Adaptation to lowest density regions with application to support recovery

A scheme for locally adaptive bandwidth selection is proposed which sensitively shrinks the bandwidth of a kernel estimator at lowest density regions such as the support boundary which are unknown to the statistician. In case of a H\"{o}lder continuous density, this locally minimax-optimal bandwidth is shown to be smaller than the usual rate, even in case of homogeneous smoothness. Some new type of risk bound with respect to a density-dependent standardized loss of this estimator is established. This bound is fully nonasymptotic and allows to deduce convergence rates at lowest density regions that can be substantially faster than $n^{-1/2}$. It is complemented by a weighted minimax lower bound which splits into two regimes depending on the value of the density. The new estimator adapts into the second regime, and it is shown that simultaneous adaptation into the fastest regime is not possible in principle as long as the H\"{o}lder exponent is unknown. Consequences on plug-in rules for support recovery are worked out in detail. In contrast to those with classical density estimators, the plug-in rules based on the new construction are minimax-optimal, up to some logarithmic factor.Comment: Published at http://dx.doi.org/10.1214/15-AOS1366 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Optimal Detection for Diffusion-Based Molecular Timing Channels

This work studies optimal detection for communication over diffusion-based molecular timing (DBMT) channels. The transmitter simultaneously releases multiple information particles, where the information is encoded in the time of release. The receiver decodes the transmitted information based on the random time of arrival of the information particles, which is modeled as an additive noise channel. For a DBMT channel without flow, this noise follows the L\'evy distribution. Under this channel model, the maximum-likelihood (ML) detector is derived and shown to have high computational complexity. It is also shown that under ML detection, releasing multiple particles improves performance, while for any additive channel with $\alpha$-stable noise where $\alpha<1$ (such as the DBMT channel), under linear processing at the receiver, releasing multiple particles degrades performance relative to releasing a single particle. Hence, a new low-complexity detector, which is based on the first arrival (FA) among all the transmitted particles, is proposed. It is shown that for a small number of released particles, the performance of the FA detector is very close to that of the ML detector. On the other hand, error exponent analysis shows that the performance of the two detectors differ when the number of released particles is large.Comment: 16 pages, 9 figures. Submitted for publicatio

Machine learning based activity recognition to identify wasteful activities in production

Lean Management focusses on the elimination of wasteful activities in production. Whilst numerous methods such as value stream analysis or spaghetti diagrams exist to identify transport, inventory, defects, overproduction or waiting, the waste of human motion is difficult to detect. Activity recognition attempts to categorize human activities using sensor data. Human activity recognition (HAR) is already used in the consumer domain to detect human activities such as walking, climbing stairs or running. This paper presents an approach to transfer the human activity recognition methods to production in order to detect wasteful motion in production processes and to evaluate workplaces. Using sensor data from ordinary smartphones, long-term short-term memory networks (LSTM) are used to classify human activities. Additional to the LSTM-network, the paper contributes a labeled data set for supervised learning. The paper demonstrates how activity recognition can be included in learning factory training starting from the generation of training data to the analysis of the results

Systems biology of industrial oxytetracycline production in Streptomyces rimosus: the secrets of a mutagenized hyperproducer

Background Oxytetracycline which is derived from Streptomyces rimosus, inhibits a wide range of bacteria and is industrially important. The underlying biosynthetic processes are complex and hinder rational engineering, so industrial manufacturing currently relies on classical mutants for production. While the biochemistry underlying oxytetracycline synthesis is known to involve polyketide synthase, hyperproducing strains of S. rimosus have not been extensively studied, limiting our knowledge on fundamental mechanisms that drive production. Results In this study, a multiomics analysis of S. rimosus is performed and wild-type and hyperproducing strains are compared. Insights into the metabolic and regulatory networks driving oxytetracycline formation were obtained. The overproducer exhibited increased acetyl-CoA and malonyl CoA supply, upregulated oxytetracycline biosynthe‑ sis, reduced competing byproduct formation, and streamlined morphology. These features were used to synthesize bhimamycin, an antibiotic, and a novel microbial chassis strain was created. A cluster deletion derivative showed enhanced bhimamycin production. Conclusions This study suggests that the precursor supply should be globally increased to further increase the expression of the oxytetracycline cluster while maintaining the natural cluster sequence. The mutagenized hyper‑ producer S. rimosus HP126 exhibited numerous mutations, including large genomic rearrangements, due to natural genetic instability, and single nucleotide changes. More complex mutations were found than those typically observed in mutagenized bacteria, impacting gene expression, and complicating rational engineering. Overall, the approach revealed key traits infuencing oxytetracycline production in S. rimosus, suggesting that similar studies for other antibi‑ otics could uncover general mechanisms to improve production

A regularity class for the roots of nonnegative functions

We investigate the regularity of the positive roots of a non-negative function of one-variable. A modified H\"older space $\mathcal{F}^\beta$ is introduced such that if $f\in \mathcal{F}^\beta$ then $f^\alpha \in C^{\alpha \beta}$. This provides sufficient conditions to overcome the usual limitation in the square root case ($\alpha = 1/2$) for H\"older functions that $f^{1/2}$ need be no more than $C^1$ in general. We also derive bounds on the wavelet coefficients of $f^\alpha$, which provide a finer understanding of its local regularity.Comment: 12 page

The ArcA regulon and oxidative stress resistance in Haemophilus influenzae

Haemophilus influenzae transits between niches within the human host that are predicted to differ in oxygen levels. The ArcAB two-component signal transduction system controls gene expression in response to respiratory conditions of growth and has been implicated in bacterial pathogenesis, yet the mechanism is not understood. We undertook a genome-scale study to identify genes of the H. influenzae ArcA regulon. Deletion of arcA resulted in increased anaerobic expression of genes of the respiratory chain and of H. influenzae's partial tricarboxylic acid cycle, and decreased anaerobic expression levels of genes of polyamine metabolism, and iron sequestration. Deletion of arcA also conferred a susceptibility to transient exposure to hydrogen peroxide that was greater following anaerobic growth than after aerobic growth. Array data revealed that the dps gene, not previously assigned to the ArcA modulon in bacteria, exhibited decreased expression in the arcA mutant. Deletion of dps resulted in hydrogen peroxide sensitivity and complementation restored resistance, providing insight into the previously uncharacterized mechanism of arcA-mediated H2O2 resistance. The results indicate a role for H. influenzae arcA and dps in pre-emptive defence against transitions from growth in low oxygen environments to aerobic exposure to hydrogen peroxide, an antibacterial oxidant produced by phagocytes during infection

Signal molecules and regulatory components in the Rhizobium-legume symbiosis.

Priefer UB, Patschkowski T, Schlüter A. Signal molecules and regulatory components in the Rhizobium-legume symbiosis. Endocytobiosis and Cell Research. 1998;12(3):201-202

Comprehensive metabolite profiling of Sinorhizobium meliloti using gas chromatography-mass spectrometry.

Barsch A, Patschkowski T, Niehaus K. Comprehensive metabolite profiling of Sinorhizobium meliloti using gas chromatography-mass spectrometry. Funct Integr Genomics. 2004;4(4):219-230.A metabolite analysis of the soil bacterium Sinorhizobium meliloti was established as a first step towards a better understanding of the symbiosis with its host plant Medicago truncatula. A crucial step was the development of fast harvesting and extraction methods for the bacterial metabolites because of rapid changes in their composition. S. meliloti 1021 cell cultures grown in minimal medium were harvested by centrifugation, filtration or immediate freezing in liquid nitrogen followed by a lyophilisation step. Bacteria were lysed mechanically in methanol and hydrophilic compounds were analysed after methoxymation and silylisation via GC-MS. The different compounds were identified by comparison with the NIST 98 database and available standards. From about 200 peaks in each chromatogram 65 compounds have been identified so far. A comparison of the different extraction methods giving the metabolite composition revealed clear changes in several amino acids and amino acid precursor pools. A principal component analysis (PCA) was able to distinguish S. meliloti cells grown on different carbon sources based on their metabolite profile. A comparison of the metabolite composition of a S. meliloti leucine auxotrophic mutant with the wild type revealed a marked accumulation of 2-isopropylmalate in the mutant. Interestingly, the accumulated metabolite is not the direct substrate of the mutated enzyme, 3-isopropylmalate dehydrogenase, but the substrate of isopropylmalate isomerase, which acts one step further upstream in the biosynthetic pathway of leucine. This finding further emphasises the importance of integrating metabolic data into post-genomic research