Using Grouped Linear Prediction and Accelerated Reinforcement Learning for Online Content Caching

Proactive caching is an effective way to alleviate peak-hour traffic congestion by prefetching popular contents at the wireless network edge. To maximize the caching efficiency requires the knowledge of content popularity profile, which however is often unavailable in advance. In this paper, we first propose a new linear prediction model, named grouped linear model (GLM) to estimate the future content requests based on historical data. Unlike many existing works that assumed the static content popularity profile, our model can adapt to the temporal variation of the content popularity in practical systems due to the arrival of new contents and dynamics of user preference. Based on the predicted content requests, we then propose a reinforcement learning approach with model-free acceleration (RLMA) for online cache replacement by taking into account both the cache hits and replacement cost. This approach accelerates the learning process in non-stationary environment by generating imaginary samples for Q-value updates. Numerical results based on real-world traces show that the proposed prediction and learning based online caching policy outperform all considered existing schemes.Comment: 6 pages, 4 figures, ICC 2018 worksho

MQA: Answering the Question via Robotic Manipulation

In this paper, we propose a novel task -- Manipulation Question Answering (MQA), where the robot is required to find the answer to the question by actively exploring the environment via manipulation. A framework consisting of a QA model and a manipulation model is proposed to solve this problem. For the QA model, we adopt the method of Visual Question Answering (VQA). For the manipulation model, a Deep Q Network (DQN) model is proposed to generate manipulations. By manipulating objects, the robot can continuously explore the bin until the answer to the question is found. Besides, a novel dataset for simulation that contains a variety of object models, complicated scenarios and corresponding question-answer pairs is established. Extensive experiments have been conducted to validate the effectiveness of the proposed framework

Infrared Spectroscopy Coupled with a Dispersion Model for Quantifying the Real-Time Dynamics of Kanamycin Resistance in Artificial Microbiota

Overusage of antibiotics leads to the widespread induction of antibiotic-resistance genes (ARGs). Developing an approach to allow real-time monitoring and fast prediction of ARGs dynamics in clinical or environmental samples has become an urgent matter. Vibrational spectroscopy is potentially an ideal technique toward the characterization of the microbial composition of microbiota as it is nondestructive, high-throughput, and label-free. Herein, we employed attenuated total reflection Fourier transform infrared (ATR-FT-IR) spectroscopy and developed a spectrochemical tool to quantify the static and dynamic composition of kanamycin resistance in artificial microbiota to evaluate microbial antibiotic resistance. Second-order differentiation was introduced in identifying the spectral biomarkers, and principal component analysis followed by linear discriminant analysis (PCA-LDA) was used for the multivariate analysis of the entire spectral features employed. The calculated results of the mathematical dispersion model coupled with PCA-LDA showed high similarity to the designed microbiota structure, with no significant difference (P > 0.05) in the static treatments. Moreover, our model successfully predicted the dynamics of kanamycin resistance within artificial microbiota under kanamycin pressures. This work lends new insights into the potential role of spectrochemical analyses in investigating the existence and trends of antibiotic resistance in microbiota

Critical review on unraveling uncultivable pesticide degraders via stable isotope probing (SIP)

Uncultivable microorganisms account for over 99% of all species on earth, playing essential roles in ecological processes such as carbon/nitrogen cycle and chemical mineralization. Their functions remain unclear in ecosystems and natural habitats, requiring cutting-edge biotechnologies for a deeper understanding. Stable isotope probing (SIP) incorporates isotope-labeled elements, e.g. 13 C, 18 O or 15 N, into the cellular components of active microorganisms, serving as a powerful tool to link phylogenetic identities to their ecological functions in situ. Pesticides raise increasing attention for their persistence in the environment, leading to severe damage and risks to the ecosystem and human health. Cultivation and metagenomics help to identify either cultivable pesticide degraders or potential pesticide metabolisms within microbial communities, from various environmental media including the soil, groundwater, activated sludge, plant rhizosphere, etc. However, the application of SIP in characterizing pesticide degraders is limited, leaving considerable space in understanding the natural pesticide mineralization process. In this review, we try to comprehensively summarize the fundamental principles, successful cases and technical protocols of SIP in unraveling functional-yet-uncultivable pesticide degraders, by raising its shining lights and shadows. Particularly, this study provides deeper insights into various feasible isotope-labeled substrates in SIP studies, including pesticides, pesticide metabolites, and similar compounds. Coupled with other techniques, such as next-generation sequencing, nanoscale secondary ion mass spectrometry (NanoSIMS), single cell genomics, magnetic-nanoparticle-mediated isolation (MMI) and compound-specific isotope analysis (CSIA), SIP will significantly broaden our understanding of pesticide biodegradation process in situ

Spectrochemical determination of unique bacterial responses following long-term low-level exposure to antimicrobials

Agents arising from engineering or pharmaceutical industries may have significant environmental impacts. Particularly, antimicrobials not only act as efficient eliminators of certain microbes but also facilitate the propagation of organisms with antimicrobial resistance, giving rise to critical health issues, e.g., the bloom of multidrug-resistant bacteria. Although many investigations have examined microbial responses to antimicrobials and characterized relevant mechanisms, they have focused mainly on high-level and short-term exposures, instead of simulating real-world scenarios in which the antimicrobial exposure is at a low-level for long periods. Herein, we developed a spectrochemical tool, attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy, as a high-throughput and nondestructive approach to interrogate the long-term effects of low-level antimicrobial exposure in bacterial cells. Post-exposure to nanoparticulate silver (AgNP), tetracycline or their mixtures for 12 days, Gram-positive (Mycobacterium vanbaalenii PYR-1) and Gram-negative (Pseudomonas fluorescens) bacteria exhibited distinct IR spectral alterations. Multivariate analysis coupled with multivariate regression trees (MRT) indicates nutrient depletion and exposure time as the primary factors in bacterial behaviour, followed by exposure category and bacterial type. Nutrient depletion and starvation during long-term exposure drives bacterial cells into a dormant state or to exhibit additional cellular components (e.g., fatty acids) in response to antimicrobials, consequently causing a broader range of spectral alterations compared to short-term exposure. This work is the first report highlighting the more important roles of exposure duration and nutrient depletion, instead of treatment regimens of antimicrobials, in microbial responses to low-level and prolonged environmental exposures

Spectrochemical analyses of growth phase-related bacterial responses to low (environmentally-relevant) concentrations of tetracycline and nanoparticulate silver

Exposure to environmental insults generally occurs at low levels, making it challenging to measure bacterial responses to such interactions. Additionally, microbial behaviour and phenotype varies in differing bacterial types or growth phases, likely giving rise to growth- or species-specific responses to environmental stimuli. The present study applied a spectrochemical tool, infrared (IR) spectral interrogation coupled with multivariate analysis, to investigate the growth- and species-specific responses of two bacterial strains, Gram-negative Pseudomonas fluorescens and Gram-positive Mycobacterium vanbaalenii, to low concentrations of tetracycline, nanoparticulate silver (AgNP) or mixtures thereof. Results indicate the tendency for tetracycline-induced biospectral alterations to occur in outer-cellular components, e.g., phospholipids or proteins, while AgNPs-induced changes are mainly associated with proteins (∼964 cm−1, ∼1485 cm−1, ∼1550 cm−1, ∼1650 cm−1). The primary altered targets are correlated with bacterial membranes or outer-cellular components. Furthermore, significant lipid changes at 1705–1750 cm−1 were only present in P. fluorescens cells compared to M. vanbaalenii, owing to differences in cell wall structure between Gram-positive and -negative bacteria. This study also found distinct biospectral alterations in non-log phase compared to log phase, confirming bacterial growth-dependent responses to environmental exposures. It implies that previous studies on log phase only may underestimate the impacts from exposures of interest in situ, where bacteria stay in different growth stages. Our work proves the feasibility of biospectroscopy in determining bacterial responses to low-level environmental exposures in a fast and efficient manner, revealing sufficient biochemical information continuously through growth phases. As a nondestructive approach, biospectroscopy may provide deeper insights into the actual and in situ interactions between microbes and environmental stimuli, regardless of the exposure level, growth phase, or bacterial types

Interrogating cadmium and lead biosorption mechanisms by Simplicillium chinense via infrared spectroscopy

Fungi-associated phytoremediation is an environmentally friendly and cost-efficient approach to remove potential toxic elements (PTEs) from contaminated soils. Many fungal strains have been reported to possess PTE-biosorption behaviour which benefits phytoremediation performance. Nevertheless, most studies are limited in rich or defined medium, far away from the real-world scenarios where nutrients are deficient. Understanding fungal PTE-biosorption performance and influential factors in soil environment can expand their application potential and is urgently needed. This study applied attenuated total reflection Fourier-transform infrared (ATR-FTIR) coupled with phenotypic microarrays to study the biospectral alterations of a fungal strain Simplicillium chinense QD10 and explore the mechanisms of Cd and Pb biosorption. Both Cd and Pb were efficiently adsorbed by S. chinense QD10 cultivated with 48 different carbon sources and the biosorption efficiency achieved >90%. As the first study using spectroscopic tools to analyse PTE-biosorption by fungal cells in a high-throughput manner, our results indicated that spectral biomarkers associated with phosphor-lipids and proteins (1745 cm−1, 1456 cm−1 and 1396 cm−1) were significantly correlated with Cd biosorption, suggesting the cell wall components of S. chinense QD10 as the primary interactive targets. In contrast, there was no any spectral biomarker associated with Pb biosorption. Addtionally, adsorption isotherms evidenced a Langmuir model for Cd biosorption but a Freundlich model for Pb biosorption. Accordingly, Pb and Cd biosorption by S. chinense QD10 followed discriminating mechanisms, specific adsorption on cell membrane for Cd and unspecific extracellular precipitation for Pb. This work lends new insights into the mechanisms of PTE-biosorption via IR spectrochemical tools, which provide more comprehensive clues for biosorption behaviour with a nondestructive and high-throughput manner solving the traditional technical barrier regarding the real-world scenarios

Fingerprinting microbiomes towards screening for microbial antibiotic resistance

There is an increasing need to investigate microbiomes in their entirety in a variety of contexts ranging from environmental to human health scenarios. This requirement is becoming increasingly important with emergence of antibiotic resistance. In general, more conventional approaches are too expensive and/or time-consuming and often predicated on prior knowledge of the microorganisms one wishes to study. Herein, we propose the use of biospectroscopy tools as relatively high-throughput, non-destructive approaches to profile microbiomes under study. Fourier-transform infrared (FTIR) or Raman spectroscopy both generate fingerprint spectra of biological material and such spectra can readily be subsequently classed according to biochemical changes in the microbiota, such as emergence of antibiotic resistance. FTIR spectroscopy techniques generally can only be applied to desiccated material whereas Raman approaches can be applied to more hydrated samples. The ability to readily fingerprint microbiomes could lend itself to new approaches in determining microbial behaviours and emergence of antibiotic resistance