The Performative Narrative Interview:A Creative Strategy for Data Production drawing on Dialogical Narrative Theory

This article presents a novel methodological approach to data collection/production: the Performative Narrative Interview (PNI). This approach was developed as part of an empirical study on the processual construction of the sexual identity of sexually diverse men* in Santiago de Chile. By drawing upon narrative-dialogic theoretical frameworks of subjectivity, the PNI makes explicit three aspects of narrative interviews that tend either to remain unaddressed or are treated separately within narrative inquiry: the performative, the creative and the intersubjective. The PNI utilizes these three aspects to generate a creative interview framework, detailed here, in which multiple versions of subjectivity can emerge. We suggest that methods like the PNI, which support this multiplicity to surface, lead to the production of deeper and more complex narrative data on subjectivity than traditional narrative interviews are able to produce

Alignment-free sequence comparison with spaced k-mers

Alignment-free methods are increasingly used for genome analysis and phylogeny reconstruction since they circumvent various difficulties of traditional approaches that rely on multiple sequence alignments. In particular, they are much faster than alignment-based methods. Most alignment-free approaches work by analyzing the k-mer composition of sequences. In this paper, we propose to use \u27spaced k-mers\u27, i.e. patterns of deterministic and \u27don\u27t care\u27 positions instead of contiguous k-mers. Using simulated and real-world sequence data, we demonstrate that this approach produces better phylogenetic trees than alignment-free methods that rely on contiguous k-mers. In addition, distances calculated with spaced k-mers appear to be statistically more stable than distances based on contiguous k-mers

Unveiling Black-boxes: Explainable Deep Learning Models for Patent Classification

Recent technological advancements have led to a large number of patents in a diverse range of domains, making it challenging for human experts to analyze and manage. State-of-the-art methods for multi-label patent classification rely on deep neural networks (DNNs), which are complex and often considered black-boxes due to their opaque decision-making processes. In this paper, we propose a novel deep explainable patent classification framework by introducing layer-wise relevance propagation (LRP) to provide human-understandable explanations for predictions. We train several DNN models, including Bi-LSTM, CNN, and CNN-BiLSTM, and propagate the predictions backward from the output layer up to the input layer of the model to identify the relevance of words for individual predictions. Considering the relevance score, we then generate explanations by visualizing relevant words for the predicted patent class. Experimental results on two datasets comprising two-million patent texts demonstrate high performance in terms of various evaluation measures. The explanations generated for each prediction highlight important relevant words that align with the predicted class, making the prediction more understandable. Explainable systems have the potential to facilitate the adoption of complex AI-enabled methods for patent classification in real-world applications.Comment: This is the pre-print of the submitted manuscript on the World Conference on eXplainable Artificial Intelligence (xAI2023), Lisbon, Portugal. The published manuscript can be found here https://doi.org/10.1007/978-3-031-44067-0_2

Пойкилодермия пигментная сетчатая Сиватта: клинический случай

КОЖНЫЕ БОЛЕЗНИМЕЛАНОЗСиватта пойкилодерми

Chromium(0) and Molydenum(0) Complexes with a Pyridyl-Mesoionic Carbene Ligand: Structural, (Spectro)electrochemical, Photochemical, and Theoretical Investigations

This work reports on the synthesis and in-depth electrochemical and photochemical characterization of two chromium(0) and molydenum(0) metal complexes with bidentate pyridyl-mesoionic carbene (MIC) ligands of the 1,2,3-triazol-5-ylidene type and carbonyl coligands. Metal complexes with MIC ligands have turned out to have very promising electrocatalytic and photochemical properties, but examples of MIC-containing complexes with early-transition-metal centers remain extremely rare. The electrochemistry of these new MIC complexes was studied by cyclic voltammetry and especially spectroelectrochemistry in the IR region consistent with a mainly metal-centered oxidation, which is fully reversible in the case of the chromium(0) complex. At the same time, the two reduction steps are predominantly ligand-centered according to the observed near-IR absorbance, with the first reduction step being reversible for both systems. The results of the electron paramagnetic resonance studies on the oxidized and reduced species confirm the IR spectroelectrochemistry experiments. The photochemical reactivity of the complexes with a series of organic ligands was investigated by time-resolved (step-scan) Fourier transform infrared (FTIR) spectroscopy. Interestingly, the photoreactions in pyridine and acetonitrile are fully reversible with a slow dark reverse reaction back to the educt species over minutes and even hours, depending on the metal center and reagent. This reversible behavior is in contrast to the expected loss of one or several CO ligands known from related homoleptic as well as heteroleptic M(CO)4L2 α-diimine transition-metal complexes.Fil: Bens, Tobias. Universitat Stuttgart; AlemaniaFil: Boden, Pit. Freie Universität Berlin; AlemaniaFil: Di Martino-Fumo, Patrick. Freie Universität Berlin; AlemaniaFil: Beerhues, Julia. Universitat Stuttgart; AlemaniaFil: Albold, Uta. Freie Universität Berlin; AlemaniaFil: Sobottka, Sebastian. Universitat Stuttgart; AlemaniaFil: Neuman, Nicolás Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Gerhards, Markus. Freie Universität Berlin; AlemaniaFil: Sarkar, Biprajit. Universitat Stuttgart; Alemani

Understanding the biases to sepsis surveillance and quality assurance caused by inaccurate coding in administrative health data

Purpose Timely and accurate data on the epidemiology of sepsis are essential to inform policy decisions and research priorities. We aimed to investigate the validity of inpatient administrative health data (IAHD) for surveillance and quality assurance of sepsis care. Methods We conducted a retrospective validation study in a disproportional stratified random sample of 10,334 inpatient cases of age ≥ 15 years treated in 2015–2017 in ten German hospitals. The accuracy of coding of sepsis and risk factors for mortality in IAHD was assessed compared to reference standard diagnoses obtained by a chart review. Hospital-level risk-adjusted mortality of sepsis as calculated from IAHD information was compared to mortality calculated from chart review information. Results ICD-coding of sepsis in IAHD showed high positive predictive value (76.9–85.7% depending on sepsis definition), but low sensitivity (26.8–38%), which led to an underestimation of sepsis incidence (1.4% vs. 3.3% for severe sepsis-1). Not naming sepsis in the chart was strongly associated with under-coding of sepsis. The frequency of correctly naming sepsis and ICD-coding of sepsis varied strongly between hospitals (range of sensitivity of naming: 29–71.7%, of ICD-diagnosis: 10.7–58.5%). Risk-adjusted mortality of sepsis per hospital calculated from coding in IAHD showed no substantial correlation to reference standard risk-adjusted mortality (r = 0.09). Conclusion Due to the under-coding of sepsis in IAHD, previous epidemiological studies underestimated the burden of sepsis in Germany. There is a large variability between hospitals in accuracy of diagnosing and coding of sepsis. Therefore, IAHD alone is not suited to assess quality of sepsis care

Going from where to why—interpretable prediction of protein subcellular localization

Motivation: Protein subcellular localization is pivotal in understanding a protein's function. Computational prediction of subcellular localization has become a viable alternative to experimental approaches. While current machine learning-based methods yield good prediction accuracy, most of them suffer from two key problems: lack of interpretability and dealing with multiple locations

The role of dobutamine stress cardiovascular magnetic resonance in the clinical management of patients with suspected and known coronary artery disease

BACKGROUND: Recent studies have demonstrated the consistently high diagnostic and prognostic value of dobutamine stress cardiovascular magnetic resonance (DCMR). The value of DCMR for clinical decision making still needs to be defined. Hence, the purpose of this study was to assess the utility of DCMR regarding clinical management of patients with suspected and known coronary artery disease (CAD) in a routine setting. METHODS AND RESULTS: We prospectively performed a standard DCMR examination in 1532 consecutive patients with suspected and known CAD. Patients were stratified according to the results of DCMR: DCMR-positive patients were recommended to undergo invasive coronary angiography and DCMR-negative patients received optimal medical treatment. Of 609 (40%) DCMR-positive patients coronary angiography was performed in 478 (78%) within 90 days. In 409 of these patients significant coronary stenoses ≥ 50% were present (positive predictive value 86%). Of 923 (60%) DCMR-negative patients 833 (90%) received optimal medical therapy. During a mean follow-up period of 2.1 ± 0.8 years (median: 2.1 years, interquartile range 1.5 to 2.7 years) 8 DCMR-negative patients (0.96%) sustained a cardiac event.In 131 DCMR-positive patients who did not undergo invasive angiography, 20 patients (15%) suffered cardiac events. In 90 DCMR-negative patients (10%) invasive angiography was performed within 2 years (range 0.01 to 2.0 years) with 56 patients having coronary stenoses ≥ 50%. CONCLUSION: In a routine setting DCMR proved a useful arbiter for clinical decision making and exhibited high utility for stratification and clinical management of patients with suspected and known CAD

Intramuscular Lipid Metabolism, Insulin Action and Obesity

With the increasing prevalence of obesity, research has focused on the molecular mechanism(s) linking obesity and skeletal muscle insulin resistance. Metabolic alterations within muscle, such as changes in the cellular location of fatty acid transporter proteins, decreased mitochondrial enzyme activity and defects in mitochondrial morphology, likely contribute to obesity and insulin resistance. These defects are thought to play a role in the reduced skeletal muscle fatty acid oxidation (FAO) and increased intramuscular lipid (IMCL) accumulation that is apparent with obesity and other insulin resistant states, such as type 2 diabetes. Intramuscular triacylglycerol (IMTG) does not appear to be a ubiquitous marker of insulin resistance, although specific IMCL intermediates such as long-chain fatty acyl-CoAs (LCFA-CoAs), ceramide and diacylglycerol (DAG) may inhibit insulin signal transduction. In this review, we will briefly summarize the defects in skeletal muscle lipid metabolism associated with obesity, and discuss proposed mechanisms by which these defects may contribute to insulin resistance. Originally published IUBMB Life, Vol. 6, No. 1, Jan 200

Roadmap on Photovoltaic Absorber Materials for Sustainable Energy Conversion

Photovoltaics (PVs) are a critical technology for curbing growing levels of anthropogenic greenhouse gas emissions, and meeting increases in future demand for low-carbon electricity. In order to fulfil ambitions for net-zero carbon dioxide equivalent (CO2eq) emissions worldwide, the global cumulative capacity of solar PVs must increase by an order of magnitude from 0.9 TWp in 2021 to 8.5 TWp by 2050 according to the International Renewable Energy Agency, which is considered to be a highly conservative estimate. In 2020, the Henry Royce Institute brought together the UK PV community to discuss the critical technological and infrastructure challenges that need to be overcome to address the vast challenges in accelerating PV deployment. Herein, we examine the key developments in the global community, especially the progress made in the field since this earlier roadmap, bringing together experts primarily from the UK across the breadth of the photovoltaics community. The focus is both on the challenges in improving the efficiency, stability and levelized cost of electricity of current technologies for utility-scale PVs, as well as the fundamental questions in novel technologies that can have a significant impact on emerging markets, such as indoor PVs, space PVs, and agrivoltaics. We discuss challenges in advanced metrology and computational tools, as well as the growing synergies between PVs and solar fuels, and offer a perspective on the environmental sustainability of the PV industry. Through this roadmap, we emphasize promising pathways forward in both the short- and long-term, and for communities working on technologies across a range of maturity levels to learn from each other.Comment: 160 pages, 21 figure