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Mitotic count (MC) is the most common measure to assess tumor proliferation in breast cancer patients and is highly predictive of patient outcomes. It is, however, subject to inter- and intraobserver variation and reproducibility challenges that may hamper its clinical utility. In past studies, artificial intelligence (AI)-supported MC has been shown to correlate well with traditional MC on glass slides. Considering the potential of AI to improve reproducibility of MC between pathologists, we undertook the next validation step by evaluating the prognostic value of a fully automatic method to detect and count mitoses on whole slide images using a deep learning model. The model was developed in the context of the Mitosis Domain Generalization Challenge 2021 (MIDOG21) grand challenge and was expanded by a novel automatic area selector method to find the optimal mitotic hotspot and calculate the MC per 2 mm 2 . We employed this method on a breast cancer cohort with long-term follow-up from the University Medical Centre Utrecht (N = 912) and compared predictive values for overall survival of AI-based MC and light-microscopic MC, previously assessed during routine diagnostics. The MIDOG21 model was prognostically comparable to the original MC from the pathology report in uni- and multivariate survival analysis. In conclusion, a fully automated MC AI algorithm was validated in a large cohort of breast cancer with regard to retained prognostic value compared with traditional light-microscopic MC., (© 2024 The Author(s). The Journal of Pathology: Clinical Research published by The Pathological Society of Great Britain and Ireland and John Wiley & Sons Ltd.)

While walking, ground reaction forces point from the centre of pressure to the neighbourhood of a focal point, namely the virtual pivot point (VPP), that adjusts angular momentum around the centre of mass (CoM). This study explores how age and speed affect the VPP quality and position during walking. Analysing an experimental dataset reveals high quality of the VPP in the sagittal plane for both young and elderly groups, regardless of speed. However, in the frontal plane, the VPP quality decreases with increasing speed, with elderly participants exhibiting significantly lower quality. Although not a direct measure of balance, VPP quality reflects changes in whole-body angular momentum owing to ageing and speed. Additionally, a template model is used to reproduce the VPP quality and position trends observed in the experiment. Simulation results highlight the sensitivity of VPP quality to leg force feedback and show that changing VPP height has minimal effect on gait speed. Furthermore, energy redistribution occurs through increased hip extension and leg damping, associated with a greater horizontal VPP distance from the CoM, observed in elderly walking. This study shows promise for analysing gait based on VPP, potentially aiding clinical interventions and supporting locomotion in the elderly., Competing Interests: We declare we have no competing interests., (© 2024 The Authors.)

Assembly Theory provides a promising framework to explain the complexity of systems such as molecular structures and the origins of life, with broad applicability across various disciplines. In this study, we explore and consolidate different aspects of Assembly Theory by introducing a simplified Toy Model to simulate the autocatalytic formation of complex structures. This model abstracts the molecular formation process, focusing on the probabilistic control of catalysis rather than the intricate interactions found in organic chemistry. We establish a connection between probabilistic catalysis events and key principles of Assembly Theory, particularly the probability of a possible construction path in the formation of a complex object, and examine how the assembly of complex objects is impacted by the presence of autocatalysis. Our findings suggest that this presence of autocatalysis tends to favor longer consecutive construction sequences in environments with a low probability of catalysis, while this bias diminishes in environments with higher catalysis probabilities, highlighting the significant influence of environmental factors on the assembly of complex structures.

The strongest gamma-ray burst (GRB) of the century, GRB20221009A, has been detected by the Korean Pathfinder Lunar Orbiter Gamma-ray Spectrometer (KGRS) instrument onboard the Korean Pathfinder Lunar Orbiter (KPLO). KGRS uses a LaBr 3 detector to measure GRB counts with five energy bins in the energy range from 30 keV to 12 MeV. KGRS detected GRB221009A at a distance of 1.508 million kilometers from the Earth. The full duration of the main burst was recorded between 13:20 and 13:26 on October 9, 2022 with peak counts of over 1000 times background. The dead time of KGRS reached as high as 50%, and the intrinsic gamma-ray spectrum of LaBr 3 was significantly altered., (© 2024. The Author(s).)

Recent years have seen vast progress in the development of machine learned force fields (MLFFs) based on ab-initio reference calculations. Despite achieving low test errors, the reliability of MLFFs in molecular dynamics (MD) simulations is facing growing scrutiny due to concerns about instability over extended simulation timescales. Our findings suggest a potential connection between robustness to cumulative inaccuracies and the use of equivariant representations in MLFFs, but the computational cost associated with these representations can limit this advantage in practice. To address this, we propose a transformer architecture called SO3KRATES that combines sparse equivariant representations (Euclidean variables) with a self-attention mechanism that separates invariant and equivariant information, eliminating the need for expensive tensor products. SO3KRATES achieves a unique combination of accuracy, stability, and speed that enables insightful analysis of quantum properties of matter on extended time and system size scales. To showcase this capability, we generate stable MD trajectories for flexible peptides and supra-molecular structures with hundreds of atoms. Furthermore, we investigate the PES topology for medium-sized chainlike molecules (e.g., small peptides) by exploring thousands of minima. Remarkably, SO3KRATES demonstrates the ability to strike a balance between the conflicting demands of stability and the emergence of new minimum-energy conformations beyond the training data, which is crucial for realistic exploration tasks in the field of biochemistry., (© 2024. The Author(s).)

Data from the Internet of Things (IoT) enables the design of new business models and services that improve user experience and satisfaction. These data serve as important information sources for many domains, including disaster management, biosurveillance, smart cities, and smart health, among others. However, this scenario involves the collection of personal data, raising new challenges related to data privacy protection. Therefore, we aim to provide state-of-the-art information regarding privacy issues in the context of IoT, with a particular focus on findings that utilize the Personal Data Store (PDS) as a viable solution for these concerns. To achieve this, we conduct a systematic mapping review to identify, evaluate, and interpret the relevant literature on privacy issues and PDS-based solutions in the IoT context. Our analysis is guided by three well-defined research questions, and we systematically selected 49 studies published until 2023 from an initial pool of 176 papers. We analyze and discuss the most common privacy issues highlighted by the authors and position the role of PDS technologies as a solution to privacy issues in the IoT context. As a result, our findings reveal that only a small number of works (approximately 20%) were dedicated to presenting solutions for privacy issues. Most works (almost 82%) were published between 2018 and 2023, demonstrating an increased interest in the theme in recent years. Additionally, only two works used PDS-based solutions to deal with privacy issues in the IoT context.

Tree-cut width is a parameter that has been introduced as an attempt to obtain an analogue of treewidth for edge cuts. Unfortunately, in spite of its desirable structural properties, it turned out that tree-cut width falls short as an edge-cut based alternative to treewidth in algorithmic aspects. This has led to the very recent introduction of a simple edge-based parameter called edge-cut width [WG 2022], which has precisely the algorithmic applications one would expect from an analogue of treewidth for edge cuts, but does not have the desired structural properties. In this paper, we study a variant of tree-cut width obtained by changing the threshold for so-called thin nodes in tree-cut decompositions from 2 to 1. We show that this "slim tree-cut width" satisfies all the requirements of an edge-cut based analogue of treewidth, both structural and algorithmic, while being less restrictive than edge-cut width. Our results also include an alternative characterization of slim tree-cut width via an easy-to-use spanning-tree decomposition akin to the one used for edge-cut width, a characterization of slim tree-cut width in terms of forbidden immersions as well as approximation algorithm for computing the parameter., Competing Interests: Conflict of interestThe authors have no Conflict of interest., (© The Author(s) 2024.)

In this study, we present a novel approach to estimating the Hurst exponent of time series data using a variety of machine learning algorithms. The Hurst exponent is a crucial parameter in characterizing long-range dependence in time series, and traditional methods such as Rescaled Range (R/S) analysis and Detrended Fluctuation Analysis (DFA) have been widely used for its estimation. However, these methods have certain limitations, which we sought to address by modifying the R/S approach to distinguish between fractional Lévy and fractional Brownian motion, and by demonstrating the inadequacy of DFA and similar methods for data that resembles fractional Lévy motion. This inspired us to utilize machine learning techniques to improve the estimation process. In an unprecedented step, we train various machine learning models, including LightGBM, MLP, and AdaBoost, on synthetic data generated from random walks, namely fractional Brownian motion and fractional Lévy motion, where the ground truth Hurst exponent is known. This means that we can initialize and create these stochastic processes with a scaling Hurst/scaling exponent, which is then used as the ground truth for training. Furthermore, we perform the continuous estimation of the scaling exponent directly from the time series, without resorting to the calculation of the power spectrum or other sophisticated preprocessing steps, as done in past approaches. Our experiments reveal that the machine learning-based estimators outperform traditional R/S analysis and DFA methods in estimating the Hurst exponent, particularly for data akin to fractional Lévy motion. Validating our approach on real-world financial data, we observe a divergence between the estimated Hurst/scaling exponents and results reported in the literature. Nevertheless, the confirmation provided by known ground truths reinforces the superiority of our approach in terms of accuracy. This work highlights the potential of machine learning algorithms for accurately estimating the Hurst exponent, paving new paths for time series analysis. By marrying traditional finance methods with the capabilities of machine learning, our study provides a novel contribution towards the future of time series data analysis.

The vibration signals of rolling bearings are complex and changeable, and extracting meaningful features is difficult. Currently, the commonly used empirical mode decomposition (EMD) algorithms have the problem of mode aliasing. In this paper, a new feature extraction method based on the improved complete ensemble empirical mode decomposition with adapted noise (ICEEMDAN) and permutation entropy is proposed. In this method, the ICEEMDAN algorithm is first improved and optimized to enable a self-selection function The vibration signal is then decomposed into several intrinsic modal functions using this algorithm, and the permutation entropy is extracted as the fault feature of rolling bearings, which improves the accuracy of fault classification and realizes the intelligent feature extraction of different fault states. Then, the Case Western Reserve University dataset is used for verification, and the results show that this scheme can effectively separate the vibration signal characteristics of bearings in different states, and can be used to characterize the characteristics of different bearing signals. Finally, based on the mechanical transmission system bearing experimental platform independently developed by our school, the experimental results show that compared with the unimproved ICEEMDAN algorithm, the diagnostic accuracy rate of the proposed method is 99.5%, which is increased by 6.4%, and it can be effectively used for feature extraction of rolling bearings., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 ISA. Published by Elsevier Ltd. All rights reserved.)

To enhance the robustness of ship autopilot (SA) system with nonlinear dynamics, unmeasured states, and unknown steering machine fault, an observer-based H ∞ fuzzy fault-tolerant switching control for ship course tracking is proposed. Firstly, a global Takagi-Sugeno (T-S) fuzzy nonlinear ship autopilot (NSA) is developed with full consideration of ship steering characteristics. And the actual navigation data collected from a real ship are used to verify the reasonableness and feasibility of NSA model. Then, virtual fuzzy observers (VFOs) for both fault-free and faulty systems are proposed to estimate the unmeasured states and unknown fault simultaneously, and compensate for the faulty system by using the fault estimates. Accordingly, the VFO-based H ∞ robust controller (VFO-HRC) and fault-tolerant controller (VFO-HFTC) are designed. Subsequently, a smoothed Z-score-based fault detection and alarm (FDA) is developed to provide switching signals for which the controller and its corresponding observer should be invoked. Finally, simulation results on the "Yulong" ship demonstrate the effectiveness of the developed control method., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 ISA. Published by Elsevier Ltd. All rights reserved.)

Online hate speech (OHS) is a prevalent societal problem, but most studies investigating the reasons and causes of OHS focus on the perpetrators' side while ignoring the bystanders' and the victims' side. This is also true for the underlying theories. Therefore, we proposed a new Action-Theoretical Model of Online Hate Speech (ATMOHS), which assumes that OHS is a product of environmental, situational, and personal variables with three groups involved (perpetrators, bystanders, and victims) that each have their own set of motives, attitudes, traits, and norm beliefs that are impacting their behavior. The model was put to a first test with an online survey using a quota sample of the German online population ( N  = 1,791). The study at hand is a first analysis of these data that focus on the motives of OHS. Results show that wanting to be a role model for others is an important motive on the active bystanders' side. However, it could not be confirmed that any aggression motive is important on the perpetrators' side or that undeservingness is an important motive on the victims' side. Future studies could investigate if there are other motives for the victims' side that are in-line with the underlying theory of learned helplessness, or if there is a better theory for modeling the victims' side. Future studies could also develop a better scale for aggression motives. In practice, prevention programs could focus on being a role model for others as a relevant motive for becoming an active bystander.

Background: Aphasia describes the lack of the already gained ability to use language in a common way. "Language" here covers all variations of forming or understanding messages., Objectives: The APH-Alarm project aims to develop a service concept that provides alternative communication options for people with Aphasia to trigger timely help when needed. It considers that a typical user may not be familiar with modern technologies and offers several simple and intuitive options., Methods: The approach is based on event detection of gestures (during daytime or in bed), movement pattern recognition in bed, and an easy-to-use pictogram-based smartphone app., Results: Agile evaluation of the smartphone app showed a promising outcome., Conclusion: The idea of a versatile and comprehensive solution for aphasic people to easily contact private or public helpers based on their actions or automatic detection is promising and will be further investigated in an upcoming field trial.

Digital assistants and guidance systems may support persons with dementia (PwD) during the independent use of the toilet room. The paper investigates the possible use of different light sources to provide visual stimuli for guidance. Demonstrators were presented to dementia experts to gather their views. While there is no evidence yet, it can be concluded that light stimuli in the toilet environment could be a (maybe only additional) option for guidance to be further investigated. The different methods must be always adapted to the local situation and the individual user characteristics.

Early detection and analysis of lung cancer involve a precise and efficient lung nodule segmentation in computed tomography (CT) images. However, the anonymous shapes, visual features, and surroundings of the nodules as observed in the CT images pose a challenging and critical problem to the robust segmentation of lung nodules. This article proposes a resource-efficient model architecture: an end-to-end deep learning approach for lung nodule segmentation. It incorporates a Bi-FPN (bidirectional feature network) between an encoder and a decoder architecture. Furthermore, it uses the Mish activation function and class weights of masks with the aim of enhancing the efficiency of the segmentation. The proposed model was extensively trained and evaluated on the publicly available LUNA-16 dataset consisting of 1186 lung nodules. To increase the probability of the suitable class of each voxel in the mask, a weighted binary cross-entropy loss of each sample of training was utilized as network training parameter. Moreover, on the account of further evaluation of robustness, the proposed model was evaluated on the QIN Lung CT dataset. The results of the evaluation show that the proposed architecture outperforms existing deep learning models such as U-Net with a Dice Similarity Coefficient of 82.82% and 81.66% on both datasets.

Similar to human-human interaction (HHI), gaze is an important modality in conversational human-robot interaction (HRI) settings. Previously, human-inspired gaze parameters have been used to implement gaze behavior for humanoid robots in conversational settings and improve user experience (UX). Other robotic gaze implementations disregard social aspects of gaze behavior and pursue a technical goal (e.g., face tracking). However, it is unclear how deviating from human-inspired gaze parameters affects the UX. In this study, we use eye-tracking, interaction duration, and self-reported attitudinal measures to study the impact of non-human inspired gaze timings on the UX of the participants in a conversational setting. We show the results for systematically varying the gaze aversion ratio (GAR) of a humanoid robot over a broad parameter range from almost always gazing at the human conversation partner to almost always averting the gaze. The main results reveal that on a behavioral level, a low GAR leads to shorter interaction durations and that human participants change their GAR to mimic the robot. However, they do not copy the robotic gaze behavior strictly. Additionally, in the lowest gaze aversion setting, participants do not gaze back as much as expected, which indicates a user aversion to the robot gaze behavior. However, participants do not report different attitudes toward the robot for different GARs during the interaction. In summary, the urge of humans in conversational settings with a humanoid robot to adapt to the perceived GAR is stronger than the urge of intimacy regulation through gaze aversion, and a high mutual gaze is not always a sign of high comfort, as suggested earlier. This result can be used as a justification to deviate from human-inspired gaze parameters when necessary for specific robot behavior implementations., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Koller, Weiss, Hirschmanner and Vincze.)

Conservation Banking in California is a long-established offset program. Banks are hybrid instruments that hover between market autonomy and regulatory oversight. Challenges that may affect outcomes of the program include aligning regulation with the scales and objectives of the hybrid market and conservation and interaction with other compensation instruments. I use an analytical framework combining social-ecological fit (does the regulation fit the spatial, functional, and temporal scales of the market or conservation?) and instrument interaction (are compensation instruments redundant, synergetic, etc.?) to analyze the institutional framework of the conservation banking program. Results show that the program fails to reflect the hybrid market or species conservation objectives, creating a social-ecological mismatch. The institutional framework disincentivizes banking, while its contribution in conserving species cannot be measured. Competing and redundant instruments can lead to weaker compensation. The program needs equal standards that reflect conservation objectives for all compensation instruments. Findings on fit can be useful for other banking programs, and considerations on instrument interaction could improve offsets anywhere., (© 2022. The Author(s) under exclusive licence to Royal Swedish Academy of Sciences.)

The ability to monitor the dynamics of stem cell differentiation is a major goal for understanding biochemical evolution pathways. Automating the process of metabolic profiling using 2D NMR helps us to understand the various differentiation behaviors of stem cells, and therefore sheds light on the cellular pathways of development, and enhances our understanding of best practices for in vitro differentiation to guide cellular therapies. In this work, the dynamic evolution of adipose-tissue-derived human Mesenchymal stem cells (AT-derived hMSCs) after fourteen days of cultivation, adipocyte and osteocyte differentiation, was inspected based on 1 H- 1 H TOCSY using machine learning. Multi-class classification in addition to the novelty detection of metabolites was established based on a control hMSC sample after four days' cultivation and we successively detected the changes of metabolites in differentiated MSCs following a set of 1 H- 1 H TOCSY experiments. The classifiers Kernel Null Foley-Sammon Transform and Kernel Density Estimation achieved a total classification error between 0% and 3.6% and false positive and false negative rates of 0%. This approach was successfully able to automatically reveal metabolic changes that accompanied MSC cellular evolution starting from their undifferentiated status to their prolonged cultivation and differentiation into adipocytes and osteocytes using machine learning supporting the research in the field of metabolic pathways of stem cell differentiation.

Antimicrobial peptides (AMPs) represent a potential solution to the growing problem of antimicrobial resistance, yet their identification through wet-lab experiments is a costly and time-consuming process. Accurate computational predictions would allow rapid in silico screening of candidate AMPs, thereby accelerating the discovery process. Kernel methods are a class of machine learning algorithms that utilise a kernel function to transform input data into a new representation. When appropriately normalised, the kernel function can be regarded as a notion of similarity between instances. However, many expressive notions of similarity are not valid kernel functions, meaning they cannot be used with standard kernel methods such as the support-vector machine (SVM). The Kreĭn-SVM represents generalisation of the standard SVM that admits a much larger class of similarity functions. In this study, we propose and develop Kreĭn-SVM models for AMP classification and prediction by employing the Levenshtein distance and local alignment score as sequence similarity functions. Utilising two datasets from the literature, each containing more than 3000 peptides, we train models to predict general antimicrobial activity. Our best models achieve an AUC of 0.967 and 0.863 on the test sets of each respective dataset, outperforming the in-house and literature baselines in both cases. We also curate a dataset of experimentally validated peptides, measured against Staphylococcus aureus and Pseudomonas aeruginosa , in order to evaluate the applicability of our methodology in predicting microbe-specific activity. In this case, our best models achieve an AUC of 0.982 and 0.891, respectively. Models to predict both general and microbe-specific activities are made available as web applications., Competing Interests: A. B. is an employee and shareholder in GSK., (This journal is © The Royal Society of Chemistry.)

The density of mitotic figures (MF) within tumor tissue is known to be highly correlated with tumor proliferation and thus is an important marker in tumor grading. Recognition of MF by pathologists is subject to a strong inter-rater bias, limiting its prognostic value. State-of-the-art deep learning methods can support experts but have been observed to strongly deteriorate when applied in a different clinical environment. The variability caused by using different whole slide scanners has been identified as one decisive component in the underlying domain shift. The goal of the MICCAI MIDOG 2021 challenge was the creation of scanner-agnostic MF detection algorithms. The challenge used a training set of 200 cases, split across four scanning systems. As test set, an additional 100 cases split across four scanning systems, including two previously unseen scanners, were provided. In this paper, we evaluate and compare the approaches that were submitted to the challenge and identify methodological factors contributing to better performance. The winning algorithm yielded an F 1 score of 0.748 (CI95: 0.704-0.781), exceeding the performance of six experts on the same task., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

In many research fields, human-annotated data plays an important role as it is used to accomplish a multitude of tasks. One such example is in the field of multimedia quality assessment where subjective annotations can be used to train or evaluate quality prediction models. Lab-based tests could be one approach to get such quality annotations. They are usually performed in well-defined and controlled environments to ensure high reliability. However, this high reliability comes at a cost of higher time consumption and costs incurred. To mitigate this, crowd or online tests could be used. Usually, online tests cover a wider range of end devices, environmental conditions, or participants, which may have an impact on the ratings. To verify whether such online tests can be used for visual quality assessment, we designed three online tests. These online tests are based on previously conducted lab tests as this enables comparison of the results of both test paradigms. Our focus is on the quality assessment of high-resolution images and videos. The online tests use AVrate Voyager, which is a publicly accessible framework for online tests. To transform the lab tests into online tests, dedicated adaptations in the test methodologies are required. The considered modifications are, for example, a patch-based or centre cropping of the images and videos, or a randomly sub-sampling of the to-be-rated stimuli. Based on the analysis of the test results in terms of correlation and SOS analysis it is shown that online tests can be used as a reliable replacement for lab tests albeit with some limitations. These limitations relate to, e.g., lack of appropriate display devices, limitation of web technologies, and modern browsers considering support for different video codecs and formats., Competing Interests: Conflict of interestConflict of interest of potential conflicts of interest: The authors have no potential conflicts of interest to disclose. Research involving Human Participants: Participants are asked in online or lab studies to rate specific aspects of image and video quality. All guidelines pertaining to such online/lab studies conducted in the literature are followed. Informed consent: All participants in the lab/online studies were made aware of the relevant data privacy aspects and also potential health affecting aspects if any., (© The Author(s) 2023.)

Introduction: Artificial intelligence-driven decision support systems (AI-DSS) have the potential to help physicians analyze data and facilitate the search for a correct diagnosis or suitable intervention. The potential of such systems is often emphasized. However, implementation in clinical practice deserves continuous attention. This article aims to shed light on the needs and challenges arising from the use of AI-DSS from physicians' perspectives., Methods: The basis for this study is a qualitative content analysis of expert interviews with experienced nephrologists after testing an AI-DSS in a straightforward usage scenario., Results: The results provide insights on the basics of clinical decision-making, expected challenges when using AI-DSS as well as a reflection on the test run., Discussion: While we can confirm the somewhat expectable demand for better explainability and control, other insights highlight the need to uphold classical strengths of the medical profession when using AI-DSS as well as the importance of broadening the view of AI-related challenges to the clinical environment, especially during treatment. Our results stress the necessity for adjusting AI-DSS to shared decision-making. We conclude that explainability must be context-specific while fostering meaningful interaction with the systems available., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Samhammer, Roller, Hummel, Osmanodja, Burchardt, Mayrdorfer, Duettmann and Dabrock.)

Patient care after kidney transplantation requires integration of complex information to make informed decisions on risk constellations. Many machine learning models have been developed for detecting patient outcomes in the past years. However, performance metrics alone do not determine practical utility. We present a newly developed clinical decision support system (CDSS) for detection of patients at risk for rejection and death-censored graft failure. The CDSS is based on clinical routine data including 1,516 kidney transplant recipients and more than 100,000 data points. In a reader study we compare the performance of physicians at a nephrology department with and without the CDSS. Internal validation shows AUC-ROC scores of 0.83 for rejection, and 0.95 for graft failure. The reader study shows that predictions by physicians converge toward the CDSS. However, performance does not improve (AUC-ROC; 0.6413 vs. 0.6314 for rejection; 0.8072 vs. 0.7778 for graft failure). Finally, the study shows that the CDSS detects partially different patients at risk compared to physicians. This indicates that the combination of both, medical professionals and a CDSS might help detect more patients at risk for graft failure. However, the question of how to integrate such a system efficiently into clinical practice remains open., Competing Interests: All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Roller, Mayrdorfer, Duettmann, Naik, Schmidt, Halleck, Hummel, Burchardt, Möller, Dabrock, Osmanodja and Budde.)

In Message Passing (MP) decoding of Low-Density Parity Check (LDPC) codes, extrinsic information is exchanged between Check Nodes (CNs) and Variable Nodes (VNs). In a practical implementation, this information exchange is limited by quantization using only a small number of bits. In recent investigations, a novel class of Finite Alphabet Message Passing (FA-MP) decoders are designed to maximize the Mutual Information (MI) using only a small number of bits per message (e.g., 3 or 4 bits) with a communication performance close to high-precision Belief Propagation (BP) decoding. In contrast to the conventional BP decoder, operations are given as discrete-input discrete-output mappings which can be described by multidimensional LUTs (mLUTs). A common approach to avoid exponential increases in the size of mLUTs with the node degree is given by the sequential LUT (sLUT) design approach, i.e., by using a sequence of two-dimensional Lookup-Tables (LUTs) for the design, leading to a slight performance degradation. Recently, approaches such as Reconstruction-Computation-Quantization (RCQ) and Mutual Information-Maximizing Quantized Belief Propagation (MIM-QBP) have been proposed to avoid the complexity drawback of using mLUTs by using pre-designed functions that require calculations over a computational domain. It has been shown that these calculations are able to represent the mLUT mapping exactly by executing computations with infinite precision over real numbers. Based on the framework of MIM-QBP and RCQ, the Minimum-Integer Computation (MIC) decoder design generates low-bit integer computations that are derived from the Log-Likelihood Ratio (LLR) separation property of the information maximizing quantizer to replace the mLUT mappings either exactly or approximately. We derive a novel criterion for the bit resolution that is required to represent the mLUT mappings exactly. Furthermore, we show that our MIC decoder has exactly the communication performance of the corresponding mLUT decoder, but with much lower implementation complexity. We also perform an objective comparison between the state-of-the-art Min-Sum (MS) and the FA-MP decoder implementations for throughput towards 1 Tb/s in a state-of-the-art 28 nm Fully-Depleted Silicon-on-Insulator (FD-SOI) technology. Furthermore, we demonstrate that our new MIC decoder implementation outperforms previous FA-MP decoders and MS decoders in terms of reduced routing complexity, area efficiency and energy efficiency.

For large-scale utilization of fuel cells in a future hydrogen-based energy economy, affordable and environmentally benign catalysts are needed. Pyrolytically obtained metal- and nitrogen-doped carbon (MNC) catalysts are key contenders for this task. Their systematic improvement requires detailed knowledge of the active site composition and degradation mechanisms. In FeNC catalysts, the active site is an iron ion coordinated by nitrogen atoms embedded in an extended graphene sheet. Herein, we build an active site model from in situ and operando 57 Fe Mössbauer spectroscopy and quantum chemistry. A Mössbauer signal newly emerging under operando conditions, D4, is correlated with the loss of other Mössbauer signatures (D2, D3a, D3b), implying a direct structural correspondence. Pyrrolic N-coordination, i.e. , FeN 4 C 12 , is found as a spectroscopically and thermodynamically consistent model for the entire catalytic cycle, in contrast to pyridinic nitrogen coordination. These findings thus overcome the previously conflicting structural assignments for the active site and, moreover, identify and structurally assign a previously unknown intermediate in the oxygen reduction reaction at FeNC catalysts.

Signaling by the human C-type lectin-like receptor, natural killer (NK) cell inhibitory receptor NKR-P1, has a critical role in many immune-related diseases and cancer. C-type lectin-like receptors have weak affinities to their ligands; therefore, setting up a comprehensive model of NKR-P1-LLT1 interactions that considers the natural state of the receptor on the cell surface is necessary to understand its functions. Here we report the crystal structures of the NKR-P1 and NKR-P1:LLT1 complexes, which provides evidence that NKR-P1 forms homodimers in an unexpected arrangement to enable LLT1 binding in two modes, bridging two LLT1 molecules. These interaction clusters are suggestive of an inhibitory immune synapse. By observing the formation of these clusters in solution using SEC-SAXS analysis, by dSTORM super-resolution microscopy on the cell surface, and by following their role in receptor signaling with freshly isolated NK cells, we show that only the ligation of both LLT1 binding interfaces leads to effective NKR-P1 inhibitory signaling. In summary, our findings collectively support a model of NKR-P1:LLT1 clustering, which allows the interacting proteins to overcome weak ligand-receptor affinity and to trigger signal transduction upon cellular contact in the immune synapse., (© 2022. The Author(s).)

Digital Elevation Models (DEMs) of planet Mars are crucial for many remote sensing applications and for landing site characterization of rover missions. Shape from Shading (SfS) is known to work well as a complementary method to greatly enhance the quality of photogrammetrically obtained DEMs of planetary surfaces with respect to the effective resolution and the overall accuracy. In this work, we extend our previous lunar shape and albedo from shading framework by embedding the Hapke photometric reflectance model in an atmospheric model such that it is applicable to Mars. Compared to previous approaches, the proposed method is capable of directly estimating the atmospheric parameters from a given scene without the need for external data, and assumes a spatially varying albedo. The DEMs are generated from imagery of the Context Camera (CTX) onboard the Mars Reconnaissance Orbiter (MRO) and are validated for clear and opaque atmospheric conditions. We analyze the necessity of using atmospheric compensation depending on the atmospheric conditions. For low optical depths, the Hapke model without an atmospheric component is still applicable to the Martian surface. For higher optical depths, atmospheric compensation is required to obtain good quality DEMs.

Most metabolic profiling approaches focus only on identifying pre-known metabolites on NMR TOCSY spectrum using configured parameters. However, there is a lack of tasks dealing with automating the detection of new metabolites that might appear during the dynamic evolution of biological cells. Novelty detection is a category of machine learning that is used to identify data that emerge during the test phase and were not considered during the training phase. We propose a novelty detection system for detecting novel metabolites in the 2D NMR TOCSY spectrum of a breast cancer-tissue sample. We build one- and multi-class recognition systems using different classifiers such as, Kernel Null Foley-Sammon Transform, Kernel Density Estimation, and Support Vector Data Description. The training models were constructed based on different sizes of training data and are used in the novelty detection procedure. Multiple evaluation measures were applied to test the performance of the novelty detection methods. Depending on the training data size, all classifiers were able to achieve 0% false positive rates and total misclassification error in addition to 100% true positive rates. The median total time for the novelty detection process varies between 1.5 and 20 seconds, depending on the classifier and the amount of training data. The results of our novel metabolic profiling method demonstrate its suitability, robustness and speed in automated metabolic research., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.)

People with physical limitations face significant challenges when using existing toilets. User requirements work shows the wide range of user needs and confirms the high demand for innovative toilets, enabling people to leave home more often and participate more in societal life. The Toilet For Me too (T4ME2) project aims to implement and test a new ICT-based toilet system capable of physically supporting users, allowing autonomous and safe use outside the home.

Predictive models of thermodynamic properties of mixtures are paramount in chemical engineering and chemistry. Classical thermodynamic models are successful in generalizing over (continuous) conditions like temperature and concentration. On the other hand, matrix completion methods (MCMs) from machine learning successfully generalize over (discrete) binary systems; these MCMs can make predictions without any data for a given binary system by implicitly learning commonalities across systems. In the present work, we combine the strengths from both worlds in a hybrid approach. The underlying idea is to predict the pair-interaction energies , as they are used in basically all physical models of liquid mixtures, by an MCM. As an example, we embed an MCM into UNIQUAC, a widely-used physical model for the Gibbs excess energy. We train the resulting hybrid model in a Bayesian machine-learning framework on experimental data for activity coefficients in binary systems of 1146 components from the Dortmund Data Bank. We thereby obtain, for the first time, a complete set of UNIQUAC parameters for all binary systems of these components, which allows us to predict, in principle, activity coefficients at arbitrary temperature and composition for any combination of these components, not only for binary but also for multicomponent systems. The hybrid model even outperforms the best available physical model for predicting activity coefficients, the modified UNIFAC (Dortmund) model., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

In a world where technologies often serve to amplify the persistent rendering of disability as an undesired deficit, what we need are empowering utopias concerning bodies, disabilities and assistive technologies. Specifically, I use Barad's article 'Transmaterialities: Trans*/Matter/Realities and Queer Political Imaginings' to illustrate how we might speculate on technologies that understand disabled bodies as affording potentials. The Transreal Tracing Device reimagines our bodies as surfaces of possibility, encouraging explorations into how disabled bodies do and could look like. The speculative device offers an opportunity for positive renegotiations of disabled bodies as malleable and desirable - as ontologically indeterminate and transcendent. In traversing theoretical approaches and using them to design queer-feminist utopias centring disabled people, the concept challenges dominant notions of disabilities and assistive technologies alike. I close by discussing implications for ability-based, participatory but even more so self-determined design, and how to shift the focus of disabled technologies towards potential, from support to appreciation, from isolation to kinship and, ultimately, from shame to pride., (© The Author(s) 2022.)

Machine-learning techniques are shifting the boundaries of feasibility in many fields of ethological research. Here, we describe an application of machine learning to the detection/measurement of hygienic behaviour, an important breeding trait in the honey bee ( Apis mellifera ). Hygienic worker bees are able to detect and destroy diseased brood, thereby reducing the reproduction of economically important pathogens and parasites such as the Varroa mite ( Varroa destructor ). Video observation of this behaviour on infested combs has many advantages over other methods of measurement, but analysing the recorded material is extremely time-consuming. We approached this problem by combining automatic tracking of bees in the video recordings, extracting relevant features, and training a multi-layer discriminator on positive and negative examples of the behaviour of interest. Including expert knowledge into the design of the features lead to an efficient model for identifying the uninteresting parts of the video which can be safely skipped. This algorithm was then used to semiautomatically identify individual worker bees involved in the behaviour. Application of the machine-learning method allowed to save 70% of the time required for manual analysis, and substantially increased the number of cell openings correctly identified. It thereby turns video-observation of individual cell opening events into an economically competitive method for selecting potentially resistant bees. This method presents an example of how machine learning can be used to boost ethological research, and how it can generate new knowledge by explaining the learned decision rule in form of meaningful parameters., (© The Author(s) 2022. Published by Oxford University Press.)

Cartilage damage typically starts at its surface, either due to wear or trauma. Treatment of these superficial defects is important in preventing degradation and osteoarthritis. Biomaterials currently used for deep cartilage defects lack appropriate properties for this application. Therefore, we investigated photo-crosslinked gelatin methacryloyl (gelMA) as a candidate for treatment of surface defects. It allows for liquid application, filling of surface defects and forming a protective layer after UV-crosslinking, thereby keeping therapeutic cells in place. gelMA and photo-initiator lithium phenyl-2,4,6-trimethyl-benzoylphosphinate (Li-TPO) concentration were optimized for application as a carrier to create a favorable environment for human articular chondrocytes (hAC). Primary hAC were used in passages 3 and 5, encapsulated into two different gelMA concentrations (7.5 wt% (soft) and 10 wt% (stiff)) and cultivated for 3 weeks with TGF-β3 (0, 1 and 10 ng/mL). Higher TGF-β3 concentrations induced spherical cell morphology independent of gelMA stiffness, while low TGF-β3 concentrations only induced rounded morphology in stiff gelMA. Gene expression did not vary across gel stiffnesses. As a functional model gelMA was loaded with two different cell types (hAC and/or human adipose-derived stem cells [ASC/TERT1]) and applied to human osteochondral osteoarthritic plugs. GelMA attached to the cartilage, smoothened the surface and retained cells in place. Resistance against shear forces was tested using a tribometer, simulating normal human gait and revealing maintained cell viability. In conclusion gelMA is a versatile, biocompatible material with good bonding capabilities to cartilage matrix, allowing sealing and smoothening of superficial cartilage defects while simultaneously delivering therapeutic cells for tissue regeneration., (© 2021 The Authors. Journal of Tissue Engineering and Regenerative Medicine published by John Wiley & Sons Ltd.)

The income situation and the division of labor in households, which are closely related to occupational mobility, are central aspects of the debate on gender equality. Women have shorter commuting times and distances than men and spend fewer nights away from their main place of residence for work-related reasons. Various studies attribute these gender differences to a gendered division of labor and the associated greater involvement of women in household tasks and childcare. Consequently, studies investigating these gender differences focus primarily on employees in relationships and the associated intra-couple interactions, while little attention is paid to singles. Based on the German Family Panel (pairfam) this research aims to broaden the scope of interpretation and examines gender differences in work-related high mobility among employees in partnerships with and without children and among singles. Logistic regression models including gender interaction terms show that gender differences exist not only among employees with partners (and children), but also among singles. The results highlight that gender differences in high mobility are due to factors related to relationships and parenthood, as well as from other factors. Gender differences in high mobility are thus not merely the result of negotiation processes or of (patriarchal) power structures in relationships and gendered labor division. They are also related to gendered occupational segregation and economic disparities and internalized gender preferences that are independent of partnership and parenthood., Competing Interests: Conflict of interestThe authors declare that there is no conflict of interest., (© The Author(s) 2021.)

The COVID-19 pandemic and crises like the Ukraine-Russia war have led to numerous restrictions for industrial manufacturing due to interrupted supply chains, staff absences due to illness or quarantine measures, and order situations that changed significantly at short notice. These influences have exposed that it is crucial to address the issue of manufacturing resilience in the context of current disruptions. This can be plausibly guaranteed by subjecting the ML model of a manufacturing system to attacks deliberately designed to fool its prediction. Such attacks can provide useful insights into properties that can increase resilience of manufacturing systems., (© 2022 The Author(s).)

An anisotropic interlayer force field that describes the interlayer interactions in molybdenum disulfide (MoS 2 ) is presented. The force field is benchmarked against density functional theory calculations for both bilayer and bulk systems within the Heyd-Scuseria-Ernzerhof hybrid density functional approximation, augmented by a nonlocal many-body dispersion treatment of long-range correlation. The parametrization yields good agreement with the reference calculations of binding energy curves and sliding potential energy surfaces for both bilayer and bulk configurations. Benchmark calculations for the phonon spectra of bulk MoS 2 provide good agreement with experimental data, and the calculated bulk modulus falls in the lower part of experimentally measured values. This indicates the accuracy of the interlayer force field near equilibrium. Under external pressures up to 20 GPa, the developed force field provides a good description of compression curves. At higher pressures, deviations from experimental data grow, signifying the validity range of the developed force field.

The history of the research on peptic ulcer disease (PUD) is characterized by a premature abandonment of the bacterial hypothesis, which subsequently had its comeback, leading to the discovery of Helicobacter pylori-the major cause of the disease. In this paper we examine the received view on this case, according to which the primary reason for the abandonment of the bacterial hypothesis in the mid-twentieth century was a large-scale study by a prominent gastroenterologist Palmer, which suggested no bacteria could be found in the human stomach. To this end, we employ the method of digital textual analysis and study the literature on the etiology of PUD published in the decade prior to Palmer's article. Our findings suggest that the bacterial hypothesis had already been abandoned before the publication of Palmer's paper, which challenges the widely held view that his study played a crucial role in the development of this episode. In view of this result, we argue that the PUD case does not illustrate harmful effects of a high degree of information flow, as it has frequently been claimed in the literature on network epistemology. Moreover, we argue that alternative examples of harmful effects of a high degree of information flow may be hard to find in the history of science., (© 2021. The Author(s).)

Crowdsensing offers a cost-effective way to collect large amounts of environmental sensor data; however, the spatial distribution of crowdsensing sensors can hardly be influenced, as the participants carry the sensors, and, additionally, the quality of the crowdsensed data can vary significantly. Hybrid systems that use mobile users in conjunction with fixed sensors might help to overcome these limitations, as such systems allow assessing the quality of the submitted crowdsensed data and provide sensor values where no crowdsensing data are typically available. In this work, we first used a simulation study to analyze a simple crowdsensing system concerning the detection performance of spatial events to highlight the potential and limitations of a pure crowdsourcing system. The results indicate that even if only a small share of inhabitants participate in crowdsensing, events that have locations correlated with the population density can be easily and quickly detected using such a system. On the contrary, events with uniformly randomly distributed locations are much harder to detect using a simple crowdsensing-based approach. A second evaluation shows that hybrid systems improve the detection probability and time. Finally, we illustrate how to compute the minimum number of fixed sensors for the given detection time thresholds in our exemplary scenario.

Metabolomics is an expanding field of medical diagnostics since many diseases cause metabolic reprogramming alteration. Additionally, the metabolic point of view offers an insight into the molecular mechanisms of diseases. Due to the complexity of metabolic assignment dependent on the 1D NMR spectral analysis, 2D NMR techniques are preferred because of spectral resolution issues. Thus, in this work, we introduce an automated metabolite identification and assignment from 1 H- 1 H TOCSY (total correlation spectroscopy) using real breast cancer tissue. The new approach is based on customized and extended semi-supervised classifiers: KNFST, SVM, third (PC3) and fourth (PC4) degree polynomial. In our approach, metabolic assignment is based only on the vertical and horizontal frequencies of the metabolites in the 1 H- 1 H TOCSY. KNFST and SVM show high performance (high accuracy and low mislabeling rate) in relatively low size of initially labeled training data. PC3 and PC4 classifiers showed lower accuracy and high mislabeling rates, and both classifiers fail to provide an acceptable accuracy at extremely low size (≤9% of the entire dataset) of initial training data. Additionally, semi-supervised classifiers were implemented to obtain a fully automatic procedure for signal assignment and deconvolution of TOCSY, which is a big step forward in NMR metabolic profiling. A set of 27 metabolites were deduced from the TOCSY, and their assignments agreed with the metabolites deduced from a 1D NMR spectrum of the same sample analyzed by conventional human-based methodology., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Authors.)

The eukaryotic flagellum propels sperm cells and simultaneously detects physical and chemical cues that modulate the waveform of the flagellar beat. Most previous studies have characterized the flagellar beat and swimming trajectories in two space dimensions (2D) at a water/glass interface. Here, using refined holographic imaging methods, we report high-quality recordings of three-dimensional (3D) flagellar bending waves. As predicted by theory, we observed that an asymmetric and planar flagellar beat results in a circular swimming path, whereas a symmetric and non-planar flagellar beat results in a twisted-ribbon swimming path. During swimming in 3D, human sperm flagella exhibit torsion waves characterized by maxima at the low curvature regions of the flagellar wave. We suggest that these torsion waves are common in nature and that they are an intrinsic property of beating axonemes. We discuss how 3D beat patterns result in twisted-ribbon swimming paths. This study provides new insight into the axoneme dynamics, the 3D flagellar beat, and the resulting swimming behavior.