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14,710 results on '"Bach, P"'

205. Classifier Calibration with ROC-Regularized Isotonic Regression

Author

Berta, Eugene, Bach, Francis, and Jordan, Michael

Subjects
Computer Science - Machine Learning
Abstract

Calibration of machine learning classifiers is necessary to obtain reliable and interpretable predictions, bridging the gap between model confidence and actual probabilities. One prominent technique, isotonic regression (IR), aims at calibrating binary classifiers by minimizing the cross entropy on a calibration set via monotone transformations. IR acts as an adaptive binning procedure, which allows achieving a calibration error of zero, but leaves open the issue of the effect on performance. In this paper, we first prove that IR preserves the convex hull of the ROC curve -- an essential performance metric for binary classifiers. This ensures that a classifier is calibrated while controlling for overfitting of the calibration set. We then present a novel generalization of isotonic regression to accommodate classifiers with K classes. Our method constructs a multidimensional adaptive binning scheme on the probability simplex, again achieving a multi-class calibration error equal to zero. We regularize this algorithm by imposing a form of monotony that preserves the K-dimensional ROC surface of the classifier. We show empirically that this general monotony criterion is effective in striking a balance between reducing cross entropy loss and avoiding overfitting of the calibration set.

Published
2023

206. Multi-fidelity Bayesian Optimization in Engineering Design

Author

Do, Bach and Zhang, Ruda

Subjects
Computer Science - Computational Engineering, Finance, and Science, Computer Science - Machine Learning, Mathematics - Optimization and Control, Statistics - Machine Learning
Abstract

Resided at the intersection of multi-fidelity optimization (MFO) and Bayesian optimization (BO), MF BO has found a niche in solving expensive engineering design optimization problems, thanks to its advantages in incorporating physical and mathematical understandings of the problems, saving resources, addressing exploitation-exploration trade-off, considering uncertainty, and processing parallel computing. The increasing number of works dedicated to MF BO suggests the need for a comprehensive review of this advanced optimization technique. In this paper, we survey recent developments of two essential ingredients of MF BO: Gaussian process (GP) based MF surrogates and acquisition functions. We first categorize the existing MF modeling methods and MFO strategies to locate MF BO in a large family of surrogate-based optimization and MFO algorithms. We then exploit the common properties shared between the methods from each ingredient of MF BO to describe important GP-based MF surrogate models and review various acquisition functions. By doing so, we expect to provide a structured understanding of MF BO. Finally, we attempt to reveal important aspects that require further research for applications of MF BO in solving intricate yet important design optimization problems, including constrained optimization, high-dimensional optimization, optimization under uncertainty, and multi-objective optimization.

Published
2023

207. Structural-Based Uncertainty in Deep Learning Across Anatomical Scales: Analysis in White Matter Lesion Segmentation

Author

Molchanova, Nataliia, Raina, Vatsal, Malinin, Andrey, La Rosa, Francesco, Depeursinge, Adrien, Gales, Mark, Granziera, Cristina, Muller, Henning, Graziani, Mara, and Cuadra, Meritxell Bach

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper explores uncertainty quantification (UQ) as an indicator of the trustworthiness of automated deep-learning (DL) tools in the context of white matter lesion (WML) segmentation from magnetic resonance imaging (MRI) scans of multiple sclerosis (MS) patients. Our study focuses on two principal aspects of uncertainty in structured output segmentation tasks. Firstly, we postulate that a good uncertainty measure should indicate predictions likely to be incorrect with high uncertainty values. Second, we investigate the merit of quantifying uncertainty at different anatomical scales (voxel, lesion, or patient). We hypothesize that uncertainty at each scale is related to specific types of errors. Our study aims to confirm this relationship by conducting separate analyses for in-domain and out-of-domain settings. Our primary methodological contributions are (i) the development of novel measures for quantifying uncertainty at lesion and patient scales, derived from structural prediction discrepancies, and (ii) the extension of an error retention curve analysis framework to facilitate the evaluation of UQ performance at both lesion and patient scales. The results from a multi-centric MRI dataset of 334 patients demonstrate that our proposed measures more effectively capture model errors at the lesion and patient scales compared to measures that average voxel-scale uncertainty values. We provide the UQ protocols code at https://github.com/Medical-Image-Analysis-Laboratory/MS_WML_uncs., Comment: Preprint submitted to the journal

Published
2023

208. Forall-exist statements in pseudopolynomial time

Author

Bach, Eleonore, Eisenbrand, Friedrich, Rothvoss, Thomas, and Weismantel, Robert

Subjects
Mathematics - Optimization and Control
Abstract

Given a convex set $Q \subseteq R^m$ and an integer matrix $W \in Z^{m \times n}$, we consider statements of the form $ \forall b \in Q \cap Z^m$ $\exists x \in Z^n$ s.t. $Wx \leq b$. Such statements can be verified in polynomial time with the algorithm of Kannan and its improvements if $n$ is fixed and $Q$ is a polyhedron. The running time of the best-known algorithms is doubly exponential in~$n$. In this paper, we provide a pseudopolynomial-time algorithm if $m$ is fixed. Its running time is $(m \Delta)^{O(m^2)}$, where $\Delta = \|W\|_\infty$. Furthermore it applies to general convex sets $Q$.

Published
2023

209. Tunable interfacial chemisorption with atomic-level precision in a graphene WSe2 heterostructure

Author

Zhang, Mo-Han, Gao, Fei, Lorentzen, Aleksander Bach, Ren, Ya-Ning, Zhang, Ruo-Han, Zhou, Xiao-Feng, Dong, Rui, Gao, Shi-Wu, Brandbyge, Mads, and He, Lin

Subjects
Condensed Matter - Materials Science, Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

It has long been an ultimate goal to introduce chemical doping at the atomic level to precisely tune properties of materials. Two-dimensional materials have natural advantage because of its highly-exposed surface atoms, however, it is still a grand challenge to achieve this goal experimentally. Here, we demonstrate the ability to introduce chemical doping in graphene with atomic-level precision by controlling chemical adsorption of individual Se atoms, which are extracted from the underneath WSe2, at the interface of graphene-WSe2 heterostructures. Our scanning tunneling microscopy (STM) measurements, combined with first-principles calculations, reveal that individual Se atoms can chemisorbed on three possible positions in graphene, which generate distinct pseudospin-mediated atomic-scale vortices in graphene. We demonstrate that the chemisorbed positions of individual Se atoms can be manipulated by STM tip, which enables us to achieve atomic-scale controlling quantum interference of the pseudospin-mediated vortices in graphene. This result offers the promise of controlling properties of materials through chemical doping with atomic-level precision.

Published
2023

210. Follow-Up Differential Descriptions: Language Models Resolve Ambiguities for Image Classification

Author

Esfandiarpoor, Reza and Bach, Stephen H.

Subjects
Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

A promising approach for improving the performance of vision-language models like CLIP for image classification is to extend the class descriptions (i.e., prompts) with related attributes, e.g., using brown sparrow instead of sparrow. However, current zero-shot methods select a subset of attributes regardless of commonalities between the target classes, potentially providing no useful information that would have helped to distinguish between them. For instance, they may use color instead of bill shape to distinguish between sparrows and wrens, which are both brown. We propose Follow-up Differential Descriptions (FuDD), a zero-shot approach that tailors the class descriptions to each dataset and leads to additional attributes that better differentiate the target classes. FuDD first identifies the ambiguous classes for each image, and then uses a Large Language Model (LLM) to generate new class descriptions that differentiate between them. The new class descriptions resolve the initial ambiguity and help predict the correct label. In our experiments, FuDD consistently outperforms generic description ensembles and naive LLM-generated descriptions on 12 datasets. We show that differential descriptions are an effective tool to resolve class ambiguities, which otherwise significantly degrade the performance. We also show that high quality natural language class descriptions produced by FuDD result in comparable performance to few-shot adaptation methods., Comment: ICLR 2024

Published
2023

211. Word Definitions from Large Language Models

Author

Pham, Bach, Wong, JuiHsuan, Kim, Samuel, Yin, Yunting, and Skiena, Steven

Subjects
Computer Science - Computation and Language
Abstract

Dictionary definitions are historically the arbitrator of what words mean, but this primacy has come under threat by recent progress in NLP, including word embeddings and generative models like ChatGPT. We present an exploratory study of the degree of alignment between word definitions from classical dictionaries and these newer computational artifacts. Specifically, we compare definitions from three published dictionaries to those generated from variants of ChatGPT. We show that (i) definitions from different traditional dictionaries exhibit more surface form similarity than do model-generated definitions, (ii) that the ChatGPT definitions are highly accurate, comparable to traditional dictionaries, and (iii) ChatGPT-based embedding definitions retain their accuracy even on low frequency words, much better than GloVE and FastText word embeddings.

Published
2023

212. FetMRQC: a robust quality control system for multi-centric fetal brain MRI

Author

Sanchez, Thomas, Esteban, Oscar, Gomez, Yvan, Pron, Alexandre, Koob, Mériam, Dunet, Vincent, Girard, Nadine, Jakab, Andras, Eixarch, Elisenda, Auzias, Guillaume, and Cuadra, Meritxell Bach

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Machine Learning
Abstract

Fetal brain MRI is becoming an increasingly relevant complement to neurosonography for perinatal diagnosis, allowing fundamental insights into fetal brain development throughout gestation. However, uncontrolled fetal motion and heterogeneity in acquisition protocols lead to data of variable quality, potentially biasing the outcome of subsequent studies. We present FetMRQC, an open-source machine-learning framework for automated image quality assessment and quality control that is robust to domain shifts induced by the heterogeneity of clinical data. FetMRQC extracts an ensemble of quality metrics from unprocessed anatomical MRI and combines them to predict experts' ratings using random forests. We validate our framework on a pioneeringly large and diverse dataset of more than 1600 manually rated fetal brain T2-weighted images from four clinical centers and 13 different scanners. Our study shows that FetMRQC's predictions generalize well to unseen data while being interpretable. FetMRQC is a step towards more robust fetal brain neuroimaging, which has the potential to shed new insights on the developing human brain., Comment: 24 pages, 10 Figures. Accepted for publication at Medical Image Analysis

Published
2023
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213. On the Impact of Overparameterization on the Training of a Shallow Neural Network in High Dimensions

Author

Martin, Simon, Bach, Francis, and Biroli, Giulio

Subjects
Mathematics - Optimization and Control, Condensed Matter - Disordered Systems and Neural Networks, Statistics - Machine Learning
Abstract

We study the training dynamics of a shallow neural network with quadratic activation functions and quadratic cost in a teacher-student setup. In line with previous works on the same neural architecture, the optimization is performed following the gradient flow on the population risk, where the average over data points is replaced by the expectation over their distribution, assumed to be Gaussian.We first derive convergence properties for the gradient flow and quantify the overparameterization that is necessary to achieve a strong signal recovery. Then, assuming that the teachers and the students at initialization form independent orthonormal families, we derive a high-dimensional limit for the flow and show that the minimal overparameterization is sufficient for strong recovery. We verify by numerical experiments that these results hold for more general initializations.

Published
2023

214. Local differential privacy in survival analysis using private failure indicators

Author

Maxime, Egea and Mikael, Escobar-Bach

Subjects
Mathematics - Statistics Theory, Statistics - Methodology
Abstract

We consider the estimation of the cumulative hazard function, and equivalently the distribution function, with censored data under a setup that preserves the privacy of the survival database. This is done through a $\alpha$-locally differentially private mechanism for the failure indicators and by proposing a non-parametric kernel estimator for the cumulative hazard function that remains consistent under the privatization. Under mild conditions, we also prove lowers bounds for the minimax rates of convergence and show that estimator is minimax optimal under a well-chosen bandwidth.

Published
2023

215. NetPanorama: A Declarative Grammar for Network Construction, Transformation, and Visualization

Author

Scott-Brown, James and Bach, Benjamin

Subjects
Computer Science - Human-Computer Interaction
Abstract

This paper introduces NetPanorama, a domain-specific language and declarative grammar for interactive network visualizations. Exploring complex networks with multivariate, geographical, or temporal information often require bespoke visualization designs, such as adjacency matrices, arc-diagrams, small multiples, timelines, or geographic map visualizations. However, creating these requires implementing data loading, data transformations, visualization, and interactivity, which is time-consuming and slows down the iterative exploration of this huge design space. With NetPanorama, a developer specifies a network visualization design as a pipeline of parameterizable steps. Our specification and reference implementation aims to facilitate visualization development and reuse; allow for easy design exploration and iteration; and make data transformation and visual mapping decisions transparent. Documentation, source code, examples, and an interactive online editor can be found online: https://netpanorama.netlify.app/

Published
2023

216. Symmetry-preserving graph attention network to solve routing problems at multiple resolutions

Author

Tran, Cong Dao, Bach, Thong, and Hy, Truong Son

Subjects
Computer Science - Machine Learning
Abstract

Travelling Salesperson Problems (TSPs) and Vehicle Routing Problems (VRPs) have achieved reasonable improvement in accuracy and computation time with the adaptation of Machine Learning (ML) methods. However, none of the previous works completely respects the symmetries arising from TSPs and VRPs including rotation, translation, permutation, and scaling. In this work, we introduce the first-ever completely equivariant model and training to solve combinatorial problems. Furthermore, it is essential to capture the multiscale structure (i.e. from local to global information) of the input graph, especially for the cases of large and long-range graphs, while previous methods are limited to extracting only local information that can lead to a local or sub-optimal solution. To tackle the above limitation, we propose a Multiresolution scheme in combination with Equivariant Graph Attention network (mEGAT) architecture, which can learn the optimal route based on low-level and high-level graph resolutions in an efficient way. In particular, our approach constructs a hierarchy of coarse-graining graphs from the input graph, in which we try to solve the routing problems on simple low-level graphs first, then utilize that knowledge for the more complex high-level graphs. Experimentally, we have shown that our model outperforms existing baselines and proved that symmetry preservation and multiresolution are important recipes for solving combinatorial problems in a data-driven manner. Our source code is publicly available at https://github.com/HySonLab/Multires-NP-hard

Published
2023

217. Show Me My Users: A Dashboard Visualizing User Interaction Logs

Author

Wang, Jinrui, AlKadi, Mashael, and Bach, Benjamin

Subjects
Computer Science - Human-Computer Interaction
Abstract

This paper describes the design of a dashboard and analysis pipeline to monitor users of visualization tools in the wild. Our pipeline describes how to extract analytical KPIs from extensive log event data involving a mix of user types. The resulting three-page dashboard displays live KPIs, helping analysts understand users, detect exploratory behaviors, plan education interventions, and improve tool features. We propose this case study as a motivation to use the dashboard approach for a more `casual' monitoring of users and building carer mindsets for visualization tools., Comment: Accepted by the IEEE VIS conference (Melbourne, VIC), 2023

Published
2023

218. Regularization properties of adversarially-trained linear regression

Author

Ribeiro, Antônio H., Zachariah, Dave, Bach, Francis, and Schön, Thomas B.

Subjects
Statistics - Machine Learning, Computer Science - Cryptography and Security, Computer Science - Machine Learning, Mathematics - Optimization and Control
Abstract

State-of-the-art machine learning models can be vulnerable to very small input perturbations that are adversarially constructed. Adversarial training is an effective approach to defend against it. Formulated as a min-max problem, it searches for the best solution when the training data were corrupted by the worst-case attacks. Linear models are among the simple models where vulnerabilities can be observed and are the focus of our study. In this case, adversarial training leads to a convex optimization problem which can be formulated as the minimization of a finite sum. We provide a comparative analysis between the solution of adversarial training in linear regression and other regularization methods. Our main findings are that: (A) Adversarial training yields the minimum-norm interpolating solution in the overparameterized regime (more parameters than data), as long as the maximum disturbance radius is smaller than a threshold. And, conversely, the minimum-norm interpolator is the solution to adversarial training with a given radius. (B) Adversarial training can be equivalent to parameter shrinking methods (ridge regression and Lasso). This happens in the underparametrized region, for an appropriate choice of adversarial radius and zero-mean symmetrically distributed covariates. (C) For $\ell_\infty$-adversarial training -- as in square-root Lasso -- the choice of adversarial radius for optimal bounds does not depend on the additive noise variance. We confirm our theoretical findings with numerical examples., Comment: Accepted (spotlight) NeurIPS 2023; A preliminary version of this work titled: "Surprises in adversarially-trained linear regression" was made available under a different identifier: arXiv:2205.12695

Published
2023

219. How Good is ChatGPT in Giving Advice on Your Visualization Design?

Author

Kim, Nam Wook, Myers, Grace, and Bach, Benjamin

Subjects
Computer Science - Human-Computer Interaction
Abstract

Data visualization practitioners often lack formal training, resulting in a knowledge gap in visualization design best practices. Large-language models like ChatGPT, with their vast internet-scale training data, offer transformative potential in addressing this gap. To explore this potential, we adopted a mixed-method approach. Initially, we analyzed the VisGuide forum, a repository of data visualization questions, by comparing ChatGPT-generated responses to human replies. Subsequently, our user study delved into practitioners' reactions and attitudes toward ChatGPT as a visualization assistant. Participants, who brought their visualizations and questions, received feedback from both human experts and ChatGPT in a randomized order. They filled out experience surveys and shared deeper insights through post-interviews. The results highlight the unique advantages and disadvantages of ChatGPT, such as its ability to quickly provide a wide range of design options based on a broad knowledge base, while also revealing its limitations in terms of depth and critical thinking capabilities., Comment: 24 pages, 4 figures

Published
2023

220. Just-in-Time Flaky Test Detection via Abstracted Failure Symptom Matching

Author

An, Gabin, Yoon, Juyeon, Bach, Thomas, Hong, Jingun, and Yoo, Shin

Subjects
Computer Science - Software Engineering
Abstract

We report our experience of using failure symptoms, such as error messages or stack traces, to identify flaky test failures in a Continuous Integration (CI) pipeline for a large industrial software system, SAP HANA. Although failure symptoms are commonly used to identify similar failures, they have not previously been employed to detect flaky test failures. Our hypothesis is that flaky failures will exhibit symptoms distinct from those of non-flaky failures. Consequently, we can identify recurring flaky failures, without rerunning the tests, by matching the failure symptoms to those of historical flaky runs. This can significantly reduce the need for test reruns, ultimately resulting in faster delivery of test results to developers. To facilitate the process of matching flaky failures across different execution instances, we abstract newer test failure symptoms before matching them to the known patterns of flaky failures, inspired by previous research in the fields of failure deduplication and log analysis. We evaluate our symptom-based flakiness detection method using actual failure symptoms gathered from CI data of SAP HANA during a six-month period. Our method shows the potential of using failure symptoms to identify recurring flaky failures, achieving a precision of at least 96%, while saving approximately 58% of the machine time compared to the traditional rerun strategy. Analysis of the false positives and the feedback from developers underscore the importance of having descriptive and informative failure symptoms for both the effective deployment of this symptom-based approach and the debugging of flaky tests., Comment: 10 pages

Published
2023

221. Prosody Analysis of Audiobooks

Author

Pethe, Charuta, Pham, Bach, Childress, Felix D, Yin, Yunting, and Skiena, Steven

Subjects
Computer Science - Sound, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Recent advances in text-to-speech have made it possible to generate natural-sounding audio from text. However, audiobook narrations involve dramatic vocalizations and intonations by the reader, with greater reliance on emotions, dialogues, and descriptions in the narrative. Using our dataset of 93 aligned book-audiobook pairs, we present improved models for prosody prediction properties (pitch, volume, and rate of speech) from narrative text using language modeling. Our predicted prosody attributes correlate much better with human audiobook readings than results from a state-of-the-art commercial TTS system: our predicted pitch shows a higher correlation with human reading for 22 out of the 24 books, while our predicted volume attribute proves more similar to human reading for 23 out of the 24 books. Finally, we present a human evaluation study to quantify the extent that people prefer prosody-enhanced audiobook readings over commercial text-to-speech systems.

Published
2023

222. Augmenting Static Visualizations with PapARVis Designer

Author

Zhu-Tian, Chen, Tong, Wai, Wang, Qianwen, Bach, Benjamin, and Qu, Huamin

Subjects
Computer Science - Human-Computer Interaction
Abstract

This paper presents an authoring environment for augmenting static visualizations with virtual content in augmented reality. Augmenting static visualizations can leverage the best of both physical and digital worlds, but its creation currently involves different tools and devices, without any means to explicitly design and debug both static and virtual content simultaneously. To address these issues, we design an environment that seamlessly integrates all steps of a design and deployment workflow through its main features: i) an extension to Vega, ii) a preview, and iii) debug hints that facilitate valid combinations of static and augmented content. We inform our design through a design space with four ways to augment static visualizations. We demonstrate the expressiveness of our tool through examples, including books, posters, projections, wall-sized visualizations. A user study shows high user satisfaction of our environment and confirms that participants can create augmented visualizations in an average of 4.63 minutes.

Published
2023
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223. Unpacking Human-AI Interaction in Safety-Critical Industries: A Systematic Literature Review

Author

Bach, Tita A., Kristiansen, Jenny K., Babic, Aleksandar, and Jacovi, Alon

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Artificial Intelligence
Abstract

Ensuring quality human-AI interaction (HAII) in safety-critical industries is essential. Failure to do so can lead to catastrophic and deadly consequences. Despite this urgency, existing research on HAII is limited, fragmented, and inconsistent. We present here a survey of that literature and recommendations for research best practices that should improve the field. We divided our investigation into the following areas: 1) terms used to describe HAII, 2) primary roles of AI-enabled systems, 3) factors that influence HAII, and 4) how HAII is measured. Additionally, we described the capabilities and maturity of the AI-enabled systems used in safety-critical industries discussed in these articles. We found that no single term is used across the literature to describe HAII and some terms have multiple meanings. According to our literature, seven factors influence HAII: user characteristics (e.g., user personality), user perceptions and attitudes (e.g., user biases), user expectations and experience (e.g., mismatched user expectations and experience), AI interface and features (e.g., interactive design), AI output (e.g., perceived accuracy), explainability and interpretability (e.g., level of detail, user understanding), and usage of AI (e.g., heterogeneity of environments). HAII is most measured with user-related subjective metrics (e.g., user perceptions, trust, and attitudes), and AI-assisted decision-making is the most common primary role of AI-enabled systems. Based on this review, we conclude that there are substantial research gaps in HAII. Researchers and developers need to codify HAII terminology, involve users throughout the AI lifecycle (especially during development), and tailor HAII in safety-critical industries to the users and environments., Comment: Accepted for publication in IEEE Access

Published
2023
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224. Variational Gaussian approximation of the Kushner optimal filter

Author

Lambert, Marc, Bonnabel, Silvère, and Bach, Francis

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

In estimation theory, the Kushner equation provides the evolution of the probability density of the state of a dynamical system given continuous-time observations. Building upon our recent work, we propose a new way to approximate the solution of the Kushner equation through tractable variational Gaussian approximations of two proximal losses associated with the propagation and Bayesian update of the probability density. The first is a proximal loss based on the Wasserstein metric and the second is a proximal loss based on the Fisher metric. The solution to this last proximal loss is given by implicit updates on the mean and covariance that we proposed earlier. These two variational updates can be fused and shown to satisfy a set of stochastic differential equations on the Gaussian's mean and covariance matrix. This Gaussian flow is consistent with the Kalman-Bucy and Riccati flows in the linear case and generalize them in the nonlinear one., Comment: Lecture Notes in Computer Science, 2023

Published
2023
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225. Low-Resource Languages Jailbreak GPT-4

Author

Yong, Zheng-Xin, Menghini, Cristina, and Bach, Stephen H.

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract

AI safety training and red-teaming of large language models (LLMs) are measures to mitigate the generation of unsafe content. Our work exposes the inherent cross-lingual vulnerability of these safety mechanisms, resulting from the linguistic inequality of safety training data, by successfully circumventing GPT-4's safeguard through translating unsafe English inputs into low-resource languages. On the AdvBenchmark, GPT-4 engages with the unsafe translated inputs and provides actionable items that can get the users towards their harmful goals 79% of the time, which is on par with or even surpassing state-of-the-art jailbreaking attacks. Other high-/mid-resource languages have significantly lower attack success rate, which suggests that the cross-lingual vulnerability mainly applies to low-resource languages. Previously, limited training on low-resource languages primarily affects speakers of those languages, causing technological disparities. However, our work highlights a crucial shift: this deficiency now poses a risk to all LLMs users. Publicly available translation APIs enable anyone to exploit LLMs' safety vulnerabilities. Therefore, our work calls for a more holistic red-teaming efforts to develop robust multilingual safeguards with wide language coverage., Comment: NeurIPS Workshop on Socially Responsible Language Modelling Research (SoLaR) 2023. Best Paper Award

Published
2023

226. Types and Semantics for Extensible Data Types (Extended Version)

Author

van der Rest, Cas and Poulsen, Casper Bach

Subjects
Computer Science - Programming Languages
Abstract

Developing and maintaining software commonly requires (1) adding new data type constructors to existing applications, but also (2) adding new functions that work on existing data. Most programming languages have native support for defining data types and functions in a way that supports either (1) or (2), but not both. This lack of native support makes it difficult to use and extend libraries. A theoretically well-studied solution is to define data types and functions using initial algebra semantics. While it is possible to encode this solution in existing programming languages, such encodings add syntactic and interpretive overhead, and commonly fail to take advantage of the map and fold fusion laws of initial algebras which compilers could exploit to generate more efficient code. A solution to these is to provide native support for initial algebra semantics. In this paper, we develop such a solution and present a type discipline and core calculus for a language with native support for initial algebra semantics., Comment: Extended version (28 pages) of the eponymous paper to appear in the conference proceedings of APLAS 2023

Published
2023

227. TELAMON: Effelsberg Monitoring of AGN Jets with Very-High-Energy Astroparticle Emissions -- Polarization properties

Author

Heßdörfer, J., Kadler, M., Benke, P., Debbrecht, L., Eich, J., Eppel, F., Gokus, A., Hämmerich, S., Kirchner, D., Paraschos, G. F., Rösch, F., Schulga, W., Sinapius, J., Weber, P., Bach, U., Berge, D., Buson, S., Dorner, D., Edwards, P. G., Fromm, C. M., Giroletti, M., Hervet, O., Kappes, A., Koyama, S., Kraus, A., Krichbaum, T. P., Lindfors, E., Mannheim, K., Ojha, R., Pueschel, E., Ros, E., Schleicher, B., Sitarek, J., Wilms, J., Zacharias, M., and Zensus, J. A.

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

We present recent results of the TELAMON program, which is using the Effelsberg 100-m telescope to monitor the radio spectra of active galactic nuclei (AGN) under scrutiny in astroparticle physics, namely TeV blazars and neutrino-associated AGN. Our sample includes all known Northern TeV-emitting blazars as well as blazars positionally coincident with IceCube neutrino alerts. Polarization can give additional insight into the source properties, as the polarized emission is often found to vary on different timescales and amplitudes than the total intensity emission. Here, we present an overview of the polarization properties of the TeV-emitting TELAMON sources at four frequencies in the 20 mm and 7 mm bands. While at 7 mm roughly $82\,\%$ of all observed sources are found to be significantly polarized, for 20 mm the percentage is $\sim58\,\%$. We find that most of the sources exhibit mean fractional polarizations of $<5\%$, matching the expectations of rather low polarization levels in these sources from previous studies at lower radio frequencies. Nevertheless, we demonstrate examples of how the polarized emission can provide additional information over the total intensity., Comment: 9 pages, 2 figures, Proceedings of the 38th International Cosmic Ray Conference (ICRC2023)

Published
2023

228. FisheyePP4AV: A privacy-preserving method for autonomous vehicles on fisheye camera images

Author

Trinh, Linh, Ha, Bach, and Tran, Tu

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

In many parts of the world, the use of vast amounts of data collected on public roadways for autonomous driving has increased. In order to detect and anonymize pedestrian faces and nearby car license plates in actual road-driving scenarios, there is an urgent need for effective solutions. As more data is collected, privacy concerns regarding it increase, including but not limited to pedestrian faces and surrounding vehicle license plates. Normal and fisheye cameras are the two common camera types that are typically mounted on collection vehicles. With complex camera distortion models, fisheye camera images were deformed in contrast to regular images. It causes computer vision tasks to perform poorly when using numerous deep learning models. In this work, we pay particular attention to protecting privacy while yet adhering to several laws for fisheye camera photos taken by driverless vehicles. First, we suggest a framework for extracting face and plate identification knowledge from several teacher models. Our second suggestion is to transform both the image and the label from a regular image to fisheye-like data using a varied and realistic fisheye transformation. Finally, we run a test using the open-source PP4AV dataset. The experimental findings demonstrated that our model outperformed baseline methods when trained on data from autonomous vehicles, even when the data were softly labeled. The implementation code is available at our github: https://github.com/khaclinh/FisheyePP4AV.

Published
2023

229. Dependent censoring with simultaneous death times based on the Generalized Marshall-Olkin model

Author

Escobar-Bach, Mikael and Helali, Salima

Subjects
Mathematics - Statistics Theory
Abstract

In this paper, we considered the problem of dependent censoring models with a positive probability that the times of failure are equal. In this context, we proposed to consider the Marshall-Olkin type model and studied some properties of the associated survival copula in its application to censored data. We also introduced estimators for the marginal distributions and the joint survival probabilities under different schemes and showed their asymptotic normality under appropriate conditions. Finally, we evaluated the finite-sample performance of our approach relying on a small simulation study on synthetic data, and an application to real data.

Published
2023

230. Testing for sufficient follow-up in censored survival data by using extremes

Author

Xie, Ping, Escobar-Bach, Mikael, and Van Keilegom, Ingrid

Subjects
Statistics - Methodology
Abstract

In survival analysis, it often happens that some individuals, referred to as cured individuals, never experience the event of interest. When analyzing time-to-event data with a cure fraction, it is crucial to check the assumption of `sufficient follow-up', which means that the right extreme of the censoring time distribution is larger than that of the survival time distribution for the non-cured individuals. However, the available methods to test this assumption are limited in the literature. In this article, we study the problem of testing whether follow-up is sufficient for light-tailed distributions and develop a simple novel test. The proposed test statistic compares an estimator of the non-cure proportion under sufficient follow-up to one without the assumption of sufficient follow-up. A bootstrap procedure is employed to approximate the critical values of the test. We also carry out extensive simulations to evaluate the finite sample performance of the test and illustrate the practical use with applications to leukemia and breast cancer datasets., Comment: 16 pages, 2 figures and 4 tables are added

Published
2023

231. The importance of considering competing risks in recurrence analysis of intracranial meningioma.

Author

Mirian, Christian, Jensen, Lasse, Juratli, Tareq, Maier, Andrea, Torp, Sverre, Shih, Helen, Morshed, Ramin, Young, Jacob, Magill, Stephen, Bertero, Luca, Stummer, Walter, Spille, Dorothee, Brokinkel, Benjamin, Oya, Soichi, Miyawaki, Satoru, Saito, Nobuhito, Proescholdt, Martin, Kuroi, Yasuhiro, Gousias, Konstantinos, Simon, Matthias, Moliterno, Jennifer, Prat-Acin, Ricardo, Goutagny, Stéphane, Prabhu, Vikram, Tsiang, John, Wach, Johannes, Güresir, Erdem, Yamamoto, Junkoh, Kim, Young, Lee, Joo, Koshy, Matthew, Perumal, Karthikeyan, Baskaya, Mustafa, Cannon, Donald, Shrieve, Dennis, Suh, Chang-Ok, Chang, Jong, Kamenova, Maria, Straumann, Sven, Soleman, Jehuda, Eyüpoglu, Ilker, Catalan, Tony, Lui, Austin, Wang, Fang, Guo, Fuyou, Góes, Pedro, de Paiva Neto, Manoel, Jamshidi, Aria, Komotar, Ricardo, Ivan, Michael, Luther, Evan, Souhami, Luis, Guiot, Marie-Christine, Csonka, Tamás, Endo, Toshiki, Barrett, Olivia, Jensen, Randy, Gupta, Tejpal, Patel, Akash, Klisch, Tiemo, Kim, Jun, Maiuri, Francesco, Barresi, Valeria, Tabernero, María, Skyrman, Simon, Broechner, Anders, Bach, Mathias, Law, Ian, Scheie, David, Kristensen, Bjarne, Munch, Tina, Meling, Torstein, Fugleholm, Kåre, Blanche, Paul, Mathiesen, Tiit, McDermott, Mike, and Theodosopoulos, Philip

Subjects
Competing risk, Meningioma, Neuro-oncology, Recurrence, Humans, Aged, Meningioma, Meningeal Neoplasms, Neoplasm Recurrence, Local, Retrospective Studies, Risk Assessment
Abstract

BACKGROUND: The risk of recurrence is overestimated by the Kaplan-Meier method when competing events, such as death without recurrence, are present. Such overestimation can be avoided by using the Aalen-Johansen method, which is a direct extension of Kaplan-Meier that accounts for competing events. Meningiomas commonly occur in older individuals and have slow-growing properties, thereby warranting competing risk analysis. The extent to which competing events are considered in meningioma literature is unknown, and the consequences of using incorrect methodologies in meningioma recurrence risk analysis have not been investigated. METHODS: We surveyed articles indexed on PubMed since 2020 to assess the usage of competing risk analysis in recent meningioma literature. To compare recurrence risk estimates obtained through Kaplan-Meier and Aalen-Johansen methods, we applied our international database comprising ~ 8,000 patients with a primary meningioma collected from 42 institutions. RESULTS: Of 513 articles, 169 were eligible for full-text screening. There were 6,537 eligible cases from our PERNS database. The discrepancy between the results obtained by Kaplan-Meier and Aalen-Johansen was negligible among low-grade lesions and younger individuals. The discrepancy increased substantially in the patient groups associated with higher rates of competing events (older patients with high-grade lesions). CONCLUSION: The importance of considering competing events in recurrence risk analysis is poorly recognized as only 6% of the studies we surveyed employed Aalen-Johansen analyses. Consequently, most of the previous literature has overestimated the risk of recurrence. The overestimation was negligible for studies involving low-grade lesions in younger individuals; however, overestimation might have been substantial for studies on high-grade lesions.

Published
2024

232. Eruptive Melanocytic Nevi in the Setting of Encorafenib, Cetuximab, and Binimetinib Combination Therapy: A Case Report.

Author

Lam, Karen, Gates, Gregory, Bach, Daniel, and Cheng, Kyle

Subjects
BRAF V600E, BRAF inhibitor, Binimetinib, Case report, Cetuximab, EGFR inhibitor, Encorafenib, Eruptive melanocytic nevi, MEK inhibitor
Abstract

INTRODUCTION: The development of new and changing melanocytic lesions has been increasingly reported as an adverse dermatologic toxicity of BRAF inhibitor therapy. Melanocytic lesions and melanomas induced by BRAF inhibitor therapy that lack BRAF V600E expression have been less commonly described. One mechanism that has been proposed for the development of BRAF inhibitor-induced melanocytic lesions, including those lacking BRAF V600E expression, is the paradoxical activation of the MAPK signaling pathway in BRAF wild-type (BRAFWT) cells. CASE PRESENTATION: Herein, we report a rare case of a 39-year-old woman who developed numerous BRAF V600E-negative eruptive melanocytic nevi following encorafenib, cetuximab, and binimetinib combination therapy, the current standard of care for the treatment of BRAF-mutant metastatic colorectal cancer. CONCLUSION: Patients treated with BRAF inhibitors, with or without related combination therapies, who develop BRAFWT melanocytic lesions are at risk for developing both dysplastic nevi and melanoma, thereby warranting baseline dermatoscopic evaluation prior to the initiation of therapy as well as regular follow-up during and after treatment.

Published
2024

233. Feelings and Actions in Threatening Virtual Reality Environments

Author

Kornemann, Lukas, Serratos Hernandez, Ulises Daniel, Brochard, Jules, and Bach, Dominik R.

Subjects
Psychology, Emotion
Abstract

Virtual Reality (VR) can offer insights into realistic human defensive behavior. In the present work, we sought to elucidate the interplay between feelings and actions in VR-simulated threatening scenarios. Participants (n = 30) encountered various animal threats in VR during a fruit collection task. We retrospectively assessed participants' feelings after each episode on several dimensions, namely valence, arousal, potency, surprise, and anxiety. As predictor variables, we included scenario characteristics, behavioral responses, and personality traits. Our results indicate that the primary determinants for subjective feelings except potency were ultimate survival, the availability of self-defense weapons, and the animals' behavior (attack or not). No strong determinants for potency could be found. Notably, participants' behavioral responses did not independently influence feelings reported later. These findings highlight VR's potential in expanding our understanding of subjective feelings in threatening situations. Our research suggests that behavior and feelings in defensive situations might not be closely linked.

Published
2024

235. Measurement of the Bs0→J/ψKS0s effective lifetime from proton-proton collisions at Bs0→J/ψKS0s = 13 TeV

Author

Hayrapetyan, A., Tumasyan, A., Adam, W., Andrejkovic, J. W., Bergauer, T., Chatterjee, S., Damanakis, K., Dragicevic, M., Hussain, P. S., Jeitler, M., Krammer, N., Li, A., Liko, D., Mikulec, I., Schieck, J., Schöfbeck, R., Schwarz, D., Sonawane, M., Waltenberger, W., Wulz, C.-E., Janssen, T., Van Laer, T., Van Mechelen, P., Breugelmans, N., D’Hondt, J., Dansana, S., De Moor, A., Delcourt, M., Heyen, F., Lowette, S., Makarenko, I., Müller, D., Tavernier, S., Tytgat, M., Van Onsem, G. P., Van Putte, S., Vannerom, D., Bilin, B., Clerbaux, B., Das, A. K., De Lentdecker, G., Evard, H., Favart, L., Gianneios, P., Jaramillo, J., Khalilzadeh, A., Khan, F. A., Lee, K., Mahdavikhorrami, M., Malara, A., Paredes, S., Shahzad, M. A., Thomas, L., Vanden Bemden, M., Vander Velde, C., Vanlaer, P., De Coen, M., Dobur, D., Gokbulut, G., Hong, Y., Knolle, J., Lambrecht, L., Marckx, D., Mota Amarilo, K., Samalan, A., Skovpen, K., Van Den Bossche, N., van der Linden, J., Wezenbeek, L., Benecke, A., Bethani, A., Bruno, G., Caputo, C., De Favereau De Jeneret, J., Delaere, C., Donertas, I. S., Giammanco, A., Guzel, A. O., Jain, Sa., Lemaitre, V., Lidrych, J., Mastrapasqua, P., Tran, T. T., Wertz, S., Alves, G. A., Alves Gallo Pereira, M., Coelho, E., Correia Silva, G., Hensel, C., Menezes De Oliveira, T., Mora Herrera, C., Moraes, A., Rebello Teles, P., Soeiro, M., Vilela Pereira, A., Aldá Júnior, W. L., Barroso Ferreira Filho, M., Brandao Malbouisson, H., Carvalho, W., Chinellato, J., Da Costa, E. M., Da Silveira, G. G., De Jesus Damiao, D., Fonseca De Souza, S., Gomes De Souza, R., Macedo, M., Martins, J., Mundim, L., Nogima, H., Pinheiro, J. P., Santoro, A., Sznajder, A., Thiel, M., Bernardes, C. A., Calligaris, L., Fernandez Perez Tomei, T. R., Gregores, E. M., Maietto Silverio, I., Mercadante, P. G., Novaes, S. F., Orzari, B., Padula, Sandra S., Aleksandrov, A., Antchev, G., Hadjiiska, R., Iaydjiev, P., Misheva, M., Shopova, M., Sultanov, G., Dimitrov, A., Litov, L., Pavlov, B., Petkov, P., Petrov, A., Shumka, E., Keshri, S., Laroze, D., Thakur, S., Cheng, T., Javaid, T., Yuan, L., Hu, Z., Liang, Z., Liu, J., Yi, K., Chen, G. M., Chen, H. S., Chen, M., Iemmi, F., Jiang, C. H., Kapoor, A., Liao, H., Liu, Z.-A., Sharma, R., Song, J. N., Tao, J., Wang, C., Wang, J., Wang, Z., Zhang, H., Zhao, J., Agapitos, A., Ban, Y., Deng, S., Guo, B., Jiang, C., Levin, A., Li, C., Li, Q., Mao, Y., Qian, S., Qian, S. J., Qin, X., Sun, X., Wang, D., Yang, H., Zhang, L., Zhao, Y., Zhou, C., Yang, S., You, Z., Jaffel, K., Lu, N., Bauer, G., Li, B., Zhang, J., Gao, X., Li, Y., Lin, Z., Lu, C., Xiao, M., Avila, C., Barbosa Trujillo, D. A., Cabrera, A., Florez, C., Fraga, J., Reyes Vega, J. A., Ramirez, F., Rendón, C., Rodriguez, M., Ruales Barbosa, A. A., Ruiz Alvarez, J. D., Giljanovic, D., Godinovic, N., Lelas, D., Sculac, A., Kovac, M., Petkovic, A., Sculac, T., Bargassa, P., Brigljevic, V., Chitroda, B. K., Ferencek, D., Jakovcic, K., Starodumov, A., Susa, T., Attikis, A., Christoforou, K., Hadjiagapiou, A., Leonidou, C., Mousa, J., Nicolaou, C., Paizanos, L., Ptochos, F., Razis, P. A., Rykaczewski, H., Saka, H., Stepennov, A., Finger, M., Finger, Jr., M., Kveton, A., Carrera Jarrin, E., Abdelalim, A. A., Elgammal, S., Ellithi Kamel, A., Lotfy, A., Mahmoud, M. A., Ehataht, K., Kadastik, M., Lange, T., Nandan, S., Nielsen, C., Pata, J., Raidal, M., Tani, L., Veelken, C., Kirschenmann, H., Osterberg, K., Voutilainen, M., Bharthuar, S., Bin Norjoharuddeen, N., Brücken, E., Garcia, F., Inkaew, P., Kallonen, K. T. S., Lampén, T., Lassila-Perini, K., Lehti, S., Lindén, T., Martikainen, L., Myllymäki, M., Rantanen, M. m., Siikonen, H., Tuominiemi, J., Luukka, P., Petrow, H., Besancon, M., Couderc, F., Dejardin, M., Denegri, D., Faure, J. L., Ferri, F., Ganjour, S., Gras, P., Hamel de Monchenault, G., Kumar, M., Lohezic, V., Malcles, J., Orlandi, F., Portales, L., Rosowsky, A., Sahin, M. Ö., Savoy-Navarro, A., Simkina, P., Titov, M., Tornago, M., Beaudette, F., Boldrini, G., Busson, P., Cappati, A., Charlot, C., Chiusi, M., Damas, F., Davignon, O., De Wit, A., Ehle, I. T., Fontana Santos Alves, B. A., Ghosh, S., Gilbert, A., Granier de Cassagnac, R., Hakimi, A., Harikrishnan, B., Kalipoliti, L., Liu, G., Nguyen, M., Ochando, C., Salerno, R., Sauvan, J. B., Sirois, Y., Urda Gómez, L., Vernazza, E., Zabi, A., Zghiche, A., Agram, J.-L., Andrea, J., Apparu, D., Bloch, D., Brom, J.-M., Chabert, E. C., Collard, C., Falke, S., Goerlach, U., Haeberle, R., Le Bihan, A.-C., Meena, M., Poncet, O., Saha, G., Sessini, M. A., Van Hove, P., Vaucelle, P., Di Florio, A., Amram, D., Beauceron, S., Blancon, B., Boudoul, G., Chanon, N., Contardo, D., Depasse, P., Dozen, C., El Mamouni, H., Fay, J., Gascon, S., Gouzevitch, M., Greenberg, C., Grenier, G., Ille, B., Jourd‘huy, E., Laktineh, I. B., Lethuillier, M., Mirabito, L., Perries, S., Purohit, A., Vander Donckt, M., Verdier, P., Xiao, J., Chokheli, D., Lomidze, I., Tsamalaidze, Z., Botta, V., Consuegra Rodríguez, S., Feld, L., Klein, K., Lipinski, M., Meuser, D., Pauls, A., Pérez Adán, D., Röwert, N., Teroerde, M., Diekmann, S., Dodonova, A., Eich, N., Eliseev, D., Engelke, F., Erdmann, J., Erdmann, M., Fackeldey, P., Fischer, B., Hebbeker, T., Hoepfner, K., Ivone, F., Jung, A., Lee, M. y., Mausolf, F., Merschmeyer, M., Meyer, A., Mukherjee, S., Noll, D., Nowotny, F., Pozdnyakov, A., Rath, Y., Redjeb, W., Rehm, F., Reithler, H., Sarkisovi, V., Schmidt, A., Seth, C., Sharma, A., Spah, J. L., Stein, A., Torres Da Silva De Araujo, F., Wiedenbeck, S., Zaleski, S., Dziwok, C., Flügge, G., Kress, T., Nowack, A., Pooth, O., Stahl, A., Ziemons, T., Zotz, A., Aarup Petersen, H., Aldaya Martin, M., Alimena, J., Amoroso, S., An, Y., Bach, J., Baxter, S., Bayatmakou, M., Becerril Gonzalez, H., Behnke, O., Belvedere, A., Blekman, F., Borras, K., Campbell, A., Cardini, A., Cheng, C., Colombina, F., Eckerlin, G., Eckstein, D., Estevez Banos, L. I., Filatov, O., Gallo, E., Geiser, A., Guglielmi, V., Guthoff, M., Hinzmann, A., Jeppe, L., Kaech, B., Kasemann, M., Kleinwort, C., Kogler, R., Komm, M., Krücker, D., Lange, W., Leyva Pernia, D., Lipka, K., Lohmann, W., Lorkowski, F., Mankel, R., Melzer-Pellmann, I.-A., Mendizabal Morentin, M., Meyer, A. B., Milella, G., Moral Figueroa, K., Mussgiller, A., Nair, L. P., Niedziela, J., Nürnberg, A., Otarid, Y., Park, J., Ranken, E., Raspereza, A., Rastorguev, D., Rübenach, J., Rygaard, L., Saggio, A., Scham, M., Schnake, S., Schütze, P., Schwanenberger, C., Selivanova, D., Sharko, K., Shchedrolosiev, M., Stafford, D., Vazzoler, F., Ventura Barroso, A., Walsh, R., Wang, D., Wang, Q., Wen, Y., Wichmann, K., Wiens, L., Wissing, C., Yang, Y., Zimermmane Castro Santos, A., Albrecht, A., Albrecht, S., Antonello, M., Bein, S., Benato, L., Bollweg, S., Bonanomi, M., Connor, P., El Morabit, K., Fischer, Y., Garutti, E., Grohsjean, A., Haller, J., Jabusch, H. R., Kasieczka, G., Keicher, P., Klanner, R., Korcari, W., Kramer, T., Kuo, C. c., Kutzner, V., Labe, F., Lange, J., Lobanov, A., Matthies, C., Moureaux, L., Mrowietz, M., Nigamova, A., Nissan, Y., Paasch, A., Pena Rodriguez, K. J., Quadfasel, T., Raciti, B., Rieger, M., Savoiu, D., Schindler, J., Schleper, P., Schröder, M., Schwandt, J., Sommerhalder, M., Stadie, H., Steinbrück, G., Tews, A., Wolf, M., Brommer, S., Burkart, M., Butz, E., Chwalek, T., Dierlamm, A., Droll, A., Elicabuk, U., Faltermann, N., Giffels, M., Gottmann, A., Hartmann, F., Hofsaess, R., Horzela, M., Husemann, U., Kieseler, J., Klute, M., Koppenhöfer, R., Lawhorn, J. M., Link, M., Lintuluoto, A., Maier, S., Mitra, S., Mormile, M., Müller, Th., Neukum, M., Oh, M., Pfeffer, E., Presilla, M., Quast, G., Rabbertz, K., Regnery, B., Shadskiy, N., Shvetsov, I., Simonis, H. J., Sowa, L., Stockmeier, L., Tauqeer, K., Toms, M., Trevisani, N., Von Cube, R. F., Wassmer, M., Wieland, S., Wittig, F., Wolf, R., Zuo, X., Anagnostou, G., Daskalakis, G., Kyriakis, A., Papadopoulos, A., Stakia, A., Kontaxakis, P., Melachroinos, G., Painesis, Z., Papavergou, I., Paraskevas, I., Saoulidou, N., Theofilatos, K., Tziaferi, E., Vellidis, K., Zisopoulos, I., Bakas, G., Chatzistavrou, T., Karapostoli, G., Kousouris, K., Papakrivopoulos, I., Siamarkou, E., Tsipolitis, G., Zacharopoulou, A., Adamidis, K., Bestintzanos, I., Evangelou, I., Foudas, C., Kamtsikis, C., Katsoulis, P., Kokkas, P., Kosmoglou Kioseoglou, P. G., Manthos, N., Papadopoulos, I., Strologas, J., Hajdu, C., Horvath, D., Márton, K., Rádl, A. J., Sikler, F., Veszpremi, V., Csanád, M., Farkas, K., Fehérkuti, A., Gadallah, M. M. A., Kadlecsik, Á., Major, P., Pásztor, G., Veres, G. I., Ujvari, B., Zilizi, G., Bencze, G., Czellar, S., Molnar, J., Szillasi, Z., Nemes, F., Novak, T., Bansal, S., Beri, S. B., Bhatnagar, V., Chaudhary, G., Chauhan, S., Dhingra, N., Kaur, A., Kaur, A., Kaur, H., Kaur, M., Kumar, S., Sandeep, K., Sheokand, T., Singh, J. B., Singla, A., Ahmed, A., Bhardwaj, A., Chhetri, A., Choudhary, B. C., Kumar, A., Kumar, A., Naimuddin, M., Ranjan, K., Saini, M. K., Saumya, S., Baradia, S., Barman, S., Bhattacharya, S., Das Gupta, S., Dutta, S., Dutta, S., Sarkar, S., Ameen, M. M., Behera, P. K., Behera, S. C., Chatterjee, S., Dash, G., Jana, P., Kalbhor, P., Kamble, S., Komaragiri, J. R., Kumar, D., Pujahari, P. R., Saha, N. R., Sharma, A., Sikdar, A. K., Singh, R. K., Verma, P., Verma, S., Vijay, A., Dugad, S., Mohanty, G. B., Parida, B., Shelake, M., Suryadevara, P., Bala, A., Banerjee, S., Chatterjee, R. M., Guchait, M., Jain, Sh., Jaiswal, A., Kumar, S., Majumder, G., Mazumdar, K., Parolia, S., Thachayath, A., Bahinipati, S., Kar, C., Maity, D., Mal, P., Mishra, T., Muraleedharan Nair Bindhu, V. K., Naskar, K., Nayak, A., Nayak, S., Pal, K., Sadangi, P., Swain, S. K., Varghese, S., Vats, D., Acharya, S., Alpana, A., Dube, S., Gomber, B., Hazarika, P., Kansal, B., Laha, A., Sahu, B., Sharma, S., Vaish, K. Y., Bakhshiansohi, H., Jafari, A., Zeinali, M., Bashiri, S., Chenarani, S., Etesami, S. M., Hosseini, Y., Khakzad, M., Khazaie, E., Mohammadi Najafabadi, M., Tizchang, S., Felcini, M., Grunewald, M., Abbrescia, M., Colaleo, A., Creanza, D., D’Anzi, B., De Filippis, N., De Palma, M., Elmetenawee, W., Fiore, L., Iaselli, G., Longo, L., Louka, M., Maggi, G., Maggi, M., Margjeka, I., Mastrapasqua, V., My, S., Nuzzo, S., Pellecchia, A., Pompili, A., Pugliese, G., Radogna, R., Ramos, D., Ranieri, A., Silvestris, L., Simone, F. M., Sözbilir, Ü., Stamerra, A., Troiano, D., Venditti, R., Verwilligen, P., Zaza, A., Abbiendi, G., Battilana, C., Bonacorsi, D., Capiluppi, P., Castro, A., Cavallo, F. R., Cuffiani, M., Dallavalle, G. 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2024
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238. Search for a resonance decaying to a W boson and a photon in proton-proton collisions at s = 13 TeV using leptonic W boson decays

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Hayrapetyan, A., Tumasyan, A., Adam, W., Andrejkovic, J. W., Bergauer, T., Chatterjee, S., Damanakis, K., Dragicevic, M., Hussain, P. S., Jeitler, M., Krammer, N., Li, A., Liko, D., Mikulec, I., Schieck, J., Schöfbeck, R., Schwarz, D., Sonawane, M., Templ, S., Waltenberger, W., Wulz, C.-E., Janssen, T., Van Laer, T., Van Mechelen, P., Breugelmans, N., D’Hondt, J., Dansana, S., De Moor, A., Delcourt, M., Heyen, F., Lowette, S., Makarenko, I., Müller, D., Tavernier, S., Tytgat, M., Van Onsem, G. P., Van Putte, S., Vannerom, D., Bilin, B., Clerbaux, B., Das, A. K., De Lentdecker, G., Evard, H., Favart, L., Gianneios, P., Jaramillo, J., Khalilzadeh, A., Khan, F. A., Lee, K., Mahdavikhorrami, M., Malara, A., Paredes, S., Shahzad, M. A., Thomas, L., Vanden Bemden, M., Vander Velde, C., Vanlaer, P., De Coen, M., Dobur, D., Gokbulut, G., Hong, Y., Knolle, J., Lambrecht, L., Marckx, D., Mota Amarilo, K., Samalan, A., Skovpen, K., Van Den Bossche, N., van der Linden, J., Wezenbeek, L., Benecke, A., Bethani, A., Bruno, G., Caputo, C., De Favereau De Jeneret, J., Delaere, C., Donertas, I. S., Giammanco, A., Guzel, A. O., Jain, Sa., Lemaitre, V., Lidrych, J., Mastrapasqua, P., Tran, T. T., Wertz, S., Alves, G. A., Alves Gallo Pereira, M., Coelho, E., Correia Silva, G., Hensel, C., Menezes De Oliveira, T., Mora Herrera, C., Moraes, A., Rebello Teles, P., Soeiro, M., Vilela Pereira, A., Aldá Júnior, W. L., Barroso Ferreira Filho, M., Brandao Malbouisson, H., Carvalho, W., Chinellato, J., Da Costa, E. M., Da Silveira, G. G., De Jesus Damiao, D., Fonseca De Souza, S., Gomes De Souza, R., Macedo, M., Martins, J., Mundim, L., Nogima, H., Pinheiro, J. P., Santoro, A., Sznajder, A., Thiel, M., Bernardes, C. A., Calligaris, L., Fernandez Perez Tomei, T. R., Gregores, E. M., Lopes Da Costa, B., Maietto Silverio, I., Mercadante, P. G., Novaes, S. F., Orzari, B., Padula, Sandra S., Aleksandrov, A., Antchev, G., Hadjiiska, R., Iaydjiev, P., Misheva, M., Shopova, M., Sultanov, G., Dimitrov, A., Litov, L., Pavlov, B., Petkov, P., Petrov, A., Shumka, E., Keshri, S., Laroze, D., Thakur, S., Cheng, T., Javaid, T., Yuan, L., Hu, Z., Liang, Z., Liu, J., Yi, K., Chen, G. M., Chen, H. S., Chen, M., Iemmi, F., Jiang, C. H., Kapoor, A., Liao, H., Liu, Z.-A., Sharma, R., Song, J. N., Tao, J., Wang, C., Wang, J., Wang, Z., Zhang, H., Zhao, J., Agapitos, A., Ban, Y., Deng, S., Guo, B., Jiang, C., Levin, A., Li, C., Li, Q., Mao, Y., Qian, S., Qian, S. J., Qin, X., Sun, X., Wang, D., Yang, H., Zhang, L., Zhao, Y., Zhou, C., Yang, S., You, Z., Jaffel, K., Lu, N., Bauer, G., Li, B., Zhang, J., Gao, X., Li, Y., Lin, Z., Lu, C., Xiao, M., Avila, C., Barbosa Trujillo, D. 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K., Wang, F., Wildridge, A., Xie, W., Yao, Y., Dolen, J., Parashar, N., Pathak, A., Acosta, D., Carnahan, T., Ecklund, K. M., Fernández Manteca, P. J., Freed, S., Gardner, P., Geurts, F. J. M., Krommydas, I., Li, W., Lin, J., Miguel Colin, O., Padley, B. P., Redjimi, R., Rotter, J., Yigitbasi, E., Zhang, Y., Bodek, A., de Barbaro, P., Demina, R., Dulemba, J. L., Garcia-Bellido, A., Hindrichs, O., Khukhunaishvili, A., Parmar, N., Parygin, P., Taus, R., Chiarito, B., Chou, J. P., Clark, S. V., Gadkari, D., Gershtein, Y., Halkiadakis, E., Heindl, M., Houghton, C., Jaroslawski, D., Konstantinou, S., Laflotte, I., Lath, A., Montalvo, R., Nash, K., Reichert, J., Routray, H., Saha, P., Salur, S., Schnetzer, S., Somalwar, S., Stone, R., Thayil, S. A., Thomas, S., Vora, J., Wang, H., Ally, D., Delannoy, A. G., Fiorendi, S., Higginbotham, S., Holmes, T., Kanuganti, A. R., Karunarathna, N., Lee, L., Nibigira, E., Spanier, S., Aebi, D., Ahmad, M., Akhter, T., Bouhali, O., Eusebi, R., Gilmore, J., Huang, T., Kamon, T., Kim, H., Luo, S., Mueller, R., Overton, D., Rathjens, D., Safonov, A., Akchurin, N., Damgov, J., Gogate, N., Hegde, V., Hussain, A., Kazhykarim, Y., Lamichhane, K., Lee, S. W., Mankel, A., Peltola, T., Volobouev, I., Appelt, E., Chen, Y., Greene, S., Gurrola, A., Johns, W., Kunnawalkam Elayavalli, R., Melo, A., Romeo, F., Sheldon, P., Tuo, S., Velkovska, J., Viinikainen, J., Cardwell, B., Chung, H., Cox, B., Hakala, J., Hirosky, R., Ledovskoy, A., Neu, C., Bhattacharya, S., Karchin, P. E., Aravind, A., Banerjee, S., Black, K., Bose, T., Dasu, S., De Bruyn, I., Everaerts, P., Galloni, C., He, H., Herndon, M., Herve, A., Koraka, C. K., Lanaro, A., Loveless, R., Madhusudanan Sreekala, J., Mallampalli, A., Mohammadi, A., Mondal, S., Parida, G., Pétré, L., Pinna, D., Savin, A., Shang, V., Sharma, V., Smith, W. H., Teague, D., Tsoi, H. F., Vetens, W., Warden, A., Afanasiev, S., Alexakhin, V., Andreev, V., Andreev, Yu., Aushev, T., Azarkin, M., Babaev, A., Blinov, V., Boos, E., Borshch, V., Budkouski, D., Bunichev, V., Chekhovsky, V., Chistov, R., Danilov, M., Dermenev, A., Dimova, T., Druzhkin, D., Dubinin, M., Dudko, L., Gavrilov, G., Gavrilov, V., Gninenko, S., Golovtcov, V., Golubev, N., Golutvin, I., Gorbunov, I., Gribushin, A., Ivanov, Y., Kachanov, V., Karjavine, V., Karneyeu, A., Kim, V., Kirakosyan, M., Kirpichnikov, D., Kirsanov, M., Klyukhin, V., Kodolova, O., Konstantinov, D., Korenkov, V., Kozyrev, A., Krasnikov, N., Lanev, A., Levchenko, P., Lychkovskaya, N., Makarenko, V., Malakhov, A., Matveev, V., Murzin, V., Nikitenko, A., Obraztsov, S., Oreshkin, V., Palichik, V., Perelygin, V., Perfilov, M., Petrushanko, S., Polikarpov, S., Popov, V., Radchenko, O., Savina, M., Savrin, V., Shalaev, V., Shmatov, S., Shulha, S., Skovpen, Y., Slabospitskii, S., Smirnov, V., Sosnov, D., Sulimov, V., Terkulov, A., Teryaev, O., Tlisova, I., Toropin, A., Uvarov, L., Uzunian, A., Vorobyev, A., Vorotnikov, G., Voytishin, N., Yuldashev, B. S., Zarubin, A., Zhizhin, I., and Zhokin, A.

Published
2024
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240. Integrating gene expression and imaging data across Visium capture areas with visiumStitched

Author

Nicholas J. Eagles, Svitlana V. Bach, Madhavi Tippani, Prashanthi Ravichandran, Yufeng Du, Ryan A. Miller, Thomas M. Hyde, Stephanie C. Page, Keri Martinowich, and Leonardo Collado-Torres

Subjects
Spatial transcriptomics, Visium, Multi-capture area study design, Image stitching, Biotechnology, TP248.13-248.65, Genetics, QH426-470
Abstract

Abstract Background Visium is a widely-used spatially-resolved transcriptomics assay available from 10x Genomics. Standard Visium capture areas (6.5mm by 6.5mm) limit the survey of larger tissue structures, but combining overlapping images and associated gene expression data allow for more complex study designs. Current software can handle nested or partial image overlaps, but is designed for merging up to two capture areas, and cannot account for some technical scenarios related to capture area alignment. Results We generated Visium data from a postmortem human tissue sample such that two capture areas were partially overlapping and a third one was adjacent. We developed the R/Bioconductor package visiumStitched, which facilitates stitching the images together with Fiji (ImageJ), and constructing SpatialExperiment R objects with the stitched images and gene expression data. visiumStitched constructs an artificial hexagonal array grid which allows seamless downstream analyses such as spatially-aware clustering without discarding data from overlapping spots. Data stitched with visiumStitched can then be interactively visualized with spatialLIBD. Conclusions visiumStitched provides a simple, but flexible framework to handle various multi-capture area study design scenarios. Specifically, it resolves a data processing step without disrupting analysis workflows and without discarding data from overlapping spots. visiumStitched relies on affine transformations by Fiji, which have limitations and are less accurate when aligning against an atlas or other situations. visiumStitched provides an easy-to-use solution which expands possibilities for designing multi-capture area study designs.

Published
2024
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241. Alterations in center-surround contrast suppression in patients with major depressive disorder

Author

Kathrin Nickel, Sven P. Heinrich, Malina Beringer, Dominique Endres, Kimon Runge, Sebastian Küchlin, Simon Maier, Michael Bach, Katharina Domschke, Ludger Tebartz van Elst, and Evelyn B. N. Friedel

Subjects
Depression, Contrast suppression, Center-surround contrast suppression, Contrast sensitivity, Retina, Medicine, Science
Abstract

Abstract Previous pattern electroretinogram studies indicate reduced retinal contrast gain in patients with a major depressive disorder (MDD) which may contribute to alterations in visual perception. In line, psychophysical investigations reported elevated contrast thresholds in MDD. This study aims to gain insights into higher-level processing of visual information in MDD by evaluating contrast suppression. We examined contrast suppression of 21 MDD patients and 23 healthy controls (HC) using four different stimulus conditions (spatial frequencies 6.3 and 12.6 cpd at 30% and 60% background contrast) in a psychophysical test. Participants were instructed to adjust perceived contrasts between two vertical target patches, embedded in a horizontally or vertically oriented grid background. With finer stimulus gratings, MDD patients exhibited less contrast suppression compared to HC, particularly when the stimulus contrast was high (p = 0.006; MDD vs. HC = − 45%). Contrast suppression in the HC group was significantly reduced for the coarse compared to the fine grating, while contrast suppression scores in MDD did not change with the spatial properties of the stimulus. The reduced contrast suppression in patients with MDD supports the hypothesis of altered dopaminergic neurotransmission and could be attributed to alterations in the retinal receptive fields or in dysfunctional adaptation mechanisms in depression.

Published
2024
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242. Unveiling the culinary tradition of ‘focaccia’ in Late Neolithic Mesopotamia by way of the integration of use-wear, phytolith & organic-residue analyses

Author

Sergio Taranto, Adrià Breu Barcons, Marta Portillo, Marie Le Miere, Anna Bach Gomèz, Miquel Molist, and Cristina Lemorini

Subjects
Food, Late Neolithic, Pottery function, Use-wear, Phytoliths, Organic-residues, Medicine, Science
Abstract

Abstract Recent studies suggest that in Upper Mesopotamia during the Late Neolithic period, specifically between 6400 and 5900 BCE, simple cereal flour doughs were baked in domed ovens using ceramic pans, commonly known as husking trays. Adopting an integrated approach that investigates various types of evidence, such as use-wear, phytoliths, and organic residues, we further refined and explored this hypothesis. Analysis of a sample of 13 sherds belonging to these trays from Mezraa Teleilat, Akarçay Tepe, and Tell Sabi Abyad provides evidence that a limited number of them could have been used to bake ‘focaccia’-like products with ingredients such as lard or oil. This research project not only further strengthens the theory that husking trays could have been used for baking, but also provides insights into the variety and elaboration of food practices that existed amongst early agricultural communities, demonstrating the existence of a number of different ‘recipes’ for a particular dish. Furthermore, from a methodological perspective, this study highlights how only an integrated approach can contribute to the knowledge of the various culinary traits and traditions of ancient communities.

Published
2024
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243. Coenzyme A fueling with pantethine limits autoreactive T cell pathogenicity in experimental neuroinflammation

Author

Stefano Angiari, Tommaso Carlucci, Simona L. Budui, Simone D. Bach, Silvia Dusi, Julia Walter, Elena Ellmeier, Alyssa Schnabl, Anika Stracke, Natalie Bordag, Cansu Tafrali, Rina Demjaha, Michael Khalil, Gabriele Angelini, Eleonora Terrabuio, Enrica C. Pietronigro, Elena Zenaro, Carlo Laudanna, Barbara Rossi, and Gabriela Constantin

Subjects
CoA metabolism, Pantethine, Autoreactive T cells, EAE, Multiple sclerosis, Immunometabolism, Neurology. Diseases of the nervous system, RC346-429
Abstract

Abstract Background Immune cell metabolism governs the outcome of immune responses and contributes to the development of autoimmunity by controlling lymphocyte pathogenic potential. In this study, we evaluated the metabolic profile of myelin-specific murine encephalitogenic T cells, to identify novel therapeutic targets for autoimmune neuroinflammation. Methods We performed metabolomics analysis on actively-proliferating encephalitogenic T cells to study their overall metabolic profile in comparison to resting T cells. Metabolomics, phosphoproteomics, in vitro functional assays, and in vivo studies in experimental autoimmune encephalomyelitis (EAE), a mouse model of multiple sclerosis (MS), were then implemented to evaluate the effect of metabolic targeting on autoreactive T cell pathogenicity. Finally, we confirmed the translational potential of our targeting approach in human pro-inflammatory T helper cell subsets and in T cells from MS patients. Results We found that autoreactive encephalitogenic T cells display an altered coenzyme A (CoA) synthesis pathway, compared to resting T cells. CoA fueling with the CoA precursor pantethine (PTTH) affected essential immune-related processes of myelin-specific T cells, such as cell proliferation, cytokine production, and cell adhesion, both in vitro and in vivo. Accordingly, pre-clinical treatment with PTTH before disease onset inhibited the development of EAE by limiting T cell pro-inflammatory potential in vivo. Importantly, PTTH also significantly ameliorated the disease course when administered after disease onset in a therapeutic setting. Finally, PTTH reduced pro-inflammatory cytokine production by human T helper 1 (Th1) and Th17 cells and by T cells from MS patients, confirming its translational potential. Conclusion Our data demonstrate that CoA fueling with PTTH in pro-inflammatory and autoreactive T cells may represent a novel therapeutic approach for the treatment of autoimmune neuroinflammation.

Published
2024
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244. Measurement of the B s 0 → J / ψK S 0 $$ {\textrm{B}}_{\textrm{s}}^0\to \textrm{J}/{\uppsi \textrm{K}}_{\textrm{S}}^0 $$ effective lifetime from proton-proton collisions at s $$ \sqrt{s} $$ = 13 TeV

Author

The CMS collaboration, A. Hayrapetyan, A. Tumasyan, W. Adam, J. W. Andrejkovic, T. Bergauer, S. Chatterjee, K. Damanakis, M. Dragicevic, P. S. Hussain, M. Jeitler, N. Krammer, A. Li, D. Liko, I. Mikulec, J. Schieck, R. Schöfbeck, D. Schwarz, M. Sonawane, W. Waltenberger, C.-E. Wulz, T. Janssen, T. Van Laer, P. Van Mechelen, N. Breugelmans, J. D’Hondt, S. Dansana, A. De Moor, M. Delcourt, F. Heyen, S. Lowette, I. Makarenko, D. Müller, S. Tavernier, M. Tytgat, G. P. Van Onsem, S. Van Putte, D. Vannerom, B. Bilin, B. Clerbaux, A. K. Das, G. De Lentdecker, H. Evard, L. Favart, P. Gianneios, J. Jaramillo, A. Khalilzadeh, F. A. Khan, K. Lee, M. Mahdavikhorrami, A. Malara, S. Paredes, M. A. Shahzad, L. Thomas, M. Vanden Bemden, C. Vander Velde, P. Vanlaer, M. De Coen, D. Dobur, G. Gokbulut, Y. Hong, J. Knolle, L. Lambrecht, D. Marckx, K. Mota Amarilo, A. Samalan, K. Skovpen, N. Van Den Bossche, J. van der Linden, L. Wezenbeek, A. Benecke, A. Bethani, G. Bruno, C. Caputo, J. De Favereau De Jeneret, C. Delaere, I. S. Donertas, A. Giammanco, A. O. Guzel, Sa. Jain, V. Lemaitre, J. Lidrych, P. Mastrapasqua, T. T. Tran, S. Wertz, G. A. Alves, M. Alves Gallo Pereira, E. Coelho, G. Correia Silva, C. Hensel, T. Menezes De Oliveira, C. Mora Herrera, A. Moraes, P. Rebello Teles, M. Soeiro, A. Vilela Pereira, W. L. Aldá Júnior, M. Barroso Ferreira Filho, H. Brandao Malbouisson, W. Carvalho, J. Chinellato, E. M. Da Costa, G. G. Da Silveira, D. De Jesus Damiao, S. Fonseca De Souza, R. Gomes De Souza, M. Macedo, J. Martins, L. Mundim, H. Nogima, J. P. Pinheiro, A. Santoro, A. Sznajder, M. Thiel, C. A. Bernardes, L. Calligaris, T. R. Fernandez Perez Tomei, E. M. Gregores, I. Maietto Silverio, P. G. Mercadante, S. F. Novaes, B. Orzari, Sandra S. Padula, A. Aleksandrov, G. Antchev, R. Hadjiiska, P. Iaydjiev, M. Misheva, M. Shopova, G. Sultanov, A. Dimitrov, L. Litov, B. Pavlov, P. Petkov, A. Petrov, E. Shumka, S. Keshri, D. Laroze, S. Thakur, T. Cheng, T. Javaid, L. Yuan, Z. Hu, Z. Liang, J. Liu, K. Yi, G. M. Chen, H. S. Chen, M. Chen, F. Iemmi, C. H. Jiang, A. Kapoor, H. Liao, Z.-A. Liu, R. Sharma, J. N. Song, J. Tao, C. Wang, J. Wang, Z. Wang, H. Zhang, J. Zhao, A. Agapitos, Y. Ban, S. Deng, B. Guo, C. Jiang, A. Levin, C. Li, Q. Li, Y. Mao, S. Qian, S. J. Qian, X. Qin, X. Sun, D. Wang, H. Yang, L. Zhang, Y. Zhao, C. Zhou, S. Yang, Z. You, K. Jaffel, N. Lu, G. Bauer, B. Li, J. Zhang, X. Gao, Y. Li, Z. Lin, C. Lu, M. Xiao, C. Avila, D. A. Barbosa Trujillo, A. Cabrera, C. Florez, J. Fraga, J. A. Reyes Vega, F. Ramirez, C. Rendón, M. Rodriguez, A. A. Ruales Barbosa, J. D. Ruiz Alvarez, D. Giljanovic, N. Godinovic, D. Lelas, A. Sculac, M. Kovac, A. Petkovic, T. Sculac, P. Bargassa, V. Brigljevic, B. K. Chitroda, D. Ferencek, K. Jakovcic, A. Starodumov, T. Susa, A. Attikis, K. Christoforou, A. Hadjiagapiou, C. Leonidou, J. Mousa, C. Nicolaou, L. Paizanos, F. Ptochos, P. A. Razis, H. Rykaczewski, H. Saka, A. Stepennov, M. Finger, A. Kveton, E. Carrera Jarrin, A. A. Abdelalim, S. Elgammal, A. Ellithi Kamel, A. Lotfy, M. A. Mahmoud, K. Ehataht, M. Kadastik, T. Lange, S. Nandan, C. Nielsen, J. Pata, M. Raidal, L. Tani, C. Veelken, H. Kirschenmann, K. Osterberg, M. Voutilainen, S. Bharthuar, N. Bin Norjoharuddeen, E. Brücken, F. Garcia, P. Inkaew, K. T. S. Kallonen, T. Lampén, K. Lassila-Perini, S. Lehti, T. Lindén, L. Martikainen, M. Myllymäki, M. m. Rantanen, H. Siikonen, J. Tuominiemi, P. Luukka, H. Petrow, M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J. L. Faure, F. Ferri, S. Ganjour, P. Gras, G. Hamel de Monchenault, M. Kumar, V. Lohezic, J. Malcles, F. Orlandi, L. Portales, A. Rosowsky, M. Ö. Sahin, A. Savoy-Navarro, P. Simkina, M. Titov, M. Tornago, F. Beaudette, G. Boldrini, P. Busson, A. Cappati, C. Charlot, M. Chiusi, F. Damas, O. Davignon, A. De Wit, I. T. Ehle, B. A. Fontana Santos Alves, S. Ghosh, A. Gilbert, R. Granier de Cassagnac, A. Hakimi, B. Harikrishnan, L. Kalipoliti, G. Liu, M. Nguyen, C. Ochando, R. Salerno, J. B. Sauvan, Y. Sirois, L. Urda Gómez, E. Vernazza, A. Zabi, A. Zghiche, J.-L. Agram, J. Andrea, D. Apparu, D. Bloch, J.-M. Brom, E. C. Chabert, C. Collard, S. Falke, U. Goerlach, R. Haeberle, A.-C. Le Bihan, M. Meena, O. Poncet, G. Saha, M. A. Sessini, P. Van Hove, P. Vaucelle, A. Di Florio, D. Amram, S. Beauceron, B. Blancon, G. Boudoul, N. Chanon, D. Contardo, P. Depasse, C. Dozen, H. El Mamouni, J. Fay, S. Gascon, M. Gouzevitch, C. Greenberg, G. Grenier, B. Ille, E. Jourd‘huy, I. B. Laktineh, M. Lethuillier, L. Mirabito, S. Perries, A. Purohit, M. Vander Donckt, P. Verdier, J. Xiao, D. Chokheli, I. Lomidze, Z. Tsamalaidze, V. Botta, S. Consuegra Rodríguez, L. Feld, K. Klein, M. Lipinski, D. Meuser, A. Pauls, D. Pérez Adán, N. Röwert, M. Teroerde, S. Diekmann, A. Dodonova, N. Eich, D. Eliseev, F. Engelke, J. Erdmann, M. Erdmann, P. Fackeldey, B. Fischer, T. Hebbeker, K. Hoepfner, F. Ivone, A. Jung, M. y. Lee, F. Mausolf, M. Merschmeyer, A. Meyer, S. Mukherjee, D. Noll, F. Nowotny, A. Pozdnyakov, Y. Rath, W. Redjeb, F. Rehm, H. Reithler, V. Sarkisovi, A. Schmidt, C. Seth, A. Sharma, J. L. Spah, A. Stein, F. Torres Da Silva De Araujo, S. Wiedenbeck, S. Zaleski, C. Dziwok, G. Flügge, T. Kress, A. Nowack, O. Pooth, A. Stahl, T. Ziemons, A. Zotz, H. Aarup Petersen, M. Aldaya Martin, J. Alimena, S. Amoroso, Y. An, J. Bach, S. Baxter, M. Bayatmakou, H. Becerril Gonzalez, O. Behnke, A. Belvedere, F. Blekman, K. Borras, A. Campbell, A. Cardini, C. Cheng, F. Colombina, G. Eckerlin, D. Eckstein, L. I. Estevez Banos, O. Filatov, E. Gallo, A. Geiser, V. Guglielmi, M. Guthoff, A. Hinzmann, L. Jeppe, B. Kaech, M. Kasemann, C. Kleinwort, R. Kogler, M. Komm, D. Krücker, W. Lange, D. Leyva Pernia, K. Lipka, W. Lohmann, F. Lorkowski, R. Mankel, I.-A. Melzer-Pellmann, M. Mendizabal Morentin, A. B. Meyer, G. Milella, K. Moral Figueroa, A. Mussgiller, L. P. Nair, J. Niedziela, A. Nürnberg, Y. Otarid, J. Park, E. Ranken, A. Raspereza, D. Rastorguev, J. Rübenach, L. Rygaard, A. Saggio, M. Scham, S. Schnake, P. Schütze, C. Schwanenberger, D. Selivanova, K. Sharko, M. Shchedrolosiev, D. Stafford, F. Vazzoler, A. Ventura Barroso, R. Walsh, Q. Wang, Y. Wen, K. Wichmann, L. Wiens, C. Wissing, Y. Yang, A. Zimermmane Castro Santos, A. Albrecht, S. Albrecht, M. Antonello, S. Bein, L. Benato, S. Bollweg, M. Bonanomi, P. Connor, K. El Morabit, Y. Fischer, E. Garutti, A. Grohsjean, J. Haller, H. R. Jabusch, G. Kasieczka, P. Keicher, R. Klanner, W. Korcari, T. Kramer, C. c. Kuo, V. Kutzner, F. Labe, J. Lange, A. Lobanov, C. Matthies, L. Moureaux, M. Mrowietz, A. Nigamova, Y. Nissan, A. Paasch, K. J. Pena Rodriguez, T. Quadfasel, B. Raciti, M. Rieger, D. Savoiu, J. Schindler, P. Schleper, M. Schröder, J. Schwandt, M. Sommerhalder, H. Stadie, G. Steinbrück, A. Tews, M. Wolf, S. Brommer, M. Burkart, E. Butz, T. Chwalek, A. Dierlamm, A. Droll, U. Elicabuk, N. Faltermann, M. Giffels, A. Gottmann, F. Hartmann, R. Hofsaess, M. Horzela, U. Husemann, J. Kieseler, M. 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Green, A. Grummer, S. Grünendahl, D. Guerrero, O. Gutsche, R. M. Harris, R. Heller, T. C. Herwig, J. Hirschauer, B. Jayatilaka, S. Jindariani, M. Johnson, U. Joshi, T. Klijnsma, B. Klima, K. H. M. Kwok, S. Lammel, D. Lincoln, R. Lipton, T. Liu, C. Madrid, K. Maeshima, C. Mantilla, D. Mason, P. McBride, P. Merkel, S. Mrenna, S. Nahn, J. Ngadiuba, D. Noonan, S. Norberg, V. Papadimitriou, N. Pastika, K. Pedro, C. Pena, F. Ravera, A. Reinsvold Hall, L. Ristori, M. Safdari, E. Sexton-Kennedy, N. Smith, A. Soha, L. Spiegel, S. Stoynev, J. Strait, L. Taylor, S. Tkaczyk, N. V. Tran, L. Uplegger, E. W. Vaandering, I. Zoi, C. Aruta, P. Avery, D. Bourilkov, P. Chang, V. Cherepanov, R. D. Field, C. Huh, E. Koenig, M. Kolosova, J. Konigsberg, A. Korytov, K. Matchev, N. Menendez, G. Mitselmakher, K. Mohrman, A. Muthirakalayil Madhu, N. Rawal, S. Rosenzweig, Y. Takahashi, T. Adams, A. Al Kadhim, A. Askew, S. Bower, V. Hagopian, R. Hashmi, R. S. Kim, T. Kolberg, G. Martinez, H. Prosper, P. R. Prova, M. Wulansatiti, R. Yohay, B. Alsufyani, M. M. Baarmand, S. Butalla, S. Das, T. Elkafrawy, M. Hohlmann, E. Yanes, M. R. Adams, A. Baty, C. Bennett, R. Cavanaugh, R. Escobar Franco, O. Evdokimov, C. E. Gerber, M. Hawksworth, A. Hingrajiya, D. J. Hofman, J. h. Lee, D. S. Lemos, A. H. Merrit, C. Mills, S. Nanda, G. Oh, B. Ozek, D. Pilipovic, R. Pradhan, E. Prifti, T. Roy, S. Rudrabhatla, M. B. Tonjes, N. Varelas, M. A. Wadud, Z. Ye, M. Alhusseini, D. Blend, K. Dilsiz, L. Emediato, G. Karaman, O. K. Köseyan, J.-P. Merlo, A. Mestvirishvili, O. Neogi, H. Ogul, Y. Onel, A. Penzo, C. Snyder, E. Tiras, B. Blumenfeld, L. Corcodilos, J. Davis, A. V. Gritsan, L. Kang, S. Kyriacou, P. Maksimovic, M. Roguljic, J. Roskes, S. Sekhar, M. Swartz, A. Abreu, L. F. Alcerro Alcerro, J. Anguiano, S. Arteaga Escatel, P. Baringer, A. Bean, Z. Flowers, D. Grove, J. King, G. Krintiras, M. Lazarovits, C. Le Mahieu, J. Marquez, M. Murray, M. Nickel, M. Pitt, S. Popescu, C. Rogan, C. Royon, R. Salvatico, S. Sanders, C. Smith, G. Wilson, B. Allmond, R. Gujju Gurunadha, A. Ivanov, K. Kaadze, Y. Maravin, J. Natoli, D. Roy, G. Sorrentino, A. Baden, A. Belloni, J. Bistany-riebman, Y. M. Chen, S. C. Eno, N. J. Hadley, S. Jabeen, R. G. Kellogg, T. Koeth, B. Kronheim, Y. Lai, S. Lascio, A. C. Mignerey, S. Nabili, C. Palmer, C. Papageorgakis, M. M. Paranjpe, E. Popova, A. Shevelev, L. Wang, J. Bendavid, I. A. Cali, P. c. Chou, M. D’Alfonso, J. Eysermans, C. Freer, G. Gomez-Ceballos, M. Goncharov, G. Grosso, P. Harris, D. Hoang, D. Kovalskyi, J. Krupa, L. Lavezzo, Y.-J. Lee, K. Long, C. Mcginn, A. Novak, C. Paus, C. Reissel, C. Roland, G. Roland, S. Rothman, G. S. F. Stephans, B. Wyslouch, T. J. Yang, B. Crossman, B. M. Joshi, C. Kapsiak, M. Krohn, D. Mahon, J. Mans, B. Marzocchi, M. Revering, R. Rusack, R. Saradhy, N. Strobbe, K. Bloom, D. R. Claes, G. Haza, J. Hossain, C. Joo, I. Kravchenko, J. E. Siado, W. Tabb, A. Vagnerini, A. Wightman, F. Yan, D. Yu, H. Bandyopadhyay, L. Hay, H. w. Hsia, I. Iashvili, A. Kalogeropoulos, A. Kharchilava, M. Morris, D. Nguyen, J. Pekkanen, S. Rappoccio, H. Rejeb Sfar, A. Williams, P. Young, G. Alverson, E. Barberis, J. Bonilla, M. Campana, J. Dervan, Y. Haddad, Y. Han, A. Krishna, J. Li, M. Lu, G. Madigan, R. Mccarthy, D. M. Morse, V. Nguyen, T. Orimoto, A. Parker, L. Skinnari, D. Wood, J. Bueghly, S. Dittmer, K. A. Hahn, Y. Liu, Y. Miao, D. G. Monk, M. H. Schmitt, A. Taliercio, M. Velasco, G. Agarwal, R. Band, R. Bucci, S. Castells, A. Das, R. Goldouzian, M. Hildreth, K. W. Ho, K. Hurtado Anampa, T. Ivanov, C. Jessop, K. Lannon, J. Lawrence, N. Loukas, L. Lutton, J. Mariano, N. Marinelli, I. Mcalister, T. McCauley, C. Mcgrady, C. Moore, Y. Musienko, H. Nelson, M. Osherson, A. Piccinelli, R. Ruchti, A. Townsend, Y. Wan, M. Wayne, H. Yockey, M. Zarucki, L. Zygala, A. Basnet, B. Bylsma, M. Carrigan, L. S. Durkin, C. Hill, M. Joyce, M. Nunez Ornelas, K. Wei, B. L. Winer, B. R. Yates, H. Bouchamaoui, P. Das, G. Dezoort, P. Elmer, A. Frankenthal, B. Greenberg, N. Haubrich, K. Kennedy, G. Kopp, S. Kwan, D. Lange, A. Loeliger, D. Marlow, I. Ojalvo, J. Olsen, D. Stickland, C. Tully, S. Malik, A. S. Bakshi, S. Chandra, R. Chawla, A. Gu, L. Gutay, M. Jones, A. W. Jung, A. M. Koshy, M. Liu, G. Negro, N. Neumeister, G. Paspalaki, S. Piperov, V. Scheurer, J. F. Schulte, M. Stojanovic, J. Thieman, A. K. Virdi, F. Wang, A. Wildridge, W. Xie, Y. Yao, J. Dolen, N. Parashar, A. Pathak, D. Acosta, T. Carnahan, K. M. Ecklund, P. J. Fernández Manteca, S. Freed, P. Gardner, F. J. M. Geurts, I. Krommydas, W. Li, J. Lin, O. Miguel Colin, B. P. Padley, R. Redjimi, J. Rotter, E. Yigitbasi, Y. Zhang, A. Bodek, P. de Barbaro, R. Demina, J. L. Dulemba, A. Garcia-Bellido, O. Hindrichs, A. Khukhunaishvili, N. Parmar, P. Parygin, R. Taus, B. Chiarito, J. P. Chou, S. V. Clark, D. Gadkari, Y. Gershtein, E. Halkiadakis, M. Heindl, C. Houghton, D. Jaroslawski, S. Konstantinou, I. Laflotte, A. Lath, R. Montalvo, K. Nash, J. Reichert, H. Routray, P. Saha, S. Salur, S. Schnetzer, S. Somalwar, R. Stone, S. A. Thayil, S. Thomas, J. Vora, H. Wang, D. Ally, A. G. Delannoy, S. Fiorendi, S. Higginbotham, T. Holmes, A. R. Kanuganti, N. Karunarathna, L. Lee, E. Nibigira, S. Spanier, D. Aebi, M. Ahmad, T. Akhter, O. Bouhali, R. Eusebi, J. Gilmore, T. Huang, T. Kamon, S. Luo, R. Mueller, D. Overton, D. Rathjens, A. Safonov, N. Akchurin, J. Damgov, N. Gogate, V. Hegde, A. Hussain, Y. Kazhykarim, K. Lamichhane, A. Mankel, T. Peltola, I. Volobouev, E. Appelt, Y. Chen, S. Greene, A. Gurrola, W. Johns, R. Kunnawalkam Elayavalli, A. Melo, F. Romeo, P. Sheldon, S. Tuo, J. Velkovska, J. Viinikainen, B. Cardwell, H. Chung, B. Cox, J. Hakala, R. Hirosky, A. Ledovskoy, C. Neu, P. E. Karchin, A. Aravind, K. Black, T. Bose, S. Dasu, I. De Bruyn, P. Everaerts, C. Galloni, H. He, M. Herndon, A. Herve, C. K. Koraka, A. Lanaro, R. Loveless, J. Madhusudanan Sreekala, A. Mallampalli, A. Mohammadi, G. Parida, L. Pétré, D. Pinna, A. Savin, V. Shang, W. H. Smith, D. Teague, H. F. Tsoi, W. Vetens, A. Warden, S. Afanasiev, V. Alexakhin, D. Budkouski, I. Golutvin, I. Gorbunov, V. Karjavine, V. Korenkov, A. Lanev, A. Malakhov, V. Matveev, V. Palichik, V. Perelygin, M. Savina, V. Shalaev, S. Shmatov, S. Shulha, V. Smirnov, O. Teryaev, N. Voytishin, B. S. Yuldashev, A. Zarubin, I. Zhizhin, G. Gavrilov, V. Golovtcov, Y. Ivanov, V. Kim, P. Levchenko, V. Murzin, V. Oreshkin, D. Sosnov, V. Sulimov, L. Uvarov, A. Vorobyev, Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, D. Kirpichnikov, M. Kirsanov, N. Krasnikov, I. Tlisova, A. Toropin, T. Aushev, V. Gavrilov, N. Lychkovskaya, A. Nikitenko, V. Popov, A. Zhokin, R. Chistov, M. Danilov, S. Polikarpov, V. Andreev, M. Azarkin, M. Kirakosyan, A. Terkulov, E. Boos, V. Bunichev, M. Dubinin, L. Dudko, A. Ershov, A. Gribushin, V. Klyukhin, O. Kodolova, S. Obraztsov, S. Petrushanko, V. Savrin, A. Snigirev, V. Blinov, T. Dimova, A. Kozyrev, O. Radchenko, Y. Skovpen, V. Kachanov, D. Konstantinov, S. Slabospitskii, A. Uzunian, A. Babaev, V. Borshch, D. Druzhkin, V. Chekhovsky, and V. Makarenko

Subjects
B Physics, Hadron-Hadron Scattering, Lifetime, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

Abstract The effective lifetime of the B s 0 $$ {\textrm{B}}_{\textrm{s}}^0 $$ meson in the decay B s 0 → J / ψK S 0 $$ {\textrm{B}}_{\textrm{s}}^0\to \textrm{J}/{\uppsi \textrm{K}}_{\textrm{S}}^0 $$ is measured using data collected during 2016–2018 with the CMS detector in s $$ \sqrt{s} $$ = 13 TeV proton-proton collisions at the LHC, corresponding to an integrated luminosity of 140 fb −1. The effective lifetime is determined by performing a two-dimensional unbinned maximum likelihood fit to the B s 0 $$ {\textrm{B}}_{\textrm{s}}^0 $$ meson invariant mass and proper decay time distributions. The resulting value of 1.59 ± 0.07(stat) ± 0.03(syst) ps is the most precise measurement to date and is in good agreement with the expected value.

Published
2024
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245. Targeting the Plasmodium falciparum IspE Enzyme

Author

Eleonora Diamanti, Annina M. Steinbach, Lais P. de Carvalho, Henni-Karoliina Ropponen, Antoine Lacour, Rawia Hamid, Sidra Eisa, Patricia Bravo, Spyridon Bousis, Boris Illarionov, Markus Fischer, Mostafa M. Hamed, Nina C. Bach, Matthias Rottmann, Jana Held, Matthias Witschel, Stephan A. Sieber, and Anna K. H. Hirsch

Subjects
Chemistry, QD1-999
Published
2024
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246. Expanding access to addictions care: Implementation of a 24-hour healthcare provider support line in British Columbia, Canada

Author

Anjali Sergeant, Amanda Giesler, Nirupa Goel, and Paxton Bach

Subjects
Medicine (General), R5-920, Social pathology. Social and public welfare. Criminology, HV1-9960
Abstract

Abstract Background Morbidity and mortality related to substance use have risen to catastrophic levels in North America, and treatment services are often difficult to access. In response, the province of British Columbia (BC), Canada, launched a province-wide addiction medicine support phone line that offers clinicians immediate access to phone consultation with an addictions medicine expert. The service operates 24/7 is accessible to any clinician in the province seeking assistance with an addiction-related question. We describe an evaluation of the reach and perceived impact of the service over its first two years. Methods The 24/7 Addiction Medicine Clinician Support Line was evaluated prospectively from June 2020 to April 2022. All provider-to-provider encounters were included. Data was collected from two primary sources: health provider demographic information collected at the time of consultation, and optional clinician surveys conducted after the consultation was complete. Descriptive data are presented as numerical values and percentages. Results Over the 22-month evaluation period, 1,279 consultations were requested by 631 distinct care providers across British Columbia. The service averaged 15 calls per week across the province, and 51.5% of calls were made outside of business hours. Physicians made the majority of calls to the service (n = 865, 67.6%), followed by nurse practitioners (n = 162, 12.7%). Among those who completed a follow-up survey (n = 258 calls, 20.2% total calls), 81.8% (n = 211) were “very” or “extremely” satisfied with the consultation. Of these respondents, 65.5% (n = 169) reported that the consultation led to the provision of better care for their patient, with 58.1% (n = 150) initiating a new prescription and 22.1% (n = 57) reporting expedited treatment for their patient. The consultation area of focus was most commonly opioid use (n = 417; 59.6%), followed by polysubstance use (n = 98; 14.0%). Conclusions The impact of the 24/7 Addiction Clinician Support Line was widespread, and the service increased accessibility to evidence-based addictions treatment across a range of care settings. Clinicians expressed a high degree of satisfaction with the service. To our knowledge, this province-wide program is the first of its kind in North America, offering a scalable and adaptable model to support access to evidence-based addictions care in under-resourced settings.

Published
2024
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247. Isotopic evidence of acetate turnover in Precambrian continental fracture fluids

Author

Elliott P. Mueller, Juliann Panehal, Alexander Meshoulam, Min Song, Christian T. Hansen, Oliver Warr, Jason Boettger, Verena B. Heuer, Wolfgang Bach, Kai-Uwe Hinrichs, John M. Eiler, Victoria Orphan, Barbara Sherwood Lollar, and Alex L. Sessions

Subjects
Science
Abstract

Abstract The deep continental crust represents a vast potential habitat for microbial life where its activity remains poorly constrained. Organic acids like acetate are common in these ecosystems, but their role in the subsurface carbon cycle - including the mechanism and rate of their turnover - is still unclear. Here, we develop an isotope-exchange ‘clock’ based on the abiotic equilibration of H-isotopes between acetate and water, which can be used to define the maximum in situ acetate residence time. We apply this technique to the fracture fluids in Birchtree and Kidd Creek mines within the Canadian Precambrian crust. At both sites, we find that acetate residence times are

Published
2024
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248. Cancer treatment monitoring using cell-free DNA fragmentomes

Author

Iris van ’t Erve, Bahar Alipanahi, Keith Lumbard, Zachary L. Skidmore, Lorenzo Rinaldi, Laurel K. Millberg, Jacob Carey, Bryan Chesnick, Stephen Cristiano, Carter Portwood, Tony Wu, Erica Peters, Karen Bolhuis, Cornelis J. A. Punt, Jennifer Tom, Peter B. Bach, Nicholas C. Dracopoli, Gerrit A. Meijer, Robert B. Scharpf, Victor E. Velculescu, Remond J. A. Fijneman, and Alessandro Leal

Subjects
Science
Abstract

Abstract Circulating cell-free DNA (cfDNA) assays for monitoring individuals with cancer typically rely on prior identification of tumor-specific mutations. Here, we develop a tumor-independent and mutation-independent approach (DELFI-tumor fraction, DELFI-TF) using low-coverage whole genome sequencing to determine the cfDNA tumor fraction and validate the method in two independent cohorts of patients with colorectal or lung cancer. DELFI-TF scores strongly correlate with circulating tumor DNA levels (ctDNA) (r = 0.90, p

Published
2024
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249. Cross-cultural validation of the Spanish version of the Kidney AlloTransplant Immunosuppressive Therapy Adherence Questionnaire (KATITA-25)

Author

Luana Cristina Lins de Medeiros Oliveira, Guillermo Pedreira-Robles, María José Pérez-Sáez, Marta Crespo, Anna Bach-Pascual, Sandra Rubio-Paez, Tania Curado-Soto, Alicia Rovira-Algara, Edoardo Melilli, Javier Jerez-Roig, Ester Oriol-Vila, Cristina Quintana Reyes, Maribel Diaz Jurado, Rand Randall Martins, Francisca Sueli Monte Moreira, and Antonio Gouveia Oliveira

Subjects
Kidney transplantation, Medication non-adherence, Psychometric scale, Cross-cultural validation, Medicine, Science
Abstract

Abstract The KATITA-25 (Kidney AlloTransplant Immunosuppressive Therapy Adherence) Questionnaire is a multidimensional self-administered scale developed in Brazilian Portuguese that measures the predisposition for non-adherence to immunosuppressive medication in candidate patients for kidney transplantation, in the pre-transplant setting. The aim of this study was the cross-cultural validation of the Spanish version of the KATITA-25 scale. The translation/back-translation method was used, followed by cognitive interviews and a pre-test. The Spanish version of KATITA-25 was administered to 163 candidate patients for kidney transplantation from 2 kidney transplant centres and 2 dialysis centres in Catalonia, Spain. The scale was re-administered to the first 79 patients included into the study, after a 2 to 4 weeks interval. Internal consistency was assessed using Cronbach’s alpha, test–retest reliability with the intraclass correlation coefficient (ICC); construct validity with goodness of fit indices after confirmatory factor analysis using structural equation modelling. The Cronbach’s alpha of the Spanish version was 0.83, the ICC was 0.86. Confirmatory factor analysis showed adequate fit of the structural model. Overall, the analytical results closely resembled those obtained in the validation of the original scale. In conclusion, this cross-cultural validation study showed adequate reliability and construct validity of the Spanish version of the KATITA-25 questionnaire.

Published
2024
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250. Ureter‐ileum‐interposition: Combined experience from two high‐volume centres

Author

Maksym Pikul, David Pfister, Constantin Rieger, Christian Bach, Oleg Voylenko, Oleksandr Stakhovskyi, Sofiya Semko, Iurii Vitruk, Oleksii Kononenko, Eduard Stakhovsky, and Axel Heidenreich

Subjects
complex kidney tumours, indications to partial nephrectomy, nephrometry, organ‐sparing management, renal‐cell carcinoma, Diseases of the genitourinary system. Urology, RC870-923
Abstract

Abstract The current study aimed to evaluate short‐ and long‐term complication rates and functional outcomes in a substantial cohort of patients undergoing ileal ureter interposition at two high‐volume medical centres. Materials and methods A retrospective single‐arm analysis was conducted on patients who underwent ureter reconstruction using ileum between 2003 and 2022 at the University Clinic of Cologne and the National Cancer Institute of Ukraine. Data on aetiology, surgical techniques, pre‐ and postoperative kidney function changes, readmission rates and complication management were collected. Postoperative complications were classified according to Clavien–Dindo, and estimated glomerular filtration rate (eGFR) was calculated using the CKD‐EPI formula. Results Results revealed 107 cases with consistent data. Within 90 days post‐surgery, 53% experienced complications, mainly graded as I–II. Grade III complications were seen in 13%, with two cases of grade IV complications leading to extended hospitalisation and patient death. The 90‐day mortality rate was 1.8%. Over a mean follow‐up of 52 months, clinically significant vesico‐renal refluxes occurred in 28%, with only 5.4% leading to persistent urinary tract infection. Antireflux techniques appeared to reduce urine upflow incidence compared with conventional interposition. Anastomosis stricture occurred in 15% of patients, with 63% requiring permanent re‐stenting and 37% needing re‐anastomosis. Metabolic acidosis was clinically significant in 7.5% of cases. A slight improvement in renal function was observed during the first year post‐surgery (average postoperative eGFR = 76 ± 22 ml/min; Mann–Witney U test, p = 0,0198). Affected kidney function improved in 56 (52%), was stable in 41 (38%) and deteriorated in 10 (9.3%). Loss of kidney function on the surgery side was seen in 4 (3.7%) patients and resulted in nephrectomy in 3 (2.8%) cases. Conclusion Ileal ureter interposition demonstrated a favourable safety profile and functional outcomes. This surgical intervention provides an effective tension‐free bypass, irrespective of healthy ureter length.

Published
2024
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