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Antibiotic Resistance in Septic Sludge and Receiving Environments of Ho Chi Minh City, Vietnam
No full textApproximately ninety percent of urban Vietnamese households are connected to septic tanks, from which up to three-quarters of the sludge is reported to be dumped into waterways in residential areas. Vietnam has a considerable prevalence of antibiotic misuse and resultant antibiotic-resistant bacteria (ARB) in the human microbiome. Due to the high excretion rate of antibiotics in their parent and similar forms after ingestion, a significant concentration of these pharmaceuticals may exist in wastewater, including septic tank effluents. The dissemination of these drugs into the environment poses a human health risk if pathogenic bacteria become resistant to antibiotic treatments. This study investigated the prevalence of antibiotic-resistant bacteria and genes in areas prone to receiving septic tank sludge in Ho Chi Minh City, Vietnam (HCMC) in winter of 2018. We collected sludge and soil samples (n = 24 and 55, respectively) from residential septic systems and environmental reservoirs (i.e. canals, rivers, and parks) in twelve districts of HCMC. Soil and sludge samples were also tested against a library of 13 antibiotic-resistant genes (ARGs). This analysis revealed that the integrase-1 gene (indicating multiple resistance and anthropogenic impact) was of high prevalence in sludge and soil, as well as genes conferring resistance to sulfonamides, erythromycin, trimethoprim, chloramphenicol, and tetracycline. We quantified concentrations of the fecal indicator bacteria Escherichia coli in soil and found that concentrations did not vary significantly between districts of the city (p > 0.05, one-way ANOVA). The susceptibility of E. coli isolated from sludge and soil (n = 104 and 129, respectively) against nine antibiotics was tested. A total of 65.4% (n = 68) of sludge isolates and 41.9% (n = 54) of soil isolates were found to be multi-drug resistant (phenotypically resistant to three or more classes of antibiotics). The resistance rates of soil and sludge isolates to ampicillin, tetracycline, sulfamethoxazole/trimethoprim, ciprofloxacin, and gentamicin were found to be significantly different between the two sample types (p < 0.05, Fisher’s exact test). In a temporal microcosm study of multidrug- and non-resistant E. coli isolates from our environmental samples, populations of multi-drug resistant bacteria were generally shown to decline at a slower rate than non-resistant strains. Temperature was found to play the most significant role in E. coli inactivation (p < 0.001, three-way ANOVA), followed by the inoculum level of resistance and presence of a background microbial community (p < 0.01, three-way ANOVA)
Differentiable Graph Module (DGM) for Graph Convolutional Networks
No full textGraph deep learning has recently emerged as a powerful ML concept allowing to generalize successful deep neural architectures to non-Euclidean structured data. One of the limitations of the majority of current graph neural network architectures is that they are often restricted to the transductive setting and rely on the assumption that the underlying graph is known and fixed. Often, this assumption is not true since the graph may be noisy, or partially and even completely unknown. In such cases, it would be helpful to infer the graph directly from the data, especially in inductive settings where some nodes were not present in the graph at training time. Furthermore, learning a graph may become an end in itself, as the inferred structure may provide complementary insights next to the downstream task. In this paper, we introduce Differentiable Graph Module (DGM), a learnable function that predicts edge probabilities in the graph which are optimal for the downstream task. DGM can be combined with convolutional graph neural network layers and trained in an end-to-end fashion. We provide an extensive evaluation on applications in healthcare, brain imaging, computer graphics, and computer vision showing a significant improvement over baselines both in transductive and inductive settings
A Multi-Language Comparison of Influences on Author Verification using Character N-Grams
No full textWe create a new multi-language corpus for author verification based on Wikipedia talkpages, and evaluate the influence that differences in topic and time have on character n-gram author profiles. Topic alignment between two texts is found to increase author verification precision, and an authors writing style is found to change over time, but not more significantly after 3 years than after 1 year.Information ArchitectureWISElectrical Engineering, Mathematics and Computer Scienc
Appropriate Similarity Measures for Author Cocitation Analysis
No full textWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
The vanishing author in computer-generated works: a critical analysis of recent Australian case law
No full textAbstract
The use of software is ubiquitous in the creation of many copyright works, yet the requirement in copyright law that every work have a human author who engages in independent intellectual effort means that its use may prevent copyright subsistence. Several recent Australian cases have refocused attention on authorship as an essential criterion of copyright subsistence, and these cases suggest that much computer-produced output may be authorless and thus lack copyright protection. This article, the first in a two-part series, analyses how each case deals with the question of authorship of computer-produced works and why the use of software diminishes copyright protection for a significant number of computer-generated works. The article critiques the application of conventional notions of human authorship developed in the pre-computer age to modern productions and suggests alternative approaches to authorship that satisfy both the major objectives of copyright policy and the need to adapt to the computer age. The article argues that, without a broader judicial approach to authorship of computer-generated works, Parliament must remedy the lacuna in protection for these ‘authorless’ works. Possible solutions for reform are suggested. In a forthcoming article, the author comprehensively examines those reform proposals
Diffusive author(s), cohesive author: Analysis of S/N (1994)
No full textThis study indicates the ways in which various aspects of the author(s) are brought forth in Dumb type’s performance art, the S/N production. Previous research has suggested a non-hierarchical organization of Dumb type and the absence of a “privileged author” in Dumb type’s collaborative work, S/N. However, the results that I have investigated from member’s interviews on the creative process of S/N along with my analysis of the recorded images of S/N, indicate a different aspect of the author(s). First, S/N was created through, so to speak, the collective ideas of the members of Dumb type. Further, S/N has at least nine quotations from previous performances, installations, and printed writings, besides the work-in-progress technique. Explicating one of the “author functions” as given by Michel Foucault, each text has plural subjects of the author. However, it has been revealed from members’ interviews that Teiji Furuhashi had a decision-making role in selecting the members’ ideas within the performance. Since then, S/N has had plural subjects of creation; however, Furuhashi is one of the subjects of creation along with the “privileged author.” S/N has plural authors (diffusive authors) yet at the same time, it has a “privileged author,” Teiji Furuhashi (cohesive author)
From open-vocabulary to vocabulary-free semantic segmentation
No full textOpen-vocabulary semantic segmentation enables models to identify novel object categories beyond their training data. While this flexibility represents a significant advancement, current approaches still rely on manually specified class names as input, creating an inherent bottleneck in real-world applications. This work proposes a Vocabulary-Free Semantic Segmentation pipeline, eliminating the need for predefined class vocabularies. Specifically, we address the chicken-and-egg problem where users need knowledge of all potential objects within a scene to identify them, yet the purpose of segmentation is often to discover these objects. The proposed approach leverages Vision-Language Models to automatically recognize objects and generate appropriate class names, aiming to solve the challenge of class specification and naming quality. Through extensive experiments on several public datasets, we highlight the crucial role of the text encoder in model performance, particularly when the image text classes are paired with generated descriptions. Despite the challenges introduced by the sensitivity of the segmentation text encoder to false negatives within the class tagging process, which adds complexity to the task, we demonstrate that our fully automated pipeline significantly enhances vocabulary-free segmentation accuracy across diverse real-world scenarios. Code is available at https://github.com/klarareichard/open-vocab2free-seg
Latent-Graph Learning for Disease Prediction
No full textRecently, Graph Convolutional Networks (GCNs) have proven to be a powerful machine learning tool for Computer Aided Diagnosis (CADx) and disease prediction. A key component in these models is to build a population graph, where the graph adjacency matrix represents pair-wise patient similarities. Until now, the similarity metrics have been defined manually, usually based on meta-features like demographics or clinical scores. The definition of the metric, however, needs careful tuning, as GCNs are very sensitive to the graph structure. In this paper, we demonstrate for the first time in the CADx domain that it is possible to learn a single, optimal graph towards the GCN’s downstream task of disease classification. To this end, we propose a novel, end-to-end trainable graph learning architecture for dynamic and localized graph pruning. Unlike commonly employed spectral GCN approaches, our GCN is spatial and inductive, and can thus infer previously unseen patients as well. We demonstrate significant classification improvements with our learned graph on two CADx problems in medicine. We further explain and visualize this result using an artificial dataset, underlining the importance of graph learning for more accurate and robust inference with GCNs in medical applications
User friendly graphical interface for workflow management during navigated robotic-assisted keyhole neurosurgery.
No full textRobust segmentation of various anatomies in 3D ultrasound using hough forests and learned data representations
No full text3D ultrasound segmentation is a challenging task due to image artefacts, low signal-to-noise ratio and lack of contrast at anatomical boundaries. Current solutions usually rely on complex, anatomy-specific regularization methods to improve segmentation accuracy. In this work, we propose a highly adaptive learning-based method for fully automatic segmentation of ultrasound volumes. During training, anatomy-specific features are obtained through a sparse auto-encoder. The extracted features are employed in a Hough Forest based framework to retrieve the position of the target anatomy and its segmentation contour. The resulting method is fully automatic, i.e. it does not require any human interaction, and can robustly and automatically adapt to different anatomies yet enforcing appearance and shape constraints.We demonstrate the performance of the method for three different applications: segmentation of midbrain, left ventricle of the heart and prostate
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