Ballistic one-dimensional holes with strong g-factor anisotropy in germanium

We report experimental evidence of ballistic hole transport in one-dimensional quantum wires gate-defined in a strained SiGe/Ge/SiGe quantum well. At zero magnetic field, we observe conductance plateaus at integer multiples of 2e2/h. At finite magnetic field, the splitting of these plateaus by Zeeman effect reveals largely anisotropic g-factors with absolute values below 1 in the quantum-well plane, and exceeding 10 out-of-plane. This g-factor anisotropy is consistent with a heavy-hole character of the propagating valence-band states, which is in line with a predominant confinement in the growth direction. Remarkably, we observe quantized ballistic conductance in device channels up to 600 nm long. These findings mark an important step toward the realization of novel devices for applications in quantum spintronics

Development of Biomarkers Based on Diet-Dependent Metabolic Serotypes: Concerns and Approaches for Cohort and Gender Issues in Serum Metabolome Studies

This is the publisher's version, also available electronically from: http://online.liebertpub.com/doi/pdfplus/10.1089/omi.2004.8.209Mathematical models that reflect the effects of dietary restriction (DR) on the sera metabolome may have utility in understanding the mechanisms of DR and in applying this knowledge to human epidemiological studies. Previous studies demonstrated both the feasibility of identifying biomarkers through metabolome analysis and the validity of our approach in independent cohorts of 6-month-oId male and female ad libitum fed or DR rats. Cross-cohort studies showed that cohort-specific effects distorted the dataset The present study extends these observations across the entire sample set, thereby validating our markers independently of specific cohorts. Metabolites originally identified in males were examined in females and vice-versa. DR's effect on the metabolom e is partially gender-specific and is modulated by environmental factors. DR reduces inter-gender differences in the metabolome. Univariate statistical methods showed that 56/93 metabolites in the female samples and 39/93 metabolites in the male samples were significantly altered (using our previous cut-off criteria of p ^ 0.2) by DR. The metabolites modulated by DR present a wide spectrum of concentration, redox reactivity and hydrophilicity, suggesting that our serotype is broadly representative of the metabolome and that DR has broad effects on the metabolome. These studies, coupled with those in the preceding and following reports, also highlight the utility for consideration of the metabolome as a network of metabolites using appropriate data analysis approaches. The inter-cohort and inter-gender differences addressed herein suggest potential cautions, and potential approaches, for identification of multivariate biomarker profiles that reflect changes in physiological status, such as a metabolism that predisposes to increased risk of neoplasia

Genomic imprinting mechanisms in mammals

Genomic imprinting is a form of epigenetic gene regulation that results in expression from a single allele in a parent-of-origin-dependent manner. This form of monoallelic expression affects a small but growing number of genes and is essential to normal mammalian development. Despite extensive studies and some major breakthroughs regarding this intriguing phenomenon, we have not yet fully characterized the underlying molecular mechanisms of genomic imprinting. This is in part due to the complexity of the system in that the epigenetic markings required for proper imprinting must be established in the germline, maintained throughout development, and then erased before being re-established in the next generation's germline. Furthermore, imprinted gene expression is often tissue or stage-specific. It has also become clear that while imprinted loci across the genome seem to rely consistently on epigenetic markings of DNA methylation and/or histone modifications to discern parental alleles, the regulatory activities underlying these markings vary among loci. Here, we discuss different modes of imprinting regulation in mammals and how perturbations of these systems result in human disease. We focus on the mechanism of genomic imprinting mediated by insulators as is present at the H19/Igf2 locus, and by non-coding RNA present at the Igf2r and Kcnq1 loci. In addition to imprinting mechanisms at autosomal loci, what is known about imprinted X-chromosome inactivation and how it compares to autosomal imprinting is also discussed. Overall, this review summarizes many years of imprinting research, while pointing out exciting new discoveries that further elucidate the mechanism of genomic imprinting, and speculating on areas that require further investigation

CTCF binding site classes exhibit distinct evolutionary, genomic, epigenomic and transcriptomic features

CTCF DNA binding sites are classified into distinct functional classes, with distinct biological properties, shedding light on the differing functional roles of CTCF binding

Development of Biomarkers Based on Diet-Dependent Metabolic Serotypes: Practical Issues in Development of Expert System-Based Classification Models in Metabolomic Studies

This is the publisher's official version, also available electronically from: http://online.liebertpub.com/doi/pdfplus/10.1089/omi.2004.8.197Dietary restriction (DR)-induced changes in the serum metabolome may be biomarkers for physiological status (e.g., relative risk of developing age-related diseases such as cancer). Megavariate analysis (unsupervised hierarchical cluster analysis IHCAJ; principal components analysis [PCAJ) of serum metabolites reproducibly distinguish DR from ad libitum fed rats. Component-based approaches (i.e., PCA) consistently perform as well as or better than distance-based metrics (i.e., HCA). We therefore tested the following: (A) Do identified subsets of serum metabolites contain sufficient information to construct mathematical models of class membership (i.e., expert systems)? (B) Do component-based metrics out-perform distance-based metrics? Testing was conducted using KNN (k-nearest neighbors, supervised HCA) and SIMCA (soft independent modeling of class analogy, supervised PCA). Models were built with single cohorts, combined cohorts or mixed samples from previously studied cohorts as training sets. Both algorithms over-fit models based on single cohort training sets. KNN models had >85% accuracy within training/test sets, but were unstable (i.e., values of k could not be accurately set in advance). SIMCA models had 100% accuracy within all training sets, 89% accuracy in test sets, did not appear to over-fit mixed cohort training sets, and did not require post-hoc modeling adjustments. These data indicate that (i) previously defined metabolites are robust enough to construct classification models (expert systems) with SIMCA that can predict unknowns by dietary category; (ii) component-based analyses outperformed distance-based metrics; (iii) use of over-fitting controls is essential; and (iv) subtle inter-cohort variability may be a critical issue for high data density biomarker studies that lack state markers

Development of Biomarkers Based on Diet-Dependent Metabolic Serotypes: Characteristics of Component-Based Models of Metabolic Serotypes

This is the publisher's version, also available electronically from: http://online.liebertpub.com/doi/pdfplus/10.1089/omi.2004Our research seeks to identify a scrum profile, or serotype, that reflects the systemic physiologic modifications resultant from dietary restriction (DR), in part such that this knowledge can be applied for biomarker studies. Direct comparison suggests that component-based classification algorithms consistently out-perform distance-based metrics for studies of nutritional modulation of metabolic serotype, but are subject to over-fitting concerns. Intercohort differences in the sera metabolome could partially obscure the effects of DR. Further analysis now shows that implementation of component-based approaches (also called projection methods) optimized for class separation and controlled for over-fitting have >97% accuracy for distinguishing sera from control or DR rats. DR's effect on the metabolome is shown to be robust across cohorts, but differs in males and females (although some metabolites are affected in both). We demonstrate the utility of projection-based methods for both sample and variable diagnostics, including identification of critical metabolites and samples that are atypical with respect to both class and variable models. Inclusion of non-statistically different variables enhances classification models. Variables that contribute to these models are sharply dependent on mathematical processing techniques; some variables that do not contribute under one paradigm arc powerful under alternative mathematical paradigms. In practical terms, this information may find purpose in other endeavors, such as mechanistic studies of DR. Application of these approaches confirms the utility of megavariate data analysis techniques for optimal generation of biomarkers based on nutritional modulation of physiological processes

Deep Reinforcement Learning for Efficient Measurement of Quantum Devices

Deep reinforcement learning is an emerging machine learning approach which can teach a computer to learn from their actions and rewards similar to the way humans learn from experience. It offers many advantages in automating decision processes to navigate large parameter spaces. This paper proposes a novel approach to the efficient measurement of quantum devices based on deep reinforcement learning. We focus on double quantum dot devices, demonstrating the fully automatic identification of specific transport features called bias triangles. Measurements targeting these features are difficult to automate, since bias triangles are found in otherwise featureless regions of the parameter space. Our algorithm identifies bias triangles in a mean time of less than 30 minutes, and sometimes as little as 1 minute. This approach, based on dueling deep Q-networks, can be adapted to a broad range of devices and target transport features. This is a crucial demonstration of the utility of deep reinforcement learning for decision making in the measurement and operation of quantum devices

Modeling Sustainability Reporting with Ternary Attractor Neural Networks

International Conference on Mining Intelligence and Knowledge Exploration. Cluj-Napoca, Romania, December 20–22, 2018This work models the Corporate Sustainability General Reporting Initiative (GRI) using a ternary attractor network. A dataset of years evolution of the GRI reports for a world-wide set of companies was compiled from a recent work and adapted to match the pattern coding for a ternary attractor network. We compare the performance of the network with a classical binary attractor network. Two types of criteria were used for encoding the ternary network, i.e., a simple and weighted threshold, and the performance retrieval was better for the latter, highlighting the importance of the real patterns’ transformation to the three-state coding. The network exceeds the retrieval performance of the binary network for the chosen correlated patterns (GRI). Finally, the ternary network was proved to be robust to retrieve the GRI patterns with initial noise.This work has been supported by Spanish grants MINECO (http://www.mineco.gob.es/) TIN2014-54580-R, TIN2017-84452-R, and by UAMSantander CEAL-AL/2017-08, and UDLA-SIS.MG.17.02

Report of the Working Group on Commercial Catches (WGCATCH)

The Working Group on Commercial Catches (WGCATCH), chaired by Hans Gerritsen (Ireland) and Nuno Prista (Sweden), met in Lisbon, Portugal, 9–13 November 2015. WGCATCH is responsible for documenting national fishery sampling schemes, establishing best practice and guidelines on sampling and estimation procedures, and providing advice on other uses of fishery data. The meeting was attended by 30 participants from 15 countries. The group addressed a large number of terms of reference and the meeting was con-ducted through presentations, discussions and analysis of questionnaires. The main terms of reference were addressed in subgroups. The report is structured directly along the terms of reference and the main outcomes are listed below. Data collection schemes for small-scale fisheries WGCATCH provided descriptions of national small-scale fisheries through question-naires. An overview was obtained on the current data collection methods. Two major approaches were identified - census (e.g., sales, logbooks) and sampling methods (e.g., catch surveys) - and their main pros and cons were discussed. In most cases, specific sampling approaches are needed for these fisheries. The group developed a work plan to establish good-practice guidelines. Analysis of case studies of commercial fishery sampling designs and estimation Case studies of sampling designs and estimation involving megrim in divisions 7-8 were presented. A common theme is that issues with practical implementation of prob-ability-based sampling remain. WGCATCH summarized the main issues and provided a set of possible solutions. The group also provided guidance on dealing with previous data collected under métier-based sampling designs. Simulation models to investigate survey designs Several simulation studies were presented, most of them outlining the work of fishPi project (funded under MARE/2014/19) in evaluating regional sampling designs. A crit-ical review was carried out and WGCATCH produced general considerations and guidelines. WGCATCH recommends that these are taken into account when analysing the results of simulations of regional sampling design at RCM level. The affect of the landing obligation on catch sampling opportunities The affects on sampling and data quality of the current implementation of the landing obligation in the Baltic were reviewed. The group found that refusal rates for observer trips have increased to nearly 100% in at least one country, while in many other coun-tries on-board observer programmes did not suffer noticeable changes. WGCATCH established that the catches below the minimum size cannot be accurately estimated by sampling the landings below the minimum size because an unknown proportion of the catches may be discarded. The group also reiterated that it is important that the logbooks distinguish landings below and above the minimum size. Publication on statistically sound sampling schemes WGCATCH drafted detailed plans to produce a peer-reviewed paper in 2016. The pa-per will provide a synthesis of the evolution of sampling design towards best practice, illustrated with a number of concise case studies. Estimation procedures in the Regional Database (RDB) The work of WKRDB 2015 presented alongside existing and planned estimation pro-cedures in the RDB. Current work by Norway on a software package that will allow design-based estimation and optimization for stock assessment purposes was also pre-sented. The advantages of ensuring compatibility of this new software with the devel-opments currently planned for RDB-FishFrame are underscored. Repository of resources relevant to catch sampling WGCATCH initiated a repository with key resources; putting them into context with brief descriptions or review of each report, paper, book, website, software package etc. The intention is for this repository to be made available online by ICES. Sampling of incidental bycatches WGCATCH agreed to start routine documentation of sampling practices for bycatches of protected, endangered and threatened species (PETS) and rare fish species as well as routine evaluation of the limitations of current methods for collection and analysis. Training course on Design and Analysis of Statistical Sound catch sampling pro-grammes WGCATCH considered continuous training and expertise on sampling design, estima-tion and simulation to be the basis for successful implementation of statistical sound catch sampling programs. A new ICES Training Course in Design and Analysis of Sta-tistical Sound will take place at ICES HQ in Copenhagen, 12–16 September 2016. WGCATCH recommends that RCMs promote the attendance of these meetings among all MS involved

Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning

The potential of Si and SiGe-based devices for the scaling of quantum circuits is tainted by device variability. Each device needs to be tuned to operation conditions and each device realisation requires a different tuning protocol. We demonstrate that it is possible to automate the tuning of a 4-gate Si FinFET, a 5-gate GeSi nanowire and a 7-gate Ge/SiGe heterostructure double quantum dot device from scratch with the same algorithm. We achieve tuning times of 30, 10, and 92 min, respectively. The algorithm also provides insight into the parameter space landscape for each of these devices, allowing for the characterization of the regions where double quantum dot regimes are found. These results show that overarching solutions for the tuning of quantum devices are enabled by machine learning