Experimental Investigation of the Self-Healing Potential of Bacteria for Sustainable Concrete Structures

Although concrete is the most widely used building material in the world, its limited tensile strength makes cracking a common phenomenon in concrete elements. This study investigates the potential of autonomous self-healing as an eco-friendly and lowcost method to increase the durability of concrete. The crack-healing potential of different types of high-alkaline-tolerant bacteria or calcite-precipitation microorganisms is investigated. High-alkaline-tolerant bacteria and calcite-precipitation microorganisms were used to retrofit lab-fractured concrete samples. The samples healed with each of these bacteria groups were cast and tested under compressive load up to failure to measure the compressive strength of the concrete samples. The outcomes of experimental tests on concrete samples healed with biological processes demonstrate how this technique can be implemented when retrofitting durability-enhanced, eco-friendly concrete structures to improve the strength of durability of the material and ultimately improve the durability of many forms of concrete infrastructure

Expressions 2004-2006

https://openspace.dmacc.edu/expressions/1022/thumbnail.jp

Experimental Investigation of the Self Healing Potential of Bacteria for Sustainable Concrete Structures

ZSB12017-SJAUXAlthough concrete is the most widely used building material in the world, its limited tensile strength makes cracking a common phenomenon in concrete elements. This study investigates the potential of autonomous self-healing as an eco-friendly and lowcost method to increase the durability of concrete. The crack-healing potential of different types of high-alkaline-tolerant bacteria or calcite-precipitation microorganisms is investigated. High-alkaline-tolerant bacteria and calcite-precipitation microorganisms were used to retrofit lab-fractured concrete samples. The samples healed with each of these bacteria groups were cast and tested under compressive load up to failure to measure the compressive strength of the concrete samples. The outcomes of experimental tests on concrete samples healed with biological processes demonstrate how this technique can be implemented when retrofitting durability-enhanced, eco-friendly concrete structures to improve the strength of durability of the material and ultimately improve the durability of many forms of concrete infrastructure

Experimental Investigation of the Self Healing Potential of Bacteria for Sustainable Concrete Structures [Brief]

Concrete is the most widely used building material in the world, but\u2014due to its limited tensile strength\u2014cracking is a common phenomenon in concrete elements. Cracks may affect the durability of concrete structures by allowing potentially harmful liquids or gasses to sink in. Additionally, the steel reinforcement within concrete elements may be affected by degradation phenomena, such as corrosion. Increasing the service life of concrete structures is a key task of civil engineering, and self-healing is an eco-friendly and low-cost method to increase the durability of concrete. This work aims to study an autonomous self-healing technique. The crack-healing potential of different types of high-alkaline-tolerant bacteria or calcite-precipitation microorganisms is investigated. The outcomes of experimental tests on concrete samples healed with biological processes demonstrate how this technique can be implemented when retrofitting durability-enhanced, eco-friendly concrete structures to improve the strength of durability of the material

Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer (BC) has been well established, and tumor-infiltrating lymphocytes (TILs) have emerged as a predictive and prognostic biomarker for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2 negative) breast cancer (TNBC) and HER2-positive breast cancer. How computational assessment of TILs can complement manual TIL-assessment in trial- and daily practices is currently debated and still unclear. Recent efforts to use machine learning (ML) for the automated evaluation of TILs show promising results. We review state-of-the-art approaches and identify pitfalls and challenges by studying the root cause of ML discordances in comparison to manual TILs quantification. We categorize our findings into four main topics; (i) technical slide issues, (ii) ML and image analysis aspects, (iii) data challenges, and (iv) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns, or design choices in the computational implementation. To aid the adoption of ML in TILs assessment, we provide an in-depth discussion of ML and image analysis including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial- and routine clinical management of patients with TNBC

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Nudges for Privacy and Security: Understanding and Assisting Users' Choices Online

Advancements in information technology often task users with complex and consequential privacy and security decisions. A growing body of research has investigated individuals' choices in the presence of privacy and information security tradeoffs, the decision-making hurdles affecting those choices, and ways to mitigate such hurdles. This article provides a multi-disciplinary assessment of the literature pertaining to privacy and security decision making. It focuses on research on assisting individuals' privacy and security choices with soft paternalistic interventions that nudge users toward more beneficial choices. The article discusses potential benefits of those interventions, highlights their shortcomings, and identifies key ethical, design, and research challenges.National Science Foundation [CNS-1012763, CNS-0627513, CNS-0905562]; Google; CMU CyLab from the Army Research Office [DAAD19-02-1-0389, W911NF-09-1-0273]; IWT SBO SPION Project; Nokia; France Telecom; CMU/Portugal Information and Communication Technologies InstituteThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Spatial analyses of immune cell infiltration in cancer: current methods and future directions.:A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well-described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers.</p