Contextual Motifs: Increasing the Utility of Motifs using Contextual Data

Motifs are a powerful tool for analyzing physiological waveform data. Standard motif methods, however, ignore important contextual information (e.g., what the patient was doing at the time the data were collected). We hypothesize that these additional contextual data could increase the utility of motifs. Thus, we propose an extension to motifs, contextual motifs, that incorporates context. Recognizing that, oftentimes, context may be unobserved or unavailable, we focus on methods to jointly infer motifs and context. Applied to both simulated and real physiological data, our proposed approach improves upon existing motif methods in terms of the discriminative utility of the discovered motifs. In particular, we discovered contextual motifs in continuous glucose monitor (CGM) data collected from patients with type 1 diabetes. Compared to their contextless counterparts, these contextual motifs led to better predictions of hypo- and hyperglycemic events. Our results suggest that even when inferred, context is useful in both a long- and short-term prediction horizon when processing and interpreting physiological waveform data.Comment: 10 pages, 7 figures, accepted for oral presentation at KDD '1

Resistance loci affecting distinct stages of fungal pathogenesis: use of introgression lines for QTL mapping and characterization in the maize - Setosphaeria turcica pathosystem

<p>Abstract</p> <p>Background</p> <p>Studies on host-pathogen interactions in a range of pathosystems have revealed an array of mechanisms by which plants reduce the efficiency of pathogenesis. While R-gene mediated resistance confers highly effective defense responses against pathogen invasion, quantitative resistance is associated with intermediate levels of resistance that reduces disease progress. To test the hypothesis that specific loci affect distinct stages of fungal pathogenesis, a set of maize introgression lines was used for mapping and characterization of quantitative trait loci (QTL) conditioning resistance to <it>Setosphaeria turcica</it>, the causal agent of northern leaf blight (NLB). To better understand the nature of quantitative resistance, the identified QTL were further tested for three secondary hypotheses: (1) that disease QTL differ by host developmental stage; (2) that their performance changes across environments; and (3) that they condition broad-spectrum resistance.</p> <p>Results</p> <p>Among a set of 82 introgression lines, seven lines were confirmed as more resistant or susceptible than B73. Two NLB QTL were validated in BC₄F₂segregating populations and advanced introgression lines. These loci, designated <it>qNLB1.02 </it>and <it>qNLB1.06</it>, were investigated in detail by comparing the introgression lines with B73 for a series of macroscopic and microscopic disease components targeting different stages of NLB development. Repeated greenhouse and field trials revealed that <it>qNLB1.06_Tx303</it>(the Tx303 allele at bin 1.06) reduces the efficiency of fungal penetration, while <it>qNLB1.02_B73</it>(the B73 allele at bin 1.02) enhances the accumulation of callose and phenolics surrounding infection sites, reduces hyphal growth into the vascular bundle and impairs the subsequent necrotrophic colonization in the leaves. The QTL were equally effective in both juvenile and adult plants; <it>qNLB1.06_Tx303</it>showed greater effectiveness in the field than in the greenhouse. In addition to NLB resistance, <it>qNLB1.02_B73</it>was associated with resistance to Stewart's wilt and common rust, while <it>qNLB1.06_Tx303</it>conferred resistance to Stewart's wilt. The non-specific resistance may be attributed to pleiotropy or linkage.</p> <p>Conclusions</p> <p>Our research has led to successful identification of two reliably-expressed QTL that can potentially be utilized to protect maize from <it>S. turcica </it>in different environments. This approach to identifying and dissecting quantitative resistance in plants will facilitate the application of quantitative resistance in crop protection.</p

Development of a species-specific polymerase chain reaction assay for Gardnerella vaginalis

The nucleotide sequence of the region between the 16S and 23S rRNA genes of the facultative anaerobic bacteriumGardnerella vaginalishas been determined, together with the 5′ proximal 500 nucleotides of the 23S rRNA gene. Regions suited for the development of specific, probe-confirmable polymerase chain reaction (PCR) assays were selected. PCR assays were evaluated with respect to sensitivity and specificity, the latter in comparison with a number ofG. vaginalisreference strains and closely related species likeBifidobacteriumspp. In an initial diagnostic study it appeared that the PCR test detectedG. vaginalisin 40% of women irrespective of their clinical status. Ten out of 11 patients suffering from bacterial vaginosis as defined on the basis of clinical parameters were carryingG. vaginalis

Quantum device fine-tuning using unsupervised embedding learning

Quantum devices with a large number of gate electrodes allow for precise control of device parameters. This capability is hard to fully exploit due to the complex dependence of these parameters on applied gate voltages. We experimentally demonstrate an algorithm capable of fine-tuning several device parameters at once. The algorithm acquires a measurement and assigns it a score using a variational auto-encoder. Gate voltage settings are set to optimise this score in real-time in an unsupervised fashion. We report fine-tuning times of a double quantum dot device within approximately 40 min

Machine learning enables completely automatic tuning of a quantum device faster than human experts

Variability is a problem for the scalability of semiconductor quantum devices. The parameter space is large, and the operating range is small. Our statistical tuning algorithm searches for specific electron transport features in gate-defined quantum dot devices with a gate voltage space of up to eight dimensions. Starting from the full range of each gate voltage, our machine learning algorithm can tune each device to optimal performance in a median time of under 70 minutes. This performance surpassed our best human benchmark (although both human and machine performance can be improved). The algorithm is approximately 180 times faster than an automated random search of the parameter space, and is suitable for different material systems and device architectures. Our results yield a quantitative measurement of device variability, from one device to another and after thermal cycling. Our machine learning algorithm can be extended to higher dimensions and other technologies

Chemical Proteomic Analysis of Serine Hydrolase Activity in Niemann-Pick Type C Mouse Brain

The endocannabinoid system (ECS) is considered to be an endogenous protective system in various neurodegenerative diseases. Niemann-Pick type C (NPC) is a neurodegenerative disease in which the role of the ECS has not been studied yet. Most of the endocannabinoid enzymes are serine hydrolases, which can be studied using activity-based protein profiling (ABPP). Here, we report the serine hydrolase activity in brain proteomes of a NPC mouse model as measured by ABPP. Two ABPP methods are used: a gel-based method and a chemical proteomics method. The activities of the following endocannabinoid enzymes were quantified: diacylglycerol lipase (DAGL) α, α/β-hydrolase domain-containing protein 4, α/β-hydrolase domain-containing protein 6, α/β-hydrolase domain-containing protein 12, fatty acid amide hydrolase, and monoacylglycerol lipase. Using the gel-based method, two bands were observed for DAGL α. Only the upper band corresponding to this enzyme was significantly decreased in the NPC mouse model. Chemical proteomics showed that three lysosomal serine hydrolase activities (retinoid-inducible serine carboxypeptidase, cathepsin A, and palmitoyl-protein thioesterase 1) were increased in Niemann-Pick C1 protein knockout mouse brain compared to wild-type brain, whereas no difference in endocannabinoid hydrolase activity was observed. We conclude that these targets might be interesting therapeutic targets for future validation studies

First on-line β\beta-NMR on oriented nuclei: magnetic dipole moments of the νp1/2−1;1/2−\nu{p}^{-1}_{1/2}; 1/2^- ground state in 67^{67}Ni and πp3/2+1;3/2−\pi{p}^{+1}_{3/2}; 3/2^- ground state in 69^{69}Cu

The first fully on-line use of the angular distribution of $\beta$ - emission in detection of NMR of nuclei oriented at low temperatures is reported. The magnetic moments of the single valence particle, intermediate mass, isotopes $^{67}$Ni($\nu{p}^{-1}_{1/2}$; 1/2$^{-}$) and $^{69}$Cu($\pi{p}^{+1}_{ 3/2}$; 3/2$^{-}$) are measured to be +0.601(5)$\mu_{N}$ and +2.84(1)$\mu_{N}$ respectively, revealing only a small deviation from the neutron $p_{1/2}$ single-particle value in the former and a large deviation from the proton $p_{3/2}$ single-particle value in the latter. Quantitative interpretation is given in terms of core polarization and meson-exchange currents

A five-year perspective on the situation of haemorrhagic fever with renal syndrome and status of the hantavirus reservoirs in Europe, 2005-2010

Hantavirus infections are reported from many countries in Europe and with highly variable annual case numbers. In 2010, more than 2,000 human cases were reported in Germany, and numbers above the baseline have also been registered in other European countries. Depending on the virus type human infections are characterised by mild to severe forms of haemorrhagic fever with renal syndrome. The member laboratories of the European Network for diagnostics of Imported Viral Diseases present here an overview of the progression of human cases in the period from 2005 to 2010. Further we provide an update on the available diagnostic methods and endemic regions in their countries, with an emphasis on occurring virus types and reservoirs

Two-step activity-based protein profiling of diacylglycerol lipase

Diacylglycerol lipases (DAGL) produce the endocannabinoid 2-arachidonoylglycerol, a key modulator of neurotransmitter release. Chemical tools that visualize endogenous DAGL activity are desired. Here, we report the design, synthesis and application of a triazole urea probe for DAGL equipped with a norbornene as a biorthogonal handle. The activity and selectivity of the probe was assessed with activity-based protein profiling. This probe was potent against endogenous DAGLα (IC50 = 5 nM) and it was successfully applied as a two-step activity-based probe for labeling of DAGLα using an inverse electron-demand Diels–Alder ligation in living cells.Bio-organic SynthesisMolecular Physiolog