Neural NILM: Deep Neural Networks Applied to Energy Disaggregation

Energy disaggregation estimates appliance-by-appliance electricity consumption from a single meter that measures the whole home's electricity demand. Recently, deep neural networks have driven remarkable improvements in classification performance in neighbouring machine learning fields such as image classification and automatic speech recognition. In this paper, we adapt three deep neural network architectures to energy disaggregation: 1) a form of recurrent neural network called `long short-term memory' (LSTM); 2) denoising autoencoders; and 3) a network which regresses the start time, end time and average power demand of each appliance activation. We use seven metrics to test the performance of these algorithms on real aggregate power data from five appliances. Tests are performed against a house not seen during training and against houses seen during training. We find that all three neural nets achieve better F1 scores (averaged over all five appliances) than either combinatorial optimisation or factorial hidden Markov models and that our neural net algorithms generalise well to an unseen house.Comment: To appear in ACM BuildSys'15, November 4--5, 2015, Seou

Interspecies Interactions Stimulate Diversification of the Streptomyces coelicolor Secreted Metabolome

ABSTRACT Soils host diverse microbial communities that include filamentous actinobacteria (actinomycetes). These bacteria have been a rich source of useful metabolites, including antimicrobials, antifungals, anticancer agents, siderophores, and immunosuppressants. While humans have long exploited these compounds for therapeutic purposes, the role these natural products may play in mediating interactions between actinomycetes has been difficult to ascertain. As an initial step toward understanding these chemical interactions at a systems level, we employed the emerging techniques of nanospray desorption electrospray ionization (NanoDESI) and matrix-assisted laser desorption ionization–time of flight (MALDI-TOF) imaging mass spectrometry to gain a global chemical view of the model bacterium Streptomyces coelicolor interacting with five other actinomycetes. In each interaction, the majority of secreted compounds associated with S. coelicolor colonies were unique, suggesting an idiosyncratic response from S. coelicolor. Spectral networking revealed a family of unknown compounds produced by S. coelicolor during several interactions. These compounds constitute an extended suite of at least 12 different desferrioxamines with acyl side chains of various lengths; their production was triggered by siderophores made by neighboring strains. Taken together, these results illustrate that chemical interactions between actinomycete bacteria exhibit high complexity and specificity and can drive differential secondary metabolite production

D-Amino Acids Indirectly Inhibit Biofilm Formation in Bacillus subtilis by Interfering with Protein Synthesis

The soil bacterium Bacillus subtilis forms biofilms on surfaces and at air-liquid interfaces. It was previously reported that these biofilms disassemble late in their life cycle and that conditioned medium from late-stage biofilms inhibits biofilm formation. Such medium contained a mixture of d-leucine, d-methionine, d-tryptophan, and d-tyrosine and was reported to inhibit biofilm formation via the incorporation of these d-amino acids into the cell wall. Here, we show that l-amino acids were able to specifically reverse the inhibitory effects of their cognate d-amino acids. We also show that d-amino acids inhibited growth and the expression of biofilm matrix genes at concentrations that inhibit biofilm formation. Finally, we report that the strain routinely used to study biofilm formation has a mutation in the gene (dtd) encoding d-tyrosyl-tRNA deacylase, an enzyme that prevents the misincorporation of d-amino acids into protein in B. subtilis. When we repaired the dtd gene, B. subtilis became resistant to the biofilm-inhibitory effects of d-amino acids without losing the ability to incorporate at least one noncanonical d-amino acid, d-tryptophan, into the peptidoglycan peptide side chain. We conclude that the susceptibility of B. subtilis to the biofilm-inhibitory effects of d-amino acids is largely, if not entirely, due to their toxic effects on protein synthesis.Chemistry and Chemical Biolog

Horizontal Gene Acquisitions, Mobile Element Proliferation, and Genome Decay in the Host-Restricted Plant Pathogen \u3ci\u3eErwinia Tracheiphila\u3c/i\u3e

Modern industrial agriculture depends on high-density cultivation of genetically similar crop plants, creating favorable conditions for the emergence of novel pathogens with increased fitness in managed compared with ecologically intact settings. Here, we present the genome sequence of six strains of the cucurbit bacterial wilt pathogen Erwinia tracheiphila (Enterobacteriaceae) isolated from infected squash plants in New York, Pennsylvania, Kentucky, and Michigan. These genomes exhibit a high proportion of recent horizontal gene acquisitions, invasion and remarkable amplification of mobile genetic elements, and pseudogenization of approximately 20% of the coding sequences. These genome attributes indicate that E. tracheiphila recently emerged as a host-restricted pathogen. Furthermore, chromosomal rearrangements associated with phage and transposable element proliferation contribute to substantial differences in gene content and genetic architecture between the six E. tracheiphila strains and other Erwinia species. Together, these data lead us to hypothesize that E. tracheiphila has undergone recent evolution through both genome decay (pseudogenization) and genome expansion (horizontal gene transfer and mobile element amplification). Despite evidence of dramatic genomic changes, the six strains are genetically monomorphic, suggesting a recent population bottleneck and emergence into E. tracheiphila’s current ecological niche

Cytological and transcript analyses reveal fat and lazy persister-like bacilli in tuberculous sputum

As nonreplicating tubercle bacilli are tolerant to the cidal action of antibiotics and resistant to multiple stresses, identification of this persister-like population of tubercle bacilli in sputum presents exciting and tractable new opportunities to investigate both responses to chemotherapy and the transmission of tuberculosis

The Escherichia coli transcriptome mostly consists of independently regulated modules

Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome

Preparation of anti-vicinal amino alcohols: asymmetric synthesis of D-erythro-Sphinganine, (+)-spisulosine and D-ribo-phytosphingosine

Two variations of the Overman rearrangement have been developed for the highly selective synthesis of anti-vicinal amino alcohol natural products. A MOM-ether directed palladium(II)-catalyzed rearrangement of an allylic trichloroacetimidate was used as the key step for the preparation of the protein kinase C inhibitor D-erythro-sphinganine and the antitumor agent (+)-spisulosine, while the Overman rearrangement of chiral allylic trichloroacetimidates generated by asymmetric reduction of an alpha,beta-unsaturated methyl ketone allowed rapid access to both D-ribo-phytosphingosine and L-arabino-phytosphingosine

Ensemble Learning for Low-Level Hardware-Supported Malware Detection

Abstract. Recent work demonstrated hardware-based online malware detection using only low-level features. This detector is envisioned as a first line of defense that prioritizes the application of more expensive and more accurate software detectors. Critical to such a framework is the detection performance of the hardware detector. In this paper, we explore the use of both specialized detectors and ensemble learning tech-niques to improve performance of the hardware detector. The proposed detectors reduce the false positive rate by more than half compared to a single detector, while increasing the detection rate. We also contribute approximate metrics to quantify the detection overhead, and show that the proposed detectors achieve more than 11x reduction in overhead compared to a software only detector (1.87x compared to prior work), while improving detection time. Finally, we characterize the hardware complexity by extending an open core and synthesizing it on an FPGA platform, showing that the overhead is minimal.

Analytic philosophy for biomedical research: the imperative of applying yesterday's timeless messages to today's impasses

The mantra that "the best way to predict the future is to invent it" (attributed to the computer scientist Alan Kay) exemplifies some of the expectations from the technical and innovative sides of biomedical research at present. However, for technical advancements to make real impacts both on patient health and genuine scientific understanding, quite a number of lingering challenges facing the entire spectrum from protein biology all the way to randomized controlled trials should start to be overcome. The proposal in this chapter is that philosophy is essential in this process. By reviewing select examples from the history of science and philosophy, disciplines which were indistinguishable until the mid-nineteenth century, I argue that progress toward the many impasses in biomedicine can be achieved by emphasizing theoretical work (in the true sense of the word 'theory') as a vital foundation for experimental biology. Furthermore, a philosophical biology program that could provide a framework for theoretical investigations is outlined