Nanophotonic reservoir computing with photonic crystal cavities to generate periodic patterns

Reservoir computing (RC) is a technique in machine learning inspired by neural systems. RC has been used successfully to solve complex problems such as signal classification and signal generation. These systems are mainly implemented in software, and thereby they are limited in speed and power efficiency. Several optical and optoelectronic implementations have been demonstrated, in which the system has signals with an amplitude and phase. It is proven that these enrich the dynamics of the system, which is beneficial for the performance. In this paper, we introduce a novel optical architecture based on nanophotonic crystal cavities. This allows us to integrate many neurons on one chip, which, compared with other photonic solutions, closest resembles a classical neural network. Furthermore, the components are passive, which simplifies the design and reduces the power consumption. To assess the performance of this network, we train a photonic network to generate periodic patterns, using an alternative online learning rule called first-order reduced and corrected error. For this, we first train a classical hyperbolic tangent reservoir, but then we vary some of the properties to incorporate typical aspects of a photonics reservoir, such as the use of continuous-time versus discrete-time signals and the use of complex-valued versus real-valued signals. Then, the nanophotonic reservoir is simulated and we explore the role of relevant parameters such as the topology, the phases between the resonators, the number of nodes that are biased and the delay between the resonators. It is important that these parameters are chosen such that no strong self-oscillations occur. Finally, our results show that for a signal generation task a complex-valued, continuous-time nanophotonic reservoir outperforms a classical (i.e., discrete-time, real-valued) leaky hyperbolic tangent reservoir (normalized root-mean-square errors = 0.030 versus NRMSE = 0.127)

Advances in photonic reservoir computing on an integrated platform

Reservoir computing is a recent approach from the fields of machine learning and artificial neural networks to solve a broad class of complex classification and recognition problems such as speech and image recognition. As is typical for methods from these fields, it involves systems that were trained based on examples, instead of using an algorithmic approach. It originated as a new training technique for recurrent neural networks where the network is split in a reservoir that does the `computation' and a simple readout function. This technique has been among the state-of-the-art. So far implementations have been mainly software based, but a hardware implementation offers the promise of being low-power and fast. We previously demonstrated with simulations that a network of coupled semiconductor optical amplifiers could also be used for this purpose on a simple classification task. This paper discusses two new developments. First of all, we identified the delay in between the nodes as the most important design parameter using an amplifier reservoir on an isolated digit recognition task and show that when optimized and combined with coherence it even yields better results than classical hyperbolic tangent reservoirs. Second we will discuss the recent advances in photonic reservoir computing with the use of resonator structures such as photonic crystal cavities and ring resonators. Using a network of resonators, feedback of the output to the network, and an appropriate learning rule, periodic signals can be generated in the optical domain. With the right parameters, these resonant structures can also exhibit spiking behaviour

Transcriptional integration of paternal and maternal factors in the Arabidopsis zygote

In many plants, the asymmetric division of the zygote sets up the apical-basal axis of the embryo. Unlike animals, plant zygotes are transcriptionally active, implying that plants have evolved specific mechanisms to control transcriptional activation of patterning genes in the zygote. In Arabidopsis, two pathways have been found to regulate zygote asymmetry: YODA (YDA) mitogen-activated protein kinase (MAPK) signaling, which is potentiated by sperm-delivered mRNA of the SHORT SUSPENSOR (SSP) membrane protein, and up-regulation of the patterning gene WOX8 by the WRKY2 transcription factor. How SSP/YDA signaling is transduced into the nucleus and how these pathways are integrated have remained elusive. Here we show that paternal SSP/YDA signaling directly phosphorylates WRKY2, which in turn leads to the up-regulation of WOX8 transcription in the zygote. We further discovered the transcription factors HOMEODOMAIN GLABROUS11/12 (HDG11/12) as maternal regulators of zygote asymmetry that also directly regulate WOX8 transcription. Our results reveal a framework of how maternal and paternal factors are integrated in the zygote to regulate embryo patterning

Estimating the Burden of Medically Attended Norovirus Gastroenteritis: Modeling Linked Primary Care and Hospitalization Datasets.

Background: Norovirus is the leading cause of community-acquired and nosocomial acute gastroenteritis. Routine testing for norovirus is seldom undertaken, and diagnosis is mainly based on presenting symptoms. This makes understanding the burden of medically attended norovirus-attributable gastroenteritis (MA-NGE) and targeting care and prevention strategies challenging. Methods: We used linked population-based healthcare datasets (Clinical Practice Research Datalink General Practice OnLine Database linked with Hospital Episode Statistics Admitted Patient Care) to model the incidence of MA-NGE associated with primary care consultations or hospitalizations according to age groups in England in the period July 2007-June 2013. Results: Mean annual incidence rates of MA-NGE were 4.9/1000 person-years and 0.7/1000 person-years for episodes involving primary care or hospitalizations, respectively. Incidence rates were highest in children aged 65 years (1.7/1000 person-years). Conclusions: In this particular study, the burden of MA-NGE estimated from healthcare datasets was higher than previously estimated in small cohort studies in England. Routinely collected primary care and hospitalization datasets are useful resources to estimate and monitor the burden of MA-NGE in a population over time

Association between visual function response and reduction of inflammation in noninfectious uveitis of the posterior segment

PURPOSE. To examine the association between visual function response (VFR) and inflammation reduction in active noninfectious uveitis of the posterior segment (NIU-PS). METHODS. Phase 3 SAKURA Study 1 randomized 347 subjects in a double-masked fashion to receive injections of intravitreal sirolimus 44 mu g (n = 117); 440 mu g (n = 114); or 880 mu g (n = 116) every other month. Vitreous haze (VH) response, a measure of inflammation reduction, was defined as a VH score of 0 or 0.5+ at month 5 based on the modified Standardized Uveitis Nomenclature Scale. Visual function was assessed with best-corrected visual acuity (BCVA) and the National Eye Institute (NEI) Visual Function Questionnaire-25 (VFQ-25). In this post-hoc analysis, principal component analysis was used to reduce the information in the multidimensional visual function outcome to a restricted number of independently relevant VFR measures. Minimal clinically important differences (MCID) for the VFQ-25-derived components were based on the standard error of measurements. Overall VFR was defined as either a BCVA improvement of >= 2 lines, or an improvement exceeding the MCID in the VFQ-25 based visual function measures. RESULTS. The VFQ-25 composite score (VFQCS) and mental health subscale score (VFQMHS) were retained as relevant VFRs, with MCIDs of 4.3 and 11.7 points, respectively. A vitreous haze response was significantly associated with each VFR measure: VFQCS (odds ratio [ OR] = 2.23; P = 0.0004); VFQMHS (OR = 2.84; P < 0.0001); BCVA (OR = 2.60; P = 0.0009), and overall VFR (OR = 2.65; P < 0.0001). CONCLUSIONS. Inflammation reduction to a VH score of 0 or 0.5+ was significantly associated with improved visual function. Achieving a VH response of 0 or 0.5+ is a patient-relevant outcome

Network-based identification of adaptive pathways in evolved ethanol-tolerant bacterial populations

Efficient production of ethanol for use as a renewable fuel requires organisms with a high level of ethanol tolerance. However, this trait is complex and increased tolerance therefore requires mutations in multiple genes and pathways. Here, we use experimental evolution for a system-level analysis of adaptation of Escherichia coli to high ethanol stress. As adaptation to extreme stress often results in complex mutational data sets consisting of both causal and noncausal passenger mutations, identifying the true adaptive mutations in these settings is not trivial. Therefore, we developed a novel method named IAMBEE (Identification of Adaptive Mutations in Bacterial Evolution Experiments). IAMBEE exploits the temporal profile of the acquisition of mutations during evolution in combination with the functional implications of each mutation at the protein level. These data are mapped to a genome-wide interaction network to search for adaptive mutations at the level of pathways. The 16 evolved populations in our data set together harbored 2,286 mutated genes with 4,470 unique mutations. Analysis by IAMBEE significantly reduced this number and resulted in identification of 90 mutated genes and 345 unique mutations that are most likely to be adaptive. Moreover, IAMBEE not only enabled the identification of previously known pathways involved in ethanol tolerance, but also identified novel systems such as the AcrAB-TolC efflux pump and fatty acids biosynthesis and even allowed to gain insight into the temporal profile of adaptation to ethanol stress. Furthermore, this method offers a solid framework for identifying the molecular underpinnings of other complex traits as well

Use of Tracers and Isotopes to Evaluate Vulnerability of Water in Domestic Wells to Septic Waste

In Nebraska, a large number (\u3e200) of shallow sand-point and cased wells completed in coarse alluvial sediments along rivers and lakes still are used to obtain drinking water for human consumption, even though construction of sand-point wells for consumptive uses has been banned since 1987. The quality of water from shallow domestic wells potentially vulnerable to seepage from septic systems was evaluated by analyzing for the presence of tracers and multiple isotopes. Samples were collected from 26 sand-point and perforated, cased domestic wells and were analyzed for bacteria, coliphages, nitrogen species, nitrogen and boron isotopes, dissolved organic carbon (DOC), prescription and nonprescription drugs, or organic waste water contaminants. At least 13 of the 26 domestic well samples showed some evidence of septic system effects based on the results of several tracers including DOC, coliphages, NH4+, NO3–, N2, δ15N[NO3–] and boron isotopes, and antibiotics and other drugs. Sand-point wells within 30 m of a septic system and \u3c14 m deep in a shallow, thin aquifer had the most tracers detected and the highest values, indicating the greatest vulnerability to contamination from septic waste

Report on methods of safety signal generation in paediatrics from pharmacovigilance databases

This deliverable is based on the need to develop and test methods for safety signal detection in children. Signal detection is the mainstay of detecting safety issues, but so far very few groups have specifically looked at children. We developed reference sets for positive and negative drugevent combinations and vaccine-event combinations by a systematic literature review on all combinations. We retrieved the FDA AERS database, the CDC VAERS database and EUDRAVIGILANCE database. In order to analyse the datasets we had a stepwise approach from extraction of data, cleaning (e.g. mapping MedDRA and ATC codes) and transformation into a a common data model that we defined for the spontaneous reporting databases. A statistical analysis plan was created for the testing of methods and we provided some descriptive analyses of the FAERS data. Next steps will be to complete the analyses

Treatment effects on brain activity during a working memory task in obstructive sleep apnea

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73986/1/j.1365-2869.2009.00755.x.pd