Accelerated search and design of stretchable graphene kirigami using machine learning

Making kirigami-inspired cuts into a sheet has been shown to be an effective way of designing stretchable materials with metamorphic properties where the 2D shape can transform into complex 3D shapes. However, finding the optimal solutions is not straightforward as the number of possible cutting patterns grows exponentially with system size. Here, we report on how machine learning (ML) can be used to approximate the target properties, such as yield stress and yield strain, as a function of cutting pattern. Our approach enables the rapid discovery of kirigami designs that yield extreme stretchability as verified by molecular dynamics (MD) simulations. We find that convolutional neural networks, commonly used for classification in vision tasks, can be applied for regression to achieve an accuracy close to the precision of the MD simulations. This approach can then be used to search for optimal designs that maximize elastic stretchability with only 1000 training samples in a large design space of ∼4×106 candidate designs. This example demonstrates the power and potential of ML in finding optimal kirigami designs at a fraction of iterations that would be required of a purely MD or experiment-based approach, where no prior knowledge of the governing physics is known or available.P. Z. H. developed the codes, performed the simulations and data analysis, and wrote the manuscript with input from all authors. P. Z. H. and E. D. C. developed the machine learning methods. P. Z. H., D. K. C. and H. S. P. acknowledge the Hariri Institute Research Incubation Grant No. 2018-02-002 and the Boston University High Performance Shared Computing Cluster. P. Z. H. is grateful for the Hariri Graduate Fellowship. P. Z. H. thank Grace Gu and Adrian Yi for helpful discussions. (2018-02-002 - Hariri Graduate Fellowship)Published versio

Chronic subordination stress selectively downregulates the insulin signaling pathway in liver and skeletal muscle but not in adipose tissue of male mice

Chronic stress has been associated with obesity, glucose intolerance, and insulin resistance. We developed a model of chronic psychosocial stress (CPS) in which subordinate mice are vulnerable to obesity and the metabolic-like syndrome while dominant mice exhibit a healthy metabolic phenotype. Here we tested the hypothesis that the metabolic difference between subordinate and dominant mice is associated with changes in functional pathways relevant for insulin sensitivity, glucose and lipid homeostasis. Male mice were exposed to CPS for four weeks and fed either a standard diet or a high-fat diet (HFD). We first measured, by real-time PCR candidate genes, in the liver, skeletal muscle, and the perigonadal white adipose tissue (pWAT). Subsequently, we used a probabilistic analysis approach to analyze different ways in which signals can be transmitted across the pathways in each tissue. Results showed that subordinate mice displayed a drastic downregulation of the insulin pathway in liver and muscle, indicative of insulin resistance, already on standard diet. Conversely, pWAT showed molecular changes suggestive of facilitated fat deposition in an otherwise insulin-sensitive tissue. The molecular changes in subordinate mice fed a standard diet were greater compared to HFD-fed controls. Finally, dominant mice maintained a substantially normal metabolic and molecular phenotype even when fed a HFD. Overall, our data demonstrate that subordination stress is a potent stimulus for the downregulation of the insulin signaling pathway in liver and muscle and a major risk factor for the development of obesity, insulin resistance, and type 2 diabetes mellitus.Supported by UofMN Medical School start-up funds to AB, Medical Research Council MRC Disease Model Core and British Heart Foundation program grants to AVP, and BIO2011-27069 from the Spanish Ministry of Economy and Competitiveness and PROMETEOII/2014/025 from the GVA-FEDER to JD. VS was supported by a graduate student fellowship of the University of Parma. CC was supported by EU FP7-People Project(ref 316861) "MLPM2012: Machine Learning For Personalized Medicine".This is the final version of the article. It first appeared from Taylor & Francis via http://dx.doi.org/10.3109/10253890.2016.115149

Chronic subordination stress selectively downregulates the insulin signaling pathway in liver and skeletal muscle but not in adipose tissue of male mice.

Chronic stress has been associated with obesity, glucose intolerance, and insulin resistance. We developed a model of chronic psychosocial stress (CPS) in which subordinate mice are vulnerable to obesity and the metabolic-like syndrome while dominant mice exhibit a healthy metabolic phenotype. Here we tested the hypothesis that the metabolic difference between subordinate and dominant mice is associated with changes in functional pathways relevant for insulin sensitivity, glucose and lipid homeostasis. Male mice were exposed to CPS for four weeks and fed either a standard diet or a high-fat diet (HFD). We first measured, by real-time PCR candidate genes, in the liver, skeletal muscle, and the perigonadal white adipose tissue (pWAT). Subsequently, we used a probabilistic analysis approach to analyze different ways in which signals can be transmitted across the pathways in each tissue. Results showed that subordinate mice displayed a drastic downregulation of the insulin pathway in liver and muscle, indicative of insulin resistance, already on standard diet. Conversely, pWAT showed molecular changes suggestive of facilitated fat deposition in an otherwise insulin-sensitive tissue. The molecular changes in subordinate mice fed a standard diet were greater compared to HFD-fed controls. Finally, dominant mice maintained a substantially normal metabolic and molecular phenotype even when fed a HFD. Overall, our data demonstrate that subordination stress is a potent stimulus for the downregulation of the insulin signaling pathway in liver and muscle and a major risk factor for the development of obesity, insulin resistance, and type 2 diabetes mellitus.Supported by UofMN Medical School start-up funds to AB, Medical Research Council MRC Disease Model Core and British Heart Foundation program grants to AVP, and BIO2011-27069 from the Spanish Ministry of Economy and Competitiveness and PROMETEOII/2014/025 from the GVA-FEDER to JD. VS was supported by a graduate student fellowship of the University of Parma. CC was supported by EU FP7-People Project(ref 316861) "MLPM2012: Machine Learning For Personalized Medicine".This is the final version of the article. It first appeared from Taylor & Francis via http://dx.doi.org/10.3109/10253890.2016.115149

Machine learning for crystal identification and discovery

As computers get faster, researchers -- not hardware or algorithms -- become the bottleneck in scientific discovery. Computational study of colloidal self-assembly is one area that is keenly affected: even after computers generate massive amounts of raw data, performing an exhaustive search to determine what (if any) ordered structures occur in a large parameter space of many simulations can be excruciating. We demonstrate how machine learning can be applied to discover interesting areas of parameter space in colloidal self assembly. We create numerical fingerprints -- inspired by bond orientational order diagrams -- of structures found in self-assembly studies and use these descriptors to both find interesting regions in a phase diagram and identify characteristic local environments in simulations in an automated manner for simple and complex crystal structures. Utilizing these methods allows analysis methods to keep up with the data generation ability of modern high-throughput computing environments.Comment: Fixed typo, added missing acknowledgment, added supplementary informatio

The prevalence of HBV, HCV, and HIV infections in patients with cataract in Turkey

Background: Hepatitis B (HBV), hepatitis C (HCV), and human immunodeficiency virus (HIV) cause common infections all around the world. The aim of this study is to determine the HBV, HCV, and HIV prevalence in cataract patients. Material and methods: This was a retrospective study and was carried out in the ophthalmology clinic of Adana Numune Research and Training Hospital (Adana, Turkey). One hundred and forty patients undergoing cataract surgery were included to the study. The clinical findings were extracted from the medical records of the patients. The serological analyzing was done by one step immunoassay-based rapid diagnostic card tests for hepatitis B surface antigen (HBsAg), anti-HCV, and anti-HIV antibodies. HBV and HCV confirmations were done by ELISA (enzyme-linked immunosorbent assay test)-based serological tests. Results: Of the 1040 patients included in the study, 462 (44.4%) were females and 578 (55.6%) were males. The mean age of the patients was 64.8 ± 13.7 years. HBsAg was positive in 39 (3.8%) patients and Anti-HCV was positive in 14 (1.3%) patients. None of the patients had HIV. Conclusion: HBV is the most common infection among cataract patients and it is very important to apply infection prevention methods

The Persistence Of Orientalism And The Positive Portrayal Of The Gulen Movement In The New York Times And In The Wall Street Journal Between 1985 And 2016

This thesis argues that the positive portrayal of the Gulen (Hizmet) movement in the New York Times and the Wall Street Journal is a direct result of persistent Orientalism in the American academia and news media. The evidence reveals that most of the articles published in the New York Times and in the Wall Street Journal Between 1985 and 2016 and the majority of academic works completely ignored the controversial nature of the Gulen movement. This favorable approach portrays Islam as monolithic and indicates the otherness of Muslims by subjecting them to a separate set of standards for “moderation.” This is problematic for two distinct reasons. First, it shows that Orientalism is still persistent well into the year 2016. Furthermore, when referring to Gulen as “moderate and progressive,” the journalists and academics falsely portray all Muslims negatively since this portrayal indicates Gulen’s ideology is moderate in comparison to all interpretations of Islam