Efficient sonochemical catalytic degradation of tetracycline using TiO2 fractured nanoshells

Overexposure to antibiotics originating in wastewater has profound environmental and health implications. Conventional treatment methods are not fully effective in removing certain antibiotics, such as the commonly used antibiotic, tetracycline, leading to its accumulation in water catchments. Alternative antibiotic removal strategies are garnering attention, including sonocatalytic oxidative processes. In this work, we investigated the degradation of tetracycline using a combination of TiO2 fractured nanoshells (TFNs) and an advanced sonochemical reactor design. The study encompassed an examination of multiple process parameters to understand their effects on the degradation of tetracycline. These included tetracycline adsorption on TFNs, reaction time, initial tetracycline concentration, solvent pH, acoustic pressure amplitude, number of acoustic cycles, catalyst dosage, TFNs' reusability, and the impact of adjuvants such as light and H2O2. Though TFNs adsorbed tetracycline, the addition of ultrasound was able to degrade tetracycline completely (with 100% degradation) within six minutes. Under the optimal operating conditions, the proposed sonocatalytic system consumed 80% less energy compared to the values reported in recently published sonocatalytic research. It also had the lowest CO2 footprint when compared to the other sono-/photo-based technologies. This study suggests that optimizing the reaction system and operating the reaction under low power and at a lower duty cycle are effective in achieving efficient cavitation for sonocatalytic reactions

On the Impact of Flaky Tests in Automated Program Repair

The literature of Automated Program Repair is largely dominated by approaches that leverage test suites not only to expose bugs but also to validate the generated patches. Unfortunately, beyond the widely-discussed concern that test suites are an imperfect oracle because they can be incomplete, they can include tests that are flaky. A flaky test is one that can be passed or failed by a program in a non-deterministic way. Such tests are generally carefully removed from the repair benchmarks. In practice, however, flaky tests are available test suite of software repositories. To the best of our knowledge, no study has discussed this threat to validity for evaluation of program repair. In this work, we highlight this threat and further investigate the impact of flaky tests by reverting their removal from the Defects4J benchmark. Our study aims to characterize the impact of flaky tests for localizing bugs and the eventual influence on the repair performance. Among other insights, we find that (1) although flaky tests are few (≈0.3%) of total tests, they affect experiments related to a large proportion (98.9%) of Defects4J real-world faults; (2) most flaky tests (98%) actually provide deterministic results under specific environment configurations (with the jdk version influencing the results); (3) flaky tests drastically hinder the effectiveness of spectrum-based fault localization (e.g., the rankings of 90 bugs drop down while none of the bugs obtains better location results compared with results achieved without flaky tests); and (4) the repairability of APR tools is greatly affected by the presence of flaky tests (e.g., 10 state of the art APR tools can now fix significantly fewer bugs than when the benchmark is manually curated to remove flaky tests). Given that the detection of flaky tests is still nascent, we call for the program repair community to relax the artificial assumption that the test suite is free from flaky tests. One direction that we propose is to consider developing strategies where patches that partially-fix bugs are considered worthwhile: a patch may make the program pass some test cases but fail some (which may actually be the flaky ones)

Gradual Crossover from Subdiffusion to Normal Diffusion: A Many-Body Effect in Protein Surface Water

Dynamics of hydration water is essential for the function of biomacromolecules. Previous studies have demonstrated that water molecules exhibit subdiffusion on the surface of biomacromolecules; yet the microscopic mechanism remains vague. Here, by performing neutron scattering, molecular dynamics simulations, and analytic modeling on hydrated perdeuterated protein powders, we found water molecules jump randomly between trapping sites on protein surfaces, whose waiting times obey a broad distribution, resulting in subdiffusion. Moreover, the subdiffusive exponent gradually increases with observation time towards normal diffusion due to a many-body volume-exclusion effect

Gradual Crossover from Subdiffusion to Normal Diffusion: A Many-Body Effect in Protein Surface Water

Dynamics of hydration water is essential for the function of biomacromolecules. Previous studies have demonstrated that water molecules exhibit subdiffusion on the surface of biomacromolecules; yet the microscopic mechanism remains vague. Here, by performing neutron scattering, molecular dynamics simulations, and analytic modeling on hydrated perdeuterated protein powders, we found water molecules jump randomly between trapping sites on protein surfaces, whose waiting times obey a broad distribution, resulting in subdiffusion. Moreover, the subdiffusive exponent gradually increases with observation time towards normal diffusion due to a many-body volume-exclusion effect

Exploring Blockchain Technology through a Modular Lens: A Survey

Blockchain has attracted significant attention in recent years due to its potential to revolutionize various industries by providing trustlessness. To comprehensively examine blockchain systems, this article presents both a macro-level overview on the most popular blockchain systems, and a micro-level analysis on a general blockchain framework and its crucial components. The macro-level exploration provides a big picture on the endeavors made by blockchain professionals over the years to enhance the blockchain performance while the micro-level investigation details the blockchain building blocks for deep technology comprehension. More specifically, this article introduces a general modular blockchain analytic framework that decomposes a blockchain system into interacting modules and then examines the major modules to cover the essential blockchain components of network, consensus, and distributed ledger at the micro-level. The framework as well as the modular analysis jointly build a foundation for designing scalable, flexible, and application-adaptive blockchains that can meet diverse requirements. Additionally, this article explores popular technologies that can be integrated with blockchain to expand functionality and highlights major challenges. Such a study provides critical insights to overcome the obstacles in designing novel blockchain systems and facilitates the further development of blockchain as a digital infrastructure to service new applications

PEELER: Learning to Effectively Predict Flakiness without Running Tests

—Regression testing is a widely adopted approach to expose change-induced bugs as well as to verify the correctness/robustness of code in modern software development settings. Unfortunately, the occurrence of flaky tests leads to a significant increase in the cost of regression testing and eventually reduces the productivity of developers (i.e., their ability to find and fix real problems). State-of-the-art approaches leverage dynamic test information obtained through expensive re-execution of test cases to effectively identify flaky tests. Towards accounting for scalability constraints, some recent approaches have built on static test case features, but fall short on effectiveness. In this paper, we introduce PEELER, a new fully static approach for predicting flaky tests through exploring a representation of test cases based on the data dependency relations. The predictor is then trained as a neural network based model, which achieves at the same time scalability (because it does not require any test execution), effectiveness (because it exploits relevant test dependency features), and practicality (because it can be applied in the wild to find new flaky tests). Experimental validation on 17,532 test cases from 21 Java projects shows that PEELER outperforms the state-of-the-art FlakeFlagger by around 20 percentage points: we catch 22% more flaky tests while yielding 51% less false positives. Finally, in a live study with projects in-the-wild, we reported to developers 21 flakiness cases, among which 12 have already been confirmed by developers as being indeed flaky

GhWRKY6 Acts as a Negative Regulator in Both Transgenic Arabidopsis and Cotton During Drought and Salt Stress

Drought and high salinity are key limiting factors for cotton production. Therefore, research is increasingly focused on the underlying stress response mechanisms of cotton. We first identified and cloned a novel gene encoding the 525 amino acids in cotton, namely GhWRKY6. qRT-PCR analysis indicated that GhWRKY6 was induced by NaCl, PEG 6000 and ABA. Analyses of germination rate and root length indicated that overexpression of GhWRKY6 in Arabidopsis resulted in hypersensitivity to ABA, NaCl, and PEG 6000. In contrast, the loss-of-function mutant wrky6 was insensitive and had slightly longer roots than the wild-type did under these treatment conditions. Furthermore, GhWRKY6 overexpression in Arabidopsis modulated salt- and drought-sensitive phenotypes and stomatal aperture by regulating ABA signaling pathways, and reduced plant tolerance to abiotic stress through reactive oxygen species (ROS) enrichment, reduced proline content, and increased electrolytes and malondialdehyde (MDA). The expression levels of a series of ABA-, salt- and drought-related marker genes were altered in overexpression seedlings. Virus-induced gene silencing (VIGS) technology revealed that down-regulation of GhWRKY6 increased salt tolerance in cotton. These results demonstrate that GhWRKY6 is a negative regulator of plant responses to abiotic stress via the ABA signaling pathway

A Fine-grained Data Set and Analysis of Tangling in Bug Fixing Commits

Context: Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objective: We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods: We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the same label, we have consensus. Results: We estimate that between 17% and 32% of all changes in bug fixing commits modify the source code to fix the underlying problem. However, when we only consider changes to the production code files this ratio increases to 66% to 87%. We find that about 11% of lines are hard to label leading to active disagreements between participants. Due to confirmed tangling and the uncertainty in our data, we estimate that 3% to 47% of data is noisy without manual untangling, depending on the use case. Conclusion: Tangled commits have a high prevalence in bug fixes and can lead to a large amount of noise in the data. Prior research indicates that this noise may alter results. As researchers, we should be skeptics and assume that unvalidated data is likely very noisy, until proven otherwise.Comment: Status: Accepted at Empirical Software Engineerin

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data