Parsing Sage and Rosemary in Time: The Machine Learning Race to Crack Olfactory Perception

2021 ◽  
Author(s):  
Richard C Gerkin
Keyword(s):  
Molecular Structure ◽  
Predictive Modeling ◽  
Olfactory Perception ◽  
Sound Waves ◽  
Chemical Senses ◽  
Modeling Framework ◽  
Molecular Features ◽  
Wide Range ◽  
Odor Character ◽  
Physical Stimuli

Abstract Color and pitch perception are largely understandable from characteristics of a physical stimuli: the wavelengths of light and sound waves, respectively. By contrast, understanding olfactory percepts from odorous stimuli (volatile molecules) is much more challenging. No intuitive set of molecular features is up to the task. Here in Chemical Senses, the Ray lab reports using a predictive modeling framework--first breaking molecular structure into thousands of features and then using this to train a predictive statistical model on a wide range of perceptual descriptors--to create a tool for predicting the odor character of hundreds of thousands of available but previously uncharacterized molecules (Kowalewski, Huynh and Ray, 2021). This will allow future investigators to representatively sample the space of odorous molecules as well as identify previously unknown odorants with a target odor character. Here I put this work into the context of other modeling efforts and highlight the urgent need for large new datasets and transparent benchmarks for the field to make and evaluate modeling breakthroughs, respectively.

scholarly journals Bilayer ventilated labyrinthine metasurfaces with high sound absorption and tunable bandwidth

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jiayuan Du ◽  
Yuezhou Luo ◽  
Xinyu Zhao ◽  
Xiaodong Sun ◽  
Yanan Song ◽  
...  
Keyword(s):  
Sound Absorption ◽  
Air Flow ◽  
Single Layer ◽  
Sound Waves ◽  
Acoustic Metamaterials ◽  
Sound Barrier ◽  
Free Air ◽  
Relative Bandwidth ◽  
Wide Range ◽  
High Sound

AbstractThe recent advent of acoustic metamaterials offers unprecedented opportunities for sound controlling in various occasions, whereas it remains a challenge to attain broadband high sound absorption and free air flow simultaneously. Here, we demonstrated, both theoretically and experimentally, that this problem can be overcome by using a bilayer ventilated labyrinthine metasurface. By altering the spacing between two constituent single-layer metasurfaces and adopting asymmetric losses in them, near-perfect (98.6%) absorption is achieved at resonant frequency for sound waves incident from the front. The relative bandwidth of absorption peak can be tuned in a wide range (from 12% to 80%) by adjusting the open area ratio of the structure. For sound waves from the back, the bilayer metasurface still serves as a sound barrier with low transmission. Our results present a strategy to realize high sound absorption and free air flow simultaneously, and could find applications in building acoustics and noise remediation.

scholarly journals EXPRESS: Overcoming the Cold Start Problem of CRM using a Probabilistic Machine Learning Approach

2021 ◽  
pp. 002224372110329
Author(s):  
Nicolas Padilla ◽  
Eva Ascarza
Keyword(s):  
Machine Learning ◽  
Cold Start ◽  
Customer Relationship ◽  
Exponential Families ◽  
Modeling Framework ◽  
Wide Range ◽  
The Moment ◽  
Probabilistic Machine Learning ◽  
Marketing Actions ◽  
Cold Start Problem

The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to identify and leverage differences across customers — a very diffcult task when firms attempt to manage new customers, for whom only the first purchase has been observed. For those customers, the lack of repeated observations poses a structural challenge to inferring unobserved differences across them. This is what we call the “cold start” problem of CRM, whereby companies have difficulties leveraging existing data when they attempt to make inferences about customers at the beginning of their relationship. We propose a solution to the cold start problem by developing a probabilistic machine learning modeling framework that leverages the information collected at the moment of acquisition. The main aspect of the model is that it exibly captures latent dimensions that govern the behaviors observed at acquisition as well as future propensities to buy and to respond to marketing actions using deep exponential families. The model can be integrated with a variety of demand specifications and is exible enough to capture a wide range of heterogeneity structures. We validate our approach in a retail context and empirically demonstrate the model's ability at identifying high-value customers as well as those most sensitive to marketing actions, right after their first purchase.

Modeling framework for IoT integration of building automation systems

2017 ◽  
Vol 65 (9) ◽  
Author(s):  
Daniel Schachinger ◽  
Andreas Fernbach ◽  
Wolfgang Kastner
Keyword(s):  
Web Services ◽  
Web Service ◽  
Building Automation ◽  
Modeling Framework ◽  
Seamless Integration ◽  
Automation Systems ◽  
Wide Range ◽  
Machine To Machine Communication ◽  
Machine Communication ◽  
The Internet Of Things

AbstractAdvancements within the Internet of Things are leading to a pervasive integration of different domains including also building automation systems. As a result, device functionality becomes available to a wide range of applications and users outside of the building automation domain. In this context, Web services are identified as suitable solution for machine-to-machine communication. However, a major requirement to provide necessary interoperability is the consideration of underlying semantics. Thus, this work presents a universal framework for tag-based semantic modeling and seamless integration of building automation systems via Web service-based technologies. Using the example of the KNX Web services specification, the applicability of this approach is pointed out.

scholarly journals The molecular structure of plant sporopollenin

2018 ◽  
Author(s):  
Fu-Shuang Li ◽  
Pyae Phyo ◽  
Joseph Jacobowitz ◽  
Mei Hong ◽  
Jing-Ke Weng
Keyword(s):  
Molecular Structure ◽  
Minor Component ◽  
Pollen Grains ◽  
Outer Wall ◽  
Land Plant ◽  
Spores And Pollen ◽  
Biomimetic Polymers ◽  
Wide Range ◽  
Nmr Techniques ◽  
A Minor

Sporopollenin is a ubiquitous and extremely chemically inert biopolymer that constitutes the outer wall of all land-plant spores and pollen grains. Sporopollenin protects the vulnerable plant gametes against a wide range of environmental assaults, and is considered as a prerequisite for the migration of early plants onto land. Despite its importance, the chemical structure of plant sporopollenin has remained elusive. Using a newly developed thioacidolysis degradative method together with state-of-the-art solid-state NMR techniques, we determined the detailed molecular structure of pine sporopollenin. We show that pine sporopollenin is primarily composed of aliphatic-polyketide-derived polyvinyl alcohol units and 7-O-p-coumaroylated C16 aliphatic units, crosslinked through a distinctive m-dioxane moiety featuring an acetal. Naringenin was also identified as a minor component of pine sporopollenin. This discovery answers the long-standing question about the chemical makeup of plant sporopollenin, laying the foundation for future investigations of sporopollenin biosynthesis and for design of new biomimetic polymers with desirable inert properties.

scholarly journals The cardiac nanoenvironment: form and function at the nanoscale

2021 ◽  
Author(s):  
Jashan P. Singh ◽  
Jennifer L. Young
Keyword(s):  
Large Scale ◽  
New Materials ◽  
Length Scales ◽  
Form And Function ◽  
Molecular Features ◽  
Wide Range ◽  
Physiological Homeostasis ◽  
Organ Level ◽  
And Function

AbstractMechanical forces in the cardiovascular system occur over a wide range of length scales. At the whole organ level, large scale forces drive the beating heart as a synergistic unit. On the microscale, individual cells and their surrounding extracellular matrix (ECM) exhibit dynamic reciprocity, with mechanical feedback moving bidirectionally. Finally, in the nanometer regime, molecular features of cells and the ECM show remarkable sensitivity to mechanical cues. While small, these nanoscale properties are in many cases directly responsible for the mechanosensitive signaling processes that elicit cellular outcomes. Given the inherent challenges in observing, quantifying, and reconstituting this nanoscale environment, it is not surprising that this landscape has been understudied compared to larger length scales. Here, we aim to shine light upon the cardiac nanoenvironment, which plays a crucial role in maintaining physiological homeostasis while also underlying pathological processes. Thus, we will highlight strategies aimed at (1) elucidating the nanoscale components of the cardiac matrix, and (2) designing new materials and biosystems capable of mimicking these features in vitro.

scholarly journals OncoThreads: Visualization of Large Scale Longitudinal Cancer Molecular Data

2021 ◽  
Author(s):  
Theresa A Harbig ◽  
Sabrina Nusrat ◽  
Tali Mazor ◽  
Qianwen Wang ◽  
Alexander Thomson ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Large Scale ◽  
Cancer Genomics ◽  
Temporal Patterns ◽  
Molecular Data ◽  
Liquid Biopsies ◽  
Sequencing Technologies ◽  
Molecular Features ◽  
Wide Range ◽  
The Impact

Molecular profiling of patient tumors and liquid biopsies over time with next-generation sequencing technologies and new immuno-profile assays are becoming part of standard research and clinical practice. With the wealth of new longitudinal data, there is a critical need for visualizations for cancer researchers to explore and interpret temporal patterns not just in a single patient but across cohorts. To address this need we developed OncoThreads, a tool for the visualization of longitudinal clinical and cancer genomics and other molecular data in patient cohorts. The tool visualizes patient cohorts as temporal heatmaps and Sankey diagrams that support the interactive exploration and ranking of a wide range of clinical and molecular features. This allows analysts to discover temporal patterns in longitudinal data, such as the impact of mutations on response to a treatment, e.g. emergence of resistant clones. We demonstrate the functionality of OncoThreads using a cohort of 23 glioma patients sampled at 2-4 timepoints. OncoThreads is freely available at http://oncothreads.gehlenborglab.org and implemented in Javascript using the cBioPortal web API as a backend.

scholarly journals Size-Resolved Evaluation of Simulated Deep Tropical Convection

2018 ◽  
Vol 146 (7) ◽  
pp. 2161-2182 ◽  
Author(s):  
Fabian Senf ◽  
Daniel Klocke ◽  
Matthias Brueck
Keyword(s):  
Deep Convection ◽  
Winter Season ◽  
Power Laws ◽  
Moist Convection ◽  
Modeling Framework ◽  
Western Atlantic ◽  
Spatial Coupling ◽  
Wide Range ◽  
Realistic Representation ◽  
Deep Moist Convection

Abstract Deep moist convection is an inherently multiscale phenomenon with organization processes coupling convective elements to larger-scale structures. A realistic representation of the tropical dynamics demands a simulation framework that is capable of representing physical processes across a wide range of scales. Therefore, storm-resolving numerical simulations at 2.4 km have been performed covering the tropical Atlantic and neighboring parts for 2 months. The simulated cloud fields are combined with infrared geostationary satellite observations, and their realism is assessed with the help of object-based evaluation methods. It is shown that the simulations are able to develop a well-defined intertropical convergence zone. However, marine convective activity measured by the cold cloud coverage is considerably underestimated, especially for the winter season and the western Atlantic. The spatial coupling across the resolved scales leads to simulated cloud number size distributions that follow power laws similar to the observations, with slopes steeper in winter than summer and slopes steeper over ocean than over land. The simulated slopes are, however, too steep, indicating too many small and too few large tropical cloud cells. It is also discussed that the number of larger cells is less influenced by multiday variability of environmental conditions. Despite the identified deficits, the analyzed simulations highlight the great potential of this modeling framework for process-based studies of tropical deep convection.

scholarly journals Theoretical study of the impact of adaptation on cell-fate heterogeneity and fractional killing

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Julien Hurbain ◽  
Darka Labavić ◽  
Quentin Thommen ◽  
Benjamin Pfeuty
Keyword(s):  
Cell Fate ◽  
Stochastic Dynamics ◽  
Biochemical Networks ◽  
Stochastic Bifurcation ◽  
Modeling Framework ◽  
Life And Death ◽  
Cell Fate Decisions ◽  
Wide Range ◽  
Adaptation Response ◽  
The Impact

Abstract Fractional killing illustrates the cell propensity to display a heterogeneous fate response over a wide range of stimuli. The interplay between the nonlinear and stochastic dynamics of biochemical networks plays a fundamental role in shaping this probabilistic response and in reconciling requirements for heterogeneity and controllability of cell-fate decisions. The stress-induced fate choice between life and death depends on an early adaptation response which may contribute to fractional killing by amplifying small differences between cells. To test this hypothesis, we consider a stochastic modeling framework suited for comprehensive sensitivity analysis of dose response curve through the computation of a fractionality index. Combining bifurcation analysis and Langevin simulation, we show that adaptation dynamics enhances noise-induced cell-fate heterogeneity by shifting from a saddle-node to a saddle-collision transition scenario. The generality of this result is further assessed by a computational analysis of a detailed regulatory network model of apoptosis initiation and by a theoretical analysis of stochastic bifurcation mechanisms. Overall, the present study identifies a cooperative interplay between stochastic, adaptation and decision intracellular processes that could promote cell-fate heterogeneity in many contexts.

scholarly journals Non-Employment Activity Type Imputation from Points of Interest and Mobility Data at an Individual Level: How Accurate Can We Get?

2019 ◽  
Vol 8 (12) ◽  
pp. 560 ◽  
Author(s):  
Thanos Bantis ◽  
James Haworth
Keyword(s):  
Empirical Bayes ◽  
Type Inference ◽  
Detection Accuracy ◽  
Activity Type ◽  
Land Use Patterns ◽  
Modeling Framework ◽  
Spatiotemporal Resolution ◽  
Mobility Data ◽  
Wide Range ◽  
Methodological Approaches

Human activity type inference has long been the focus for applications ranging from managing transportation demand to monitoring changes in land use patterns. Today’s ever increasing volume of mobility data allow researchers to explore a wide range of methodological approaches for this task. Such data, however, lack reference observations that would allow the validation of methodological approaches. This research proposes a methodological framework for urban activity type inference using a Dirichlet multinomial dynamic Bayesian network with an empirical Bayes prior that can be applied to mobility data of low spatiotemporal resolution. The method was validated using open source Foursquare data under different isochrone configurations. The results provide evidence of the limits of activity detection accuracy using such data as determined by the Area Under Receiving Operating Curve (AUROC), log-loss, and accuracy metrics. At the same time, results demonstrate that a hierarchical modeling framework can provide some flexibility against the challenges related to the nature of unsupervised activity classification using trajectory variables and POIs as input.

Ultrasonic Monitoring of Polymer Properties from the Melt to the Solid State: Influence of Chemical/Structural Details

1988 ◽  
Vol 142 ◽  
Author(s):  
L. Piché ◽  
G. Lessard ◽  
F. Massines ◽  
A. Hamel
Keyword(s):  
Molecular Structure ◽  
Glass Transition ◽  
Complex Modulus ◽  
Ultrasonic Technique ◽  
Ultrasonic Monitoring ◽  
Crystalline Polymers ◽  
Polymer Properties ◽  
Wide Range ◽  
Structural Details ◽  
End Use

AbstractAn ultrasonic technique is described for the simultaneous measurement of specific volume, V, sound velocity, v, and attenuation, a, at frequencies between f = 0.5 and f = 15 MHz, in a wide range of temperature (- 150 to + 400°C) and pressure (up to 2 kbars). The results (V,v,a) are translated into a complex modulus, M* = M’ + iM” and analyzed in terms of the thermodynamic state of the material. Typical results for amorphous and semi-crystalline polymers are presented which show that the technique is a probe of the fundamental features of these materials (glass transition, crystallization, melting, molecular structure) which determine processability and end use properties. The method should prove of great interest for quality and process control.

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