Output Formats Research Articles

From Calculation to Communication: Using Risk Score Calculators to Inform Clinical Decision Making and Facilitate Patient Engagement.

Risk score calculators are a widely developed tool to support clinicians in identifying and managing risk for certain diseases. However, little is known about physicians' applied experiences with risk score calculators and the role of risk score estimates in clinical decision making and patient communication. Physicians providing care in outpatient community-based clinical settings (N = 20) were recruited to participate in semi-structured individual interviews to assess their use of risk score calculators in practice. Two study team members conducted an inductive thematic analysis using a consensus-based coding approach. Participants referenced at least 20 risk score calculators, the most common being the Atherosclerotic Cardiovascular Disease Risk Calculator. Ecological factors related to the clinical system (e.g., time), patient (e.g., receptivity), and physician (e.g., experience) influenced conditions and patterns of risk score calculator use. For example, compared with attending physicians, residents tended to use a greater variety of risk score calculators and with higher frequency. Risk score estimates were generally used in clinical decision making to improve or validate clinical judgment and in patient communication to serve as a motivational tool. The degree to which risk score estimates influenced physician decision making and whether and how these scores were communicated to patients varied, reflecting a nuanced role of risk score calculator use in clinical practice. The theory of planned behavior can help explain how attitudes, beliefs, and norms shape the use of risk score estimates in clinical decision making and patient communication. Additional research is needed to evaluate best practices in the use of risk score calculators and risk score estimates. The risk score calculators and estimates that participants referenced in this study represented a range of conditions (e.g., heart disease, anxiety), levels of model complexity (e.g., probability calculations, scales of severity), and output formats (e.g., point estimates, risk intervals).Risk score calculators that are easily accessed, have simple inputs, and are trusted by physicians appear more likely to be used.Risk score estimates were generally used in clinical decision making to improve or validate clinical judgment and in patient communication to serve as a motivational tool.Risk score estimates helped participants manage the uncertainty and complexity of various clinical situations, yet consideration of the limitations of these estimates was relatively minimal.Developers of risk score calculators should consider the patient- (e.g., response to risk score estimates) and physician- (e.g., training status) related characteristics that influence risk score calculator use in addition that of the clinical system.

Adaptive Output Formation Tracking for Nonlinear Multiagent Systems With Double Semi-Markovian Switching Topologies and Time-Varying Actuator Faults.

This article investigates adaptive output formation tracking control of nonlinear multiagent systems with time-varying actuator faults and unknown nonidentical control directions under double semi-Markovian switching topologies. Considering the dynamic changes of communication connections in uncertain environments, a double semi-Markov process is first introduced into the leader-follower structure to describe the random switching of communication topologies. Then, a novel adaptive distributed fault-tolerant output formation tracking control framework is established using the backstepping and Nussbaum gain technique to address matched/mismatched uncertainties and disturbances, time-varying actuator faults, and unknown nonidentical control directions. In this control framework, the independent variable of the Nussbaum function is designed as a non-negative function that monotonically increases with respect to time, thereby overcoming the presence of the absolute value of its derivative in the integration process. Based on the distributed structure, an adaptive fault-tolerant controller is further proposed to achieve the asymptotic output formation tracking in mean-square sense. The stability of the closed-loop nonlinear multiagent systems is analysed through the contradiction argument and Lyapunov theorem. The simulation example verifies the effectiveness of the proposed control strategy.

Robust Formation Tracking Control for Noncooperative Heterogeneous Multiagent Systems.

The robust formation tracking problems (FTPs) are investigated for noncooperative heterogeneous multiagent systems (MASs). The parameters and state dimensions of each agent in the MASs can be heterogeneous. The leader is noncooperative and has an unknown external input. The followers are affected by uncertainties and disturbances. A distributed extended state observer is designed to estimate the leader's state and the unknown external input. Then a robust formation tracking control method is proposed. It turns out that the output formation tracking error can reduce to any size neighborhood near the origin by selecting appropriate parameters. Finally, the effectiveness of the proposed observer and controller is demonstrated by a numerical simulation example and a hardware-in-the-loop (HITL) simulation example.

OphGLM: An ophthalmology large language-and-vision assistant

Vision computer-aided diagnostic methods have been used in early ophthalmic disease screening and diagnosis. However, the limited output formats of these methods lead to poor human–computer interaction and low clinical applicability value. Thus, ophthalmic visual question answering is worth studying. Unfortunately, no practical solutions exist before Large Language Models(LLMs). In this paper, we investigate the ophthalmic visual diagnostic interaction problem. We construct an ophthalmology large language-and-vision assistant, OphGLM, consisting of an image encoder, a text encoder, a fusion module, and an LLM module. We establish a new Chinese ophthalmic fine-tuning dataset, FundusTuning-CN, including the fundus instruction and conversation sets. Based on FundusTuning-CN, we establish a novel LLM-tuning strategy to introduce visual model understanding and ophthalmic knowledge into LLMs at a low cost and high efficiency. Leveraging the pre-training of the image encoder, OphGLM demonstrates strong visual understanding and surpasses open-source visual language models in common fundus disease classification tasks. The FundusTuning-CN enables OphGLM to surpass open-source medical LLMs in both ophthalmic knowledge and interactive capabilities. Our proposed OphGLM has the potential to revolutionize clinical applications in ophthalmology. The dataset, code, and models will be publicly available at https://github.com/ML-AILab/OphGLM.

TROPIC: A program for calculating reduced transition probabilities

Measurements of level lifetimes and the extracted transition probabilities are one of the cornerstones of nuclear structure physics. The reduced transition probabilities, B(πλ;Ji→Jf) yield information about the structure, wavefunctions, and matrix elements of excited states connected by electromagnetic transitions in a given nucleus. The arsenal of techniques for measuring lifetimes continues to expand and presently includes a wide range of values from femtoseconds to microseconds. While lifetime measurement techniques vary, the extraction of transition probabilities remains the same. RULER is the program used by the National Nuclear Data Center (NNDC) and ENDSF evaluations, while TRANSNUCLEAR was developed at the University of Cologne and modified by a variety of groups. This paper presents a new program TROPIC (TRansitiOn ProbabIlity Calculator), which is the most modern and efficient way to extract transition probabilities B(πλ). TROPIC is a program written in Python 3 with the NumPy and SciPy libraries. This is in line with the advances that ENSDF and NNDC are making in moving away from the 80-character card punch input formats. Several design features were implemented to provide a streamlined process for the user and mitigate drawbacks that were present in other programs. The results from TROPIC have been compared with TRANSNUCLEAR and RULER. The answers are as expected identical, but the investment of input to output time is significantly reduced. TROPIC will be made available for public domain use, along with a user guide and example files. Program summaryProgram Title: TROPICCPC Library link to program files:https://doi.org/10.17632/958ygp2sb4.1Developer's repository link:https://github.com/ND-fIREBall/TROPICLicensing provisions: GPLv3Programming language: Python 3Nature of problem: An efficient way to calculate multiple reduced transition probabilities with minimal effort invested from the user.Solution method: A Python 3 script has been developed to read in a CSV file containing all necessary input parameters, calculate the transition probabilities listed in the CSV file, and export the results in three different output formats.

Machine Learning for Actionable Warning Identification: A Comprehensive Survey

Actionable Warning Identification (AWI) plays a crucial role in improving the usability of static code analyzers. With recent advances in Machine Learning (ML), various approaches have been proposed to incorporate ML techniques into AWI. These ML-based AWI approaches, benefiting from ML’s strong ability to learn subtle and previously unseen patterns from historical data, have demonstrated superior performance. However, a comprehensive overview of these approaches is missing, which could hinder researchers and practitioners from understanding the current process and discovering potential for future improvement in the ML-based AWI community. In this paper, we systematically review the state-of-the-art ML-based AWI approaches. First, we employ a meticulous survey methodology and gather 51 primary studies from 2000/01/01 to 2023/09/01. Then, we outline a typical ML-based AWI workflow, including warning dataset preparation, preprocessing, AWI model construction, and evaluation stages. In such a workflow, we categorize ML-based AWI approaches based on the warning output format. Besides, we analyze the key techniques used in each stage, along with their strengths, weaknesses, and distribution. Finally, we provide practical research directions for future ML-based AWI approaches, focusing on aspects like data improvement (e.g., enhancing the warning labeling strategy) and model exploration (e.g., exploring large language models for AWI).

Performance of generative pre-trained transformer-4 on the certification test for mental health management: A factorial design

This study aimed to investigate the performance of generative pre-trained transformer-4 (GPT-4) on the Certification Test for Mental Health Management and whether tuned prompts could improve its performance. This study used a 3 × 2 factorial design to examine the performance according to test difficulty (courses) and prompt conditions. We prepared 200 multiple-choice questions (600 questions overall) for each course using the Certification Test for Mental Health Management (levels I-III) and essay questions from the level I test for the previous four examinations. Two conditions were used: a simple prompt condition using the questions as prompts and tuned prompt condition using techniques to obtain better answers. GPT-4 (gpt-4-0613) was adopted and implemented using the OpenAI API. The simple prompt condition scores were 74.5, 71.5, and 64.0 for levels III, II, and I, respectively. The tuned and simple prompt condition scores had no significant differences (OR = 1.03, 95% CI; 0.65-1.62, p = 0.908). Incorrect answers were observed in the simple prompt condition because of the inability to make choices, whereas no incorrect answers were observed in the tuned prompt condition. The average score for the essay questions under the simple prompt condition was 22.5 out of 50 points (45.0%). GPT-4 had a sufficient knowledge network for occupational mental health, surpassing the criteria for levels II and III tests. For the level I test, which required the ability to describe more advanced knowledge accurately, GPT-4 did not meet the criteria. External information may be needed when using GPT-4 at this level. Although the tuned prompts did not significantly improve the performance, they were promising in avoiding unintended outputs and organizing output formats. UMIN trial registration: UMIN-CTR ID = UMIN000053582.

A comparative analysis of ENCODE and Cistrome in the context of TF binding signal

BackgroundWith the rise of publicly available genomic data repositories, it is now common for scientists to rely on computational models and preprocessed data, either as control or to discover new knowledge. However, different repositories adhere to the different principles and guidelines, and data processing plays a significant role in the quality of the resulting datasets. Two popular repositories for transcription factor binding sites data - ENCODE and Cistrome - process the same biological samples in alternative ways, and their results are not always consistent. Moreover, the output format of the processing (BED narrowPeak) exposes a feature, the signalValue, which is seldom used in consistency checks, but can offer valuable insight on the quality of the data.ResultsWe provide evidence that data points with high signalValue(s) (top 25% of values) are more likely to be consistent between ENCODE and Cistrome in human cell lines K562, GM12878, and HepG2. In addition, we show that filtering according to said high values improves the quality of predictions for a machine learning algorithm that detects transcription factor interactions based only on positional information. Finally, we provide a set of practices and guidelines, based on the signalValue feature, for scientists who wish to compare and merge narrowPeaks from ENCODE and Cistrome.ConclusionsThe signalValue feature is an informative feature that can be effectively used to highlight consistent areas of overlap between different sources of TF binding sites that expose it. Its applicability extends to downstream to positional machine learning algorithms, making it a powerful tool for performance tweaking and data aggregation.

Theory and experiment on distributed output formation tracking of unmanned aerial and ground vehicle swarm systems over jointly connected digraphs

This paper studies the distributed output formation tracking control problem of the unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) swarm systems, where the UAV swarm cooperatively tracks the output trajectory of the UGV in a formation under directed jointly connected communication networks. A three-layer formation tracking protocol is proposed. Firstly, a novel distributed observer using neighbor interactions is designed for each UAV to estimate the states of the UGV with parameterized inputs over periodic jointly connected digraphs. Next, based on the observation result and output regulation theory, a virtual reference system that tracks the trajectory of the UGV in the desired formation is constructed to generate reference states for each UAV. Then based on the differential flatness of UAVs, a geometric controller is utilized for UAVs to track the reference states and form the formation. An algorithm to determine the gain matrices of the protocol is also presented while the convergence of the system is analyzed. Finally, an experiment platform with three quadrotor UAVs and one UGV is built. The effectiveness of the proposed protocol is validated both by the simulation and experiment.

FAIR ADCP data with OSADCP: a workflow to process ocean current data from vessel-mounted ADCPs

This paper presents the open-source Python software OSADCP developed for the processing of vessel-mounted Acoustic Doppler Current Profiler (VMADCP) data. At this stage, the toolbox is designed for processing VMADCP measurements from open-ocean applications of Teledyne RDI Ocean Surveyor ADCPs and the data acquisition software VMDAS. Based on the VMDAS ENX binary output format, the software contains implementations for cleaning and vector-averaging of single-ping velocity data, verification of the position data, and applying misalignment and amplitude corrections. The procedures of OSADCP are described in detail to encourage the scientific community to use it for their own purposes. The toolbox is an integral part of a workflow implemented on the German marine research vessels in the framework of the Underway Research Data project of the German Marine Research Alliance (DAM). It aims to ensure standardized data acquisition measures, reliable data transfer from the ADCP to shore both near-real-time and in delayed-mode, processing and quality control, and dissemination of the curated data product in the data repository PANGAEA. From PANGAEA, data sets are forwarded to the European marine data hubs Copernicus Marine Service and EMODnet. The workflow that forms the framework for OSADCP is described here as an example of scientific data management that follows the FAIR data guidelines.

A novel prompting method for few-shot NER via LLMs

In various natural language processing tasks, significant strides have been made by Large Language Models (LLMs). Researchers leverage prompt method to conduct LLMs in accomplishing specific tasks under few-shot conditions. However, the prevalent use of LLMs’ prompt methods mainly focuses on guiding generative tasks, and employing existing prompts may result in poor performance in Named Entity Recognition (NER) tasks. To tackle this challenge, we propose a novel prompting method for few-shot NER. By enhancing existing prompt methods, we devise a standardized prompts tailored for the utilization of LLMs in NER tasks. Specifically, we structure the prompts into three components: task definition, few-shot demonstration, and output format. The task definition conducts LLMs in performing NER tasks, few-shot demonstration assists LLMs in understanding NER task objectives through specific output demonstration, and output format restricts LLMs’ output to prevent the generation of unnecessary results. The content of these components has been specifically tailored for NER tasks. Moreover, for the few-shot demonstration within the prompts, we propose a selection strategy that utilizes feedback from LLMs’ outputs to identify more suitable few-shot demonstration as prompts. Additionally, to enhance entity recognition performance, we enrich the prompts by summarizing error examples from the output process of LLMs and integrating them as additional prompts.

Reading Between the Lines: An Exploratory Pilot Study of a Co-creation Approach to Life Story Work in Aged Care in Australia.

Life story work (LSW) uses a narrative, reminiscence approach to capturing memories from one's life and has shown positive outcomes for participants. However, LSW in aged care has been criticized for being resource intensive, often involving care staff using pre-determined process and output formats. This pilot study explored participants' lived experiences of a novel co-creation approach to LSW conducted predominantly with university students and older adults in residential aged care and retirement communities, producing multi-modal outputs. Within a 12-month period, 33 LSW projects were completed (21 books, 5 posters, and 7 digital stories). Semi-structured interviews (n = 44) explored participants' lived experience of completing a LSW project. Findings indicate that working with students and adopting a flexible, co-creation approach that empowers participants to make decisions, engage in learning and reflection, and build meaningful relationships can maximize opportunities for transformative impacts, and enable providers to offer a LSW program despite finite resources.

Searching for exclusive leptoquarks with the Nambu-Jona-Lasinio composite model at the LHC and HL-LHC

We present a detailed study concerning a new physics scenario involving four fermion operators of the Nambu-Jona-Lasinio type characterized by a strong-coupling ultraviolet fixed point where composite particles are formed as bound states of elementary fermions at the scale Λ=OTeV\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\Lambda =\\mathcal{O}\\left(\ extrm{TeV}\\right) $$\\end{document}. After implementing the model in the Universal FeynRules Output format, we focus on the phenomenology of the scalar leptoquarks at the LHC and the High-Luminosity option. Leptoquark particles have undergone extensive scrutiny in the literature and experimental searches, primarily relying on pair production and, more recently, incorporating single, Drell-Yan t-channel, and lepton-induced processes. This study marks, for the first time, the examination of these production modes at varying jet multiplicities. Novel mechanisms emerge, enhancing the total production cross section. A global strategy is devised to capture all final state particles produced in association with leptoquarks or originating from their decay, which we termed “exclusive”, in an analogy to the nomenclature used in nuclear reactions. The assessment of the significance in current and future LHC runs, focusing on the case of a leptoquark coupling to a muon–c quark pair, reveals greater sensitivity compared to ongoing searches. Given this heightened discovery potential, we advocate the incorporation of exclusive leptoquark searches in future investigations at the LHC.

Can We Standardize Name Reconciliaton via OpenRefine?

Scientific names in biodiversity represent one of the oldest identifiers used in science. As a result, a common repetitive task is being able to reconcile a list of scientific names against curated data sources. Reconciliation allows one to determine if names in a list are spelled correctly, whether they are currently accepted, and their nomenclatural status. There are several online and local resources that provide reconciliation services. We share here the potential in interoperability across reconciliation tools. Global Names Verifier (GNverifier), Catalogue of Life, Global Biodiversity Information Facility (GBIF), Taxonomic Name Resolution Service (TNRS), LifeWatch, National Center for Biotechnology Information (NCBI), World Flora Online, Global Biotic Interactions (GloBI), Nomer, Wikidata, and others provide their own tools for name reconciliation. All these tools have their scope, design decisions, input, and output formats. It is often useful to do reconciliation using several such services, because they often include complementary data. However, with all the idiosyncrasies of services and lack of standardization, it is not an easy task (Islam et al. 2024). It would be great for researchers if all existing and future tools could be standardized. Then moving from one resource to another would be as easy as changing the URL. Implementing elements of Findable, Accessible, Interoperable, and Reusable (FAIR) data management principles would help to create such standards. However, standardizing all existing and future resources to a common interface would be difficult. Some of them have no monetary or programmatic means to modify their code, while others have more urgent priorities. Some resources support a specific research path where adhering to a rigid standard might hinder their innovation. In this paper we suggest interoperability between reconciliation tools by implementing the OpenRefine Reconciliation Service. OpenRefine is a popular and powerful reconciliation and data cleaning application. It is used by many researchers for data transformation and normalization. Any service that implements the OpenRefine Service can be incorporated into data-management workflows just by providing the service's OpenRefine-compatible URL. Such compatible services can easily be discovered by providing their metadata in the OpenRefine Services Registry. In this paper we discuss our implementation of the OpenRefine Service with the Global Names Verifier (GNverifier) reconciliation tool. GNverifier is developed at the Species File Group as a part of the Global Names Architecture initiative. It offers a powerful, configurable, fast way to reconcile scientific names. GNverifier software aggregates data from more than 100 source datasets. Queries return currently accepted names when provided in a dataset. It allows finding matches for names that historically had several suffixes and can do fuzzy and partial matches. It sorts data by many factors to reliably provide the best available results. With a strong focus on software optimization and a sophisticated matching algorithm, it can process 2000 names a second, making it one of the fastest services available. OpenRefine can use GNverifier directly because it is compatible with the OpenRefine protocol. As shown in Fig. 1, switching between GNverifier and Wikidata reconciliation of scientific names requires only change of a service URL. Implementation of the OpenRefine protocol might solve many standardization problems. Some resources already have it implemented (e.g., Wikidata, GNverifier Whitmire and Mozzherin 2023, WFO Plant List, IPNI). Many people already use OpenRefine for their other reconciliation needs. For them, the incorporation of name reconciliation would be especially beneficial because it will fit into their existing data-management workflow Fig. 2. Basic reconciliation by itself is standard by design and a big step forward. Beyond the basic reconciliation (as seen in Fig. 2) there are more data that researchers are interested in. The Service Protocol allows one to add optional "extended" features. For example, for scientific names, we provide "currently accepted" names, data sources where a name was found, taxonomic classification, etc. Fig. 3. To make these fields standardized, we would need a recommendation document that describes additional fields and their format. We need interested parties to participate in its creation and agree on its usage. The same would apply to optional input filters, for example for restricting reconciliation to certain data sources or higher taxonomic entities. We think OpenRefine would be a significant step forward for standardization between name-reconciliation tools.

LAMAIS: A library-aided approach for efficient 1D 1H NMR qualitative analysis in plant metabolomics

BackgroundOne-dimensional proton nuclear magnetic resonance (1D 1H NMR) spectroscopy is a non-destructive, non-targeted analytical technique providing both qualitative and quantitative insights, particularly beneficial for mixture analysis. However, the qualitative analysis of 1D 1H NMR spectra for mixture samples is laborious and time-consuming, involving extensive database searches and verification experiments like spiking. This process heavily relies on the analyst's expertise, leading to efficiency discrepancies. There is a pressing need for a reliable method to streamline operations and enhance the efficiency of qualitative analysis in complex mixtures. ResultsWe introduce a library-aided method for spectral profiling, named LAMAIS. This method achieves compound identification through similarity assessment between samples and template data, allowing rapid, automatic compound identification and full-spectrum peak assignment without the need for fitting. LAMAIS correctly identifies over 90 % of components in synthetic mixtures and more than 75 % in experimental mixtures, surpassing other representative methods with a higher F2 score. Our reference library, which currently includes 71 compounds, is tailored to capture the commonality of primary metabolites across diverse plant species. The analysis of real-world samples yielded encouraging results, underscoring LAMAIS's versatility as an auxiliary tool suitable for a variety of botanical sources. For analyst convenience, interactive graphics are utilized as the output format. SignificanceLAMAIS excels, demonstrating competitiveness and reliability. The approach minimizes repetitive tasks and sample wastage, improving the efficiency of 1D 1H NMR qualitative analysis. Constructing a reference library effectively preserves knowledge, mitigates reliance on human experience, and addresses gaps in the analysis of plant source samples.

Robust output group formation tracking control of heterogeneous multi-agent systems with multiple leaders using reinforcement learning

This paper studies the distributed output formation tracking problem of grouped heterogeneous multi-agent systems under multiple leaders and uncertainties using reinforcement learning (RL). The outputs of followers are supposed to achieve robust tracking to the respective convex point of group leaders while generating an expected time-varying formation configuration. First, a distributed adaptive observer is designed under a directed graph to coordinate the multiple group leaders while estimating the leaders’ dynamics in finite-time. The adaptive mechanism avoids global information of the graph. Second, an optimal tracking problem with respect to the observer is formulated for each follower, while the feedback tracking controller is derived using an action-dependent RL algorithm. An extended learning process for essential dynamics is constructed using the same data, while the output regulation equations are solved equivalently. Third, the robust formation controller and feasibility condition are further proposed based on previous learning results. Stability of the synthetical data-driven controller is analyzed under internal uncertainties and external disturbances. Finally, simulation results are provided to demonstrate the effectiveness of the hierarchical control framework.

Investigating Cobalt Degradation in Acidic Environments for Oxygen and Hydrogen Electrocatalysis with Time-Resolved Mass Spectrometry Techniques

Cobalt is an important additive within commercially available energy devices including hydrogen fuel cell vehicles, lithium-ion batteries for electric vehicles and portable devices, and electrolyzers for water splitting. Regarding devices that include a proton exchange membrane (PEM), such as proton exchange membrane fuel cells (PEMFCs) and proton exchange membrane water electrolyzers (PEMWEs), cobalt-based materials are commonly used to perform the oxygen reduction reaction (ORR) and hydrogen evolution reaction (HER), respectively. However, given the acidic environment induced by the PEM (pH = 1), cobalt is thermodynamically unstable at ORR and HER relevant potentials, which results in cobalt dissolution and consequently decreases in device performance and lifetime. Minimizing electrode degradation of cobalt-based materials in acidic environments therefore requires a holistic understanding of cobalt’s degradation mechanisms, as it would be necessary to simultaneously monitor electrochemical output (current/voltage), cobalt dissolution, and product formation, which is not possible with one technique alone. To gain such an understanding, in this presentation I will discuss our work on developing comparable time-resolved techniques such as on-line inductively coupled plasma mass spectrometry (ICP-MS) and electrochemical mass spectrometry (EC-MS) to investigate the relationship between applied potential, time, cobalt dissolution, and product formation.With usage of EC-MS, it was observed that cobalt is active for HER in 0.1 M HClO4. and undergoes little to no cobalt dissolution (< 1 ng s-1) at potentials where cobalt is evolving H2 per ICP-MS measurements. This suggests that cobalt stability in acid strongly coincides with hydrogen evolution. In addition, when comparing cobalt’s material stability from the on-line ICP-MS experiments with its thermodynamic stability from its Pourbaix diagram, there is little to no cobalt dissolution at potentials outside its thermodynamic stability window. This is unexpected, as cobalt is projected to be thermodynamically stable only at potentials less than -0.277 VRHE (Co2+/Co = -0.277 VRHE for a dissolved total metal ion concentration of 1 M at pH = 1), whereas our data shows that cobalt dissolution is suppressed until -0.05 VRHE. Through combining findings from both EC-MS and ICP-MS, we have postulated a stability mechanism related to surface hydrogen coverage on cobalt that can be used to explain why cobalt is stable during HER and at potentials it is thermodynamically expected to be unstable.In addition to cobalt being HER active, we also uncovered its activity for ORR in O2 saturated 0.1 M HClO4. Cobalt dissolution under O2 versus N2 saturated 0.1 M HClO4 was then compared, and it was observed that in the presence of O2, cobalt dissolution occurs faster (~3x more dissolution), likely due to O2 induced chemical dissolution. However, after 3 cycles, the observed cobalt dissolution rates under both gas types are equivalent (~225 ng s-1), indicating an important relationship between time, potential, and electrochemical environment. Altogether, this work showcases how time-resolved techniques such as ICP-MS and EC-MS can be connected to unravel stability mechanisms of materials and motivative future studies to apply a holistic approach when evaluating material stability.

Dataset of Registered Hematoxylin–Eosin and Ki67 Histopathological Image Pairs Complemented by a Registration Algorithm

In this work, we describe a dataset suitable for analyzing the extent to which hematoxylin–eosin (HE)-stained tissue contains information about the expression of Ki67 in immunohistochemistry staining. The dataset provides images of corresponding pairs of HE and Ki67 stainings and is complemented by algorithms for computing the Ki67 index. We introduce a dataset of high-resolution histological images of testicular seminoma tissue. The dataset comprises digitized histology slides from 77 conventional testicular seminoma patients, obtained via surgical resection. For each patient, two physically adjacent tissue sections are stained: one with hematoxylin and eosin, and one with Ki67 immunohistochemistry staining. This results in a total of 154 high-resolution images. The images are provided in PNG format, facilitating ease of use for image analysis compared to the original scanner output formats. Each image contains enough tissue to generate thousands of non-overlapping 224 × 224 pixel patches. This shows the potential to generate more than 50,000 pairs of patches, one with HE staining and a corresponding Ki67 patch that depicts a very similar part of the tissue. Finally, we present the results of applying a ResNet neural network for the classification of HE patches into categories according to their Ki67 label.

Explainable AI for Text Classification: Lessons from a Comprehensive Evaluation of Post Hoc Methods

AbstractThis paper addresses the notable gap in evaluating eXplainable Artificial Intelligence (XAI) methods for text classification. While existing frameworks focus on assessing XAI in areas such as recommender systems and visual analytics, a comprehensive evaluation is missing. Our study surveys and categorises recent post hoc XAI methods according to their scope of explanation and output format. We then conduct a systematic evaluation, assessing the effectiveness of these methods across varying scopes and levels of output granularity using a combination of objective metrics and user studies. Key findings reveal that feature-based explanations exhibit higher fidelity than rule-based ones. While global explanations are perceived as more satisfying and trustworthy, they are less practical than local explanations. These insights enhance understanding of XAI in text classification and offer valuable guidance for developing effective XAI systems, enabling users to evaluate each explainer’s pros and cons and select the most suitable one for their needs.

The sherlock pipeline: new exoplanet candidates in the WASP-16, HAT-P-27, HAT-P-26, and TOI-2411 systems

ABSTRACT The launches of NASA Kepler and Transiting Exoplanet Survey Satellite (TESS) missions have significantly enhanced the interest in the exoplanet field during the last 15 yr, providing a vast amount of public data that are being exploited by the community thanks to the continuous development of new analysis tools. However, using these tools is not straightforward, and users must dive into different codes, input–output formats, and methodologies, hindering an efficient and robust exploration of the available data. We present the sherlock pipeline, an end-to-end public software that allows the users to easily explore observations from space-based missions such as TESS or Kepler to recover known planets and candidates issued by the official pipelines and search for new planetary candidates that remained unnoticed. The pipeline incorporates all the steps to search for transit-like features, vet potential candidates, provide statistical validation, conduct a Bayesian fitting, and compute observational windows from ground-based observatories. Its performance is tested against a catalogue of known and confirmed planets from the TESS mission, trying to recover the official TESS Objects of Interest (TOIs), explore the existence of companions that have been missed, and release them as new planetary candidates. sherlock demonstrated an excellent performance, recovering 98 per cent of the TOIs and confirmed planets in our test sample and finding new candidates. Specifically, we release four new planetary candidates around the systems WASP-16 (with P $\sim$ 10.46 d and R $\sim$ 2.20 $\mathrm{ R}_{\oplus }$), HAT-P-27 (with P $\sim$ 1.20 d and R $\sim$ 4.33 $\mathrm{ R}_{\oplus }$), HAT-P-26 (with P $\sim$ 6.59 d and R $\sim$ 1.97 $\mathrm{ R}_{\oplus }$), and TOI-2411 (with P $\sim$ 18.75 d and R $\sim$ 2.88 $\mathrm{ R}_{\oplus }$).