scholarly journals Diving beetle–like miniaturized plungers with reversible, rapid biofluid capturing for machine learning–based care of skin disease

2021 ◽  
Vol 7 (25) ◽  
pp. eabf5695
Author(s):  
Sangyul Baik ◽  
Jihyun Lee ◽  
Eun Je Jeon ◽  
Bo-yong Park ◽  
Da Wan Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Framework ◽  
Biomedical Technologies ◽  
Visual Indication ◽  
Personalized Health ◽  
Personalized Health Care ◽  
Smart Skin ◽  
Diving Beetle

Recent advances in bioinspired nano/microstructures have received attention as promising approaches with which to implement smart skin-interfacial devices for personalized health care. In situ skin diagnosis requires adaptable skin adherence and rapid capture of clinical biofluids. Here, we report a simple, all-in-one device consisting of microplungers and hydrogels that can rapidly capture biofluids and conformally attach to skin for stable, real-time monitoring of health. Inspired by the male diving beetle, the microplungers achieve repeatable, enhanced, and multidirectional adhesion to human skin in dry/wet environments, revealing the role of the cavities in these architectures. The hydrogels within the microplungers instantaneously absorb liquids from the epidermis for enhanced adhesiveness and reversibly change color for visual indication of skin pH levels. To realize advanced biomedical technologies for the diagnosis and treatment of skin, our suction-mediated device is integrated with a machine learning framework for accurate and automated colorimetric analysis of pH levels.

scholarly journals Structure prediction of crystals, surfaces and nanoparticles

2020 ◽  
Vol 378 (2186) ◽  
pp. 20190600
Author(s):  
Scott M. Woodley ◽  
Graeme M. Day ◽  
R. Catlow
Keyword(s):  
Machine Learning ◽  
Energy Function ◽  
Structure Prediction ◽  
Search Algorithm ◽  
Structure And Function ◽  
Current Status ◽  
Organic Systems ◽  
And Function

We review the current techniques used in the prediction of crystal structures and their surfaces and of the structures of nanoparticles. The main classes of search algorithm and energy function are summarized, and we discuss the growing role of methods based on machine learning. We illustrate the current status of the field with examples taken from metallic, inorganic and organic systems. This article is part of a discussion meeting issue ‘Dynamic in situ microscopy relating structure and function’.

scholarly journals Of Blooming Flowers and Multiple Sockets: The Role of Metaphors in the Politics of Infrastructural Work

2016 ◽  
Vol 29 (3) ◽  
pp. 68-87
Author(s):  
Marcello Aspria ◽  
Marleen de Mul ◽  
Samantha Adams ◽  
Roland Bal
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Health Information ◽  
Patient Portal ◽  
Alternative Strategies ◽  
Information Infrastructures ◽  
Sociotechnical Imaginaries ◽  
Personalized Health ◽  
Personalized Health Care

We explore the role of two metaphors for innovation and infrastructure integration in the development of a regional patient portal. Our premise is that metaphors have real consequences for agenda setting and decision-making; we view them as operationalizations of sociotechnical imaginaries. Drawing on our formative study of the portal project, we focus on the generative character of metaphors and argue that they are constitutive elements of information infrastructures. While the two metaphors in our study helped to make imaginaries of ‘integrated’ and ‘personalized’ health care more definite, cognizable, and classifiable, they also concealed the politics of infrastructural work. We argue that the act of ‘spelling out’ metaphors can open up a space for new imaginaries and alternative strategies. With this study we aim to contribute to existing knowledge about infrastructural work, and to renew the interest among STS scholars for the role of discursive attributes in information infrastructures.Keywords: metaphors, e-Health, information infrastructures

scholarly journals Correlation-driven machine learning for accelerated reliability assessment of solder joints in electronics

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Vahid Samavatian ◽  
Mahmud Fotuhi-Firuzabad ◽  
Majid Samavatian ◽  
Payman Dehghanian ◽  
Frede Blaabjerg
Keyword(s):  
Machine Learning ◽  
Thermal Cycling ◽  
Solder Joints ◽  
Electronic Materials ◽  
Electronic Devices ◽  
Reliability Assessment ◽  
Electronic Systems ◽  
Learning Framework ◽  
Useful Lifetime

Abstract The quantity and variety of parameters involved in the failure evolutions in solder joints under a thermo-mechanical process directs the reliability assessment of electronic devices to be frustratingly slow and expensive. To tackle this challenge, we develop a novel machine learning framework for reliability assessment of solder joints in electronic systems; we propose a correlation-driven neural network model that predicts the useful lifetime based on the materials properties, device configuration, and thermal cycling variations. The results indicate a high accuracy of the prediction model in the shortest possible time. A case study will evaluate the role of solder material and the joint thickness on the reliability of electronic devices; we will illustrate that the thermal cycling variations strongly determine the type of damage evolution, i.e., the creep or fatigue, during the operation. We will also demonstrate how an optimal selection of the solder thickness balances the damage types and considerably improves the useful lifetime. The established framework will set the stage for further exploration of electronic materials processing and offer a potential roadmap for new developments of such materials.

Opportunities in the Economics of Personalized Health Care and Prevention

2013 ◽  
Vol 16 (2) ◽  
pp. S13-S22
Author(s):  
David O. Meltzer
Keyword(s):  
Personalized Medicine ◽  
System Level ◽  
Future Research ◽  
Broad Perspective ◽  
Implementation And Dissemination ◽  
Economic Analyses ◽  
Personalized Health ◽  
Personalized Health Care ◽  
Benefits And Costs

Abstract Personalized medicine is best viewed from a broad perspective of trying to use information about a patient to improve care. While “personalized medicine” often emphasizes the value of genetic information, traditional clinical approaches to personalizing care based on patient phenotype, provider and system-level factors should not be neglected. As these diverse approaches to personalization are examined, tools such as cost-effectiveness analysis can provide important insights into the value of these approaches, strategies for their implementation and dissemination, and priorities for future research. Such analyses are likely to be most insightful if they recognize that patient and provider behaviors are essential determinants of the value of treatments and that patient factors in particular may have large effects on the value of treatments and the need for interventions to improve decision making. These comments suggest three major areas of opportunity for economic analyses of personalized medicine: (1) traditional clinical approaches to personalized medicine, (2) multi-perspective studies of the benefits and costs of personalized medicine, and (3) the role of behavior in the value of personalized medicine.

scholarly journals How Policies on Restaurants, Bars, Nightclubs, Masks, Schools, and Travel Influenced Swiss COVID-19 Reproduction Ratios

2020 ◽  
Author(s):  
C. K. Sruthi ◽  
Malay Ranjan Biswal ◽  
Brijesh Saraswat ◽  
Himanshu Joshi ◽  
Meher K. Prakash
Keyword(s):  
Machine Learning ◽  
Secondary Schools ◽  
Public Transport ◽  
Scientific Basis ◽  
Data Driven ◽  
Economic Activities ◽  
Model Framework ◽  
Fine Grained ◽  
Learning Framework

SummaryThe role of complete lockdowns in reducing the reproduction ratios (Rt) of COVID-19 is now established. However, the persisting reality in many countries is no longer a complete lockdown, but restrictions of varying degrees using different choices of Non-pharmaceutical interaction (NPI) policies. A scientific basis for understanding the effectiveness of these graded NPI policies in reducing the Rt is urgently needed to address the concerns on personal liberties and economic activities. In this work, we develop a systematic relation between the degrees of NPIs implemented by the 26 cantons in Switzerland during March 9 – September 13 and their respective contributions to the Rt. Using a machine learning framework, we find that Rt which should ideally be lower than 1.0, has significant contributions in the post-lockdown scenario from the different activities - restaurants (0.0523 (CI. 0.0517-0.0528)), bars (0.030 (CI. 0.029-0.030)), and nightclubs (0.154 (CI. 0.154-0.156)). Activities which keep the land-borders open (0.177 (CI. 0.175-0.178)), and tourism related activities contributed comparably 0.177 (CI. 0.175-0.178). However, international flights with a quarantine did not add further to the Rt of the cantons. The requirement of masks in public transport and secondary schools contributed to an overall 0.025 (CI. 0.018-0.030) reduction in Rt, compared to the baseline usage even when there are no mandates. Although causal relations are not guaranteed by the model framework, it nevertheless provides a fine-grained justification for the relative merits of choice and the degree of the NPIs and a data-driven strategy for mitigating Rt.

Detecting soil degradation and restoration through a novel coupled sensor and machine learning framework

2020 ◽  
Author(s):  
John Quinton ◽  
Mike James ◽  
Jess Davies ◽  
Greg Whiting ◽  
Christopeher Nemeth ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Frequency ◽  
Soil Degradation ◽  
Soil Health ◽  
Learning System ◽  
High Frequency Data ◽  
Frequency Data ◽  
Soil Functions ◽  
Learning Framework

<p>In this poster we will outline a new  ambitious cross-disciplinary project focused on detecting soil degradation and restoration through a novel multi-functional soil sensing platform that combines conventional and newly created sensors and a machine learning framework. Our work  aims to advance our understanding of dynamic soil processes that operate at different temporal/spatial scales. Through the creation of an innovative new approach to capturing and analyzing high frequency data from in-situ sensors, this project will predict the rate and direction of soil system functions for sites undergoing degradation or restoration. To do this, we will build and train a new mechanistically-informed machine learning system to turn high frequency data on multiple soil functions, such as water infiltration, CO2 production, and surface soil movement, into predictions of longer term changes in soil health including the status of microbial processes, soil organic matter (SOM) content, and other properties and processes. Such an approach could be transformative: a system that will allow short-term sensor data to be used to evaluate longer term soil transformations in key ecosystem functions. We will start our work with a suite of off-the-shelf sensors observing multiple soil functions that can be installed quickly. These data will allow us to rapidly initiate development and training of a novel mechanistically informed machine learning framework. In parallel we will develop two new soil health sensors focused on in-situ real time measurement of decomposition rates and transformation of soil color that reflects the accumulation or loss of SOM. We will then link these new sensors with a suite of conventional sensors in a novel data collection and networking system coupled to the Swarm satellite network to create a low cost sensor array that can be deployed in remote areas and used to support studies of soil degradation or progress toward restoration worldwide.<br><br></p>

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

{10-12} Twinning Mechanism During In Situ Micro-Tensile Loading of Pure Mg: Role of Basal Slip and Twin-Twin Boundary Interaction

2020 ◽  
Author(s):  
Nicolò Maria della Ventura ◽  
Szilvia Kalácska ◽  
Daniele Casari ◽  
Thomas Edward James Edwards ◽  
Johann Michler ◽  
...  
Keyword(s):  
Twin Boundary ◽  
Basal Slip ◽  
Tensile Loading ◽  
Boundary Interaction
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