

Influence of shells on mating behavior in the hermit crab Calcinus tibicen

Populations of the intertidal hermit crab Calcinus tibicen were observed in the laboratory and reproductive behaviors recorded. Of the 218 interactions, 68 resulted in copulation(s). Male and female sizes were positively correlated. Male size affected copulation success in a non-linear fashion. In particular, the largest males did not obtain any copulations. This was largely a consequence of the shell species occupied by large individuals; males in Nerita sp and Cittarium pica shells were unsuccessful in courtship. The ability to execute precopulatory rotation of the female was negatively affected by certain shell types. Repeated pairings of individuals suggested some level of individual recognition within the reproductively active population.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46890/1/265_2004_Article_BF00293264.pd

Self-explaining AI as an alternative to interpretable AI

The ability to explain decisions made by AI systems is highly sought after, especially in domains where human lives are at stake such as medicine or autonomous vehicles. While it is often possible to approximate the input-output relations of deep neural networks with a few human-understandable rules, the discovery of the double descent phenomena suggests that such approximations do not accurately capture the mechanism by which deep neural networks work. Double descent indicates that deep neural networks typically operate by smoothly interpolating between data points rather than by extracting a few high level rules. As a result, neural networks trained on complex real world data are inherently hard to interpret and prone to failure if asked to extrapolate. To show how we might be able to trust AI despite these problems we introduce the concept of self-explaining AI. Self-explaining AIs are capable of providing a human-understandable explanation of each decision along with confidence levels for both the decision and explanation. For this approach to work, it is important that the explanation actually be related to the decision, ideally capturing the mechanism used to arrive at the explanation. Finally, we argue it is important that deep learning based systems include a "warning light" based on techniques from applicability domain analysis to warn the user if a model is asked to extrapolate outside its training distribution. For a video presentation of this talk see https://www.youtube.com/watch?v=Py7PVdcu7WY& .Comment: 10pgs, 2 column forma

An Adaptive Sublinear-Time Block Sparse Fourier Transform

The problem of approximately computing the $k$ dominant Fourier coefficients of a vector $X$ quickly, and using few samples in time domain, is known as the Sparse Fourier Transform (sparse FFT) problem. A long line of work on the sparse FFT has resulted in algorithms with $O(k\log n\log (n/k))$ runtime [Hassanieh \emph{et al.}, STOC'12] and $O(k\log n)$ sample complexity [Indyk \emph{et al.}, FOCS'14]. These results are proved using non-adaptive algorithms, and the latter $O(k\log n)$ sample complexity result is essentially the best possible under the sparsity assumption alone: It is known that even adaptive algorithms must use $\Omega((k\log(n/k))/\log\log n)$ samples [Hassanieh \emph{et al.}, STOC'12]. By {\em adaptive}, we mean being able to exploit previous samples in guiding the selection of further samples. This paper revisits the sparse FFT problem with the added twist that the sparse coefficients approximately obey a $(k_0,k_1)$-block sparse model. In this model, signal frequencies are clustered in $k_0$ intervals with width $k_1$ in Fourier space, and $k= k_0k_1$ is the total sparsity. Signals arising in applications are often well approximated by this model with $k_0\ll k$. Our main result is the first sparse FFT algorithm for $(k_0, k_1)$-block sparse signals with a sample complexity of $O^*(k_0k_1 + k_0\log(1+ k_0)\log n)$ at constant signal-to-noise ratios, and sublinear runtime. A similar sample complexity was previously achieved in the works on {\em model-based compressive sensing} using random Gaussian measurements, but used $\Omega(n)$ runtime. To the best of our knowledge, our result is the first sublinear-time algorithm for model based compressed sensing, and the first sparse FFT result that goes below the $O(k\log n)$ sample complexity bound. Interestingly, the aforementioned model-based compressive sensing result that relies on Gaussian measurements is non-adaptive, whereas our algorithm crucially uses {\em adaptivity} to achieve the improved sample complexity bound. We prove that adaptivity is in fact necessary in the Fourier setting: Any {\em non-adaptive} algorithm must use $\Omega(k_0k_1\log \frac{n}{k_0k_1})$ samples for the $(k_0,k_1$)-block sparse model, ruling out improvements over the vanilla sparsity assumption. Our main technical innovation for adaptivity is a new randomized energy-based importance sampling technique that may be of independent interest