Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Functional interpretation of single cell similarity maps.

We present Vision, a tool for annotating the sources of variation in single cell RNA-seq data in an automated and scalable manner. Vision operates directly on the manifold of cell-cell similarity and employs a flexible annotation approach that can operate either with or without preconceived stratification of the cells into groups or along a continuum. We demonstrate the utility of Vision in several case studies and show that it can derive important sources of cellular variation and link them to experimental meta-data even with relatively homogeneous sets of cells. Vision produces an interactive, low latency and feature rich web-based report that can be easily shared among researchers, thus facilitating data dissemination and collaboration

The varieties of the psychedelic experience: A preliminary study of the association between the reported subjective effects and the binding affinity profiles of substituted phenethylamines and tryptamines

Classic psychedelics are substances of paramount cultural and neuroscientific importance. A distinctive feature of psychedelic drugs is the wide range of potential subjective effects they can elicit, known to be deeply influenced by the internal state of the user (“set”) and the surroundings (“setting”). The observation of cross-tolerance and a series of empirical studies in humans and animal models support agonism at the serotonin (5-HT)2A receptor as a common mechanism for the action of psychedelics. The diversity of subjective effects elicited by different compounds has been attributed to the variables of “set” and “setting,” to the binding affinities for other 5-HT receptor subtypes, and to the heterogeneity of transduction pathways initiated by conformational receptor states as they interact with different ligands (“functional selectivity”). Here we investigate the complementary (i.e., not mutually exclusive) possibility that such variety is also related to the binding affinity for a range of neurotransmitters and monoamine transporters including (but not limited to) 5-HT receptors. Building on two independent binding affinity datasets (compared to “in silico” estimates) in combination with natural language processing tools applied to a large repository of reports of psychedelic experiences (Erowid’s Experience Vaults), we obtained preliminary evidence supporting that the similarity between the binding affinity profiles of psychoactive substituted phenethylamines and tryptamines is correlated with the semantic similarity of the associated reports. We also showed that the highest correlation was achieved by considering the combined binding affinity for the 5-HT, dopamine (DA), glutamate, muscarinic and opioid receptors and for the Ca+ channel. Applying dimensionality reduction techniques to the reports, we linked the compounds, receptors, transporters and the Ca+ channel to distinct fingerprints of the reported subjective effects. To the extent that the existing binding affinity data is based on a low number of displacement curves that requires further replication, our analysis produced preliminary evidence consistent with the involvement of different binding sites in the reported subjective effects elicited by psychedelics. Beyond the study of this particular class of drugs, we provide a methodological framework to explore the relationship between the binding affinity profiles and the reported subjective effects of other psychoactive compounds.Fil: Zamberlan, Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Sanz, Camila. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; ArgentinaFil: Martínez Vivot, Rocío. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Pallavicini, Carla. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; ArgentinaFil: Erowid, Fire. Grass Valley; Estados UnidosFil: Erowid, Earth. Grass Valley; Estados UnidosFil: Tagliazucchi, Enzo Rodolfo. Institut du cerveau et de la moelle épinière; Francia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentin

Relations between Effects and Structure of Small Bicyclic Molecules on the Complex Model System Saccharomyces cerevisiae

The development of compounds able to modify biological functions largely took advantage of parallel synthesis to generate a broad chemical variance of compounds to be tested for the desired effect(s). The budding yeast Saccharomyces cerevisiae is a model for pharmacological studies since a long time as it represents a relatively simple system to explore the relations among chemical variance and bioactivity. To identify relations between the chemical features of the molecules and their activity, we delved into the effects of a library of small compounds on the viability of a set of S. cerevisiae strains. Thanks to the high degree of chemical diversity of the tested compounds and to the measured effect on the yeast growth rate, we were able to scale-down the chemical library and to gain information on the most effective structures at the substituent level. Our results represent a valuable source for the selection, rational design, and optimization of bioactive compounds

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Grapevine phyllosphere community analysis in response to elicitor application against powdery mildew

The reduction of antimicrobial treatments and mainly the application of environmentally friendly compounds, such as resistance elicitors, is an impelling challenge to undertake more sustainable agriculture. We performed this research to study the effectiveness of non-conventional compounds in reducing leaf fungal attack and to investigate whether they influence the grape phyllosphere. Pathogenicity tests were conducted on potted Vitis vinifera “Nebbiolo” and “Moscato” cultivars infected with the powdery mildew agent (Erysiphe necator) and treated with three elicitors. Differences in the foliar microbial community were then evaluated by community-level physiological profiling by using BiologTM EcoPlates, high throughput sequencing of the Internal Transcribed Spacer (ITS) region, and RNA sequencing for the viral community. In both cultivars, all products were effective as they significantly reduced pathogen development. EcoPlate analysis and ITS sequencing showed that the microbial communities were not influenced by the alternative compound application, confirming their specific activity as plant defense elicitors. Nevertheless, “Moscato” plants were less susceptible to the disease and presented different phyllosphere composition, resulting in a richer viral community, when compared with the “Nebbiolo” plants. The observed effect on microbial communities pointed to the existence of distinct genotype-specific defense mechanisms independently of the elicitor application

Characterization of Lignin Structural Variability and the Associated Application In Genome Wide Association Studies

Poplar (Populus sp.) is a promising biofuel feedstock due to advantageous features such as fast growth, the ability to grow on marginal land, and relatively low lignin content. However, there is tremendous variability associated with the composition of biomass. Understanding this variability, especially in lignin, is crucial to developing and implementing financially viable, integrated biorefineries. Although lignin is typically described as being comprised of three primary monolignols (syringyl, guaiacyl, p-hydroxyphenyl), it is a highly irregular biopolymer that can incorporate non-canonical monolignols. It is also connected by a variety of interunit linkages, adding to its complexity. Secondary cell wall formation requires the coordination of many metabolic pathways. Additionally, complex traits such as lignin are highly polygenic. While there are several methods to analyze lignin structure, 2D HSQC NMR is a powerful analytical tool that elucidates many structural traits of lignin simultaneously. This work examined the lignin structure of 409 unique Populus trichocarpa genotypes via HSQC NMR. Twelve lignin phenotypes were subsequently utilized for a genome-wide association study (GWAS) – a powerful approach for identifying loci contributing to natural phenotypic diversity. This deep phenotyping enabled GWAS to identify 756 candidate genes associated with at least one lignin phenotype. Many of these candidate genes have not been previously reported to be associated with lignin or cell wall biosynthesis. These results provide a valuable resource for gaining insights into the molecular mechanisms of lignin biosynthesis and offer new targets for future genetic improvement in poplar