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1. Effective design principles for leakless strand displacement systems

   https://doi.org/10.1073/pnas.1806859115  

   Wang, Boya; Thachuk, Chris; Ellington, Andrew D.; Winfree, Erik; Soloveichik, David (December 2018, Proceedings of the National Academy of Sciences)

   Artificially designed molecular systems with programmable behaviors have become a valuable tool in chemistry, biology, material science, and medicine. Although information processing in biological regulatory pathways is remarkably robust to error, it remains a challenge to design molecular systems that are similarly robust. With functionality determined entirely by secondary structure of DNA, strand displacement has emerged as a uniquely versatile building block for cell-free biochemical networks. Here, we experimentally investigate a design principle to reduce undesired triggering in the absence of input (leak), a side reaction that critically reduces sensitivity and disrupts the behavior of strand displacement cascades. Inspired by error correction methods exploiting redundancy in electrical engineering, we ensure a higher-energy penalty to leak via logical redundancy. Our design strategy is, in principle, capable of reducing leak to arbitrarily low levels, and we experimentally test two levels of leak reduction for a core “translator” component that converts a signal of one sequence into that of another. We show that the leak was not measurable in the high-redundancy scheme, even for concentrations that are up to 100 times larger than typical. Beyond a single translator, we constructed a fast and low-leak translator cascade of nine strand displacement steps and a logic OR gate circuit consisting of 10 translators, showing that our design principle can be used to effectively reduce leak in more complex chemical systems. 

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2. Sound‐guided assessment and localization of pulmonary air leak

   https://doi.org/10.1002/btm2.10322  

   Pinezich, Meghan R.; Mir, Seyed Mohammad; Reimer, Jonathan A.; Kaslow, Sarah R.; Chen, Jiawen; Guenthart, Brandon A.; Bacchetta, Matthew; O'Neill, John D.; Vunjak‐Novakovic, Gordana; Kim, Jinho (May 2022, Bioengineering & Translational Medicine)

   Pulmonary air leak is the most common complication of lung surgery, with air leaks that persist longer than 5 days representing a major source of post-surgery morbidity. Clinical management of air leaks is challenging due to limited methods to precisely locate and assess leaks. Here, we present a sound-guided methodology that enables rapid quantitative assessment and precise localization of air leaks by analyzing the distinct sounds generated as the air escapes through defective lung tissue. Air leaks often present after lung surgery due to loss of tissue integrity at or near a staple line. Accordingly, we investigated air leak sounds from a focal pleural defect in a rat model and from a staple line failure in a clinically relevant swine model to demonstrate the high sensitivity and translational potential of this approach. In rat and swine models of free-flowing air leak under positive pressure ventilation with intrapleural microphone 1 cm from the lung surface, we identified that: (a) pulmonary air leaks generate sounds that contain distinct harmonic series, (b) acoustic characteristics of air leak sounds can be used to classify leak severity, and (c) precise location of the air leak can be determined with high resolution (within 1 cm) by mapping the sound loudness level across the lung surface. Our findings suggest that sound-guided assessment and localization of pulmonary air leaks could serve as a diagnostic tool to inform air leak detection and treatment strategies during video-assisted thoracoscopic surgery (VATS) or thoracotomy procedures. 

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3. Machine learning model and strategy for fast and accurate detection of leaks in water supply network

   https://doi.org/10.1186/s43065-021-00021-6  

   Fan, Xudong; Zhang, Xijin; Yu, Xiong (April 2021, Journal of Infrastructure Preservation and Resilience)

   Abstract The water supply network (WSN) is subjected to leaks that compromise its service to the communities, which, however, is challenging to identify with conventional approaches before the consequences surface. This study developed Machine Learning (ML) models to detect leaks in the WDN. Water pressure data under leaking versus non-leaking conditions were generated with holistic WSN simulation code EPANET considering factors such as the fluctuating user demands, data noise, and the extent of leaks, etc. The results indicate that Artificial Neural Network (ANN), a supervised ML model, can accurately classify leaking versus non-leaking conditions; it, however, requires balanced dataset under both leaking and non-leaking conditions, which is difficult for a real WSN that mostly operate under normal service condition. Autoencoder neural network (AE), an unsupervised ML model, is further developed to detect leak with unbalanced data. The results show AE ML model achieved high accuracy when leaks occur in pipes inside the sensor monitoring area, while the accuracy is compromised otherwise. This observation will provide guidelines to deploy monitoring sensors to cover the desired monitoring area. A novel strategy is proposed based on multiple independent detection attempts to further increase the reliability of leak detection by the AE and is found to significantly reduce the probability of false alarm. The trained AE model and leak detection strategy is further tested on a testbed WSN and achieved promising results. The ML model and leak detection strategy can be readily deployed for in-service WSNs using data obtained with internet-of-things (IoTs) technologies such as smart meters. 

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4. RESIN: A Holistic Service for Dealing with Memory Leaks in Production Cloud Infrastructure

   Lou, Chang; Chen, Cong; Huang, Peng; Dang, Yingnong; Qin, Si; Yang, Xinsheng; Li, Xukun; Lin, Qingwei; Chintalapati, Murali (July 2022, 16th USENIX Symposium on Operating Systems Design and Implementation)

   Memory leak is a notorious issue. Despite the extensive efforts, addressing memory leaks in large production cloud systems remains challenging. Existing solutions incur high overhead and/or suffer from high inaccuracies. This paper presents RESIN, a solution designed to holistically address memory leaks in production cloud infrastructure. RESIN takes a divide-and-conquer approach to tackle the challenges. It performs a low-overhead detection first with a robust bucketization-based pivot scheme to identify suspicious leaking entities. It then takes live heap snapshots at appropriate time points in carefully sampled leak entities. RESIN analyzes the collected snapshots for leak diagnosis. Finally, RESIN automatically mitigates detected leaks. RESIN has been running in production in Microsoft Azure for 3 years. It reports on average 24 leak tickets each month with high accuracy and low overhead, and provides effective diagnosis reports. Its results translate into a 41× reduction of VM reboots caused by low memory. 

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5. From Leaks to Fixes: Automated Repairs for Resource Leak Warnings

   https://doi.org/10.1145/3611643.3616267  

   Utture, Akshay; Palsberg, Jens (November 2023, ACM)

   Resource leaks are a common and elusive source of bugs that can result in crashes and security vulnerabilities. The most effective technique to identify such leaks during development is static analysis. However, empirical studies show that in addition to leak warnings, developers often need help in the form of automated fix suggestions to correctly repair such leaks. The only existing tool that can suggest resource-leak fixes is the general-purpose tool Footpatch. Footpatch, however, performs poorly at this task; it generates fixes for only 6% of the leaks, out of which only 27% are correct. In this paper, we introduce RLFixer, a specialized repair tool that generates high-quality fixes for resource leaks identified by any resource-leak detector. A major challenge for RLFixer is that the most general version of the resource-leak repair problem is at least as hard as compile-time object deallocation, a well-known hard problem for compilers. RLFixer tackles this issue by separating the resource-leaks that are infeasible for a compile-time tool to fix from those that are feasible to fix. RLFixer achieves this separation by using a new data-flow analysis of resource objects to classify how they escape the context of their methods. The same analysis also enables RLFixer to generate correct repairs for the feasible-to-fix leaks. RLFixer is demand-driven and hence only analyzes statements relevant to the leak, thereby keeping overhead low. We evaluated RLFixer by applying it to warnings generated by five popular Java resource-leak detectors. We show that, on average, RLFixer generates repairs for 66% of their warnings, out of which 95% are correct. It has an average repair time of 14 seconds 

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