Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid QUS Radiomics: A Multimodal-Integrated Quantitative Ultrasound Radiomics for Assessing Ambulatory Function in Duchenne Muscular Dystrophy

Background: Duchenne muscular dystrophy (DMD) is a neuromuscular disorder that affects ambulatory function. Quantitative ultrasound (QUS) imaging, utilizing envelope statistics, has proven effective in diagnosing DMD. Radiomics enables the extraction of detailed features from QUS images. This study further proposes a hybrid QUS radiomics and explores its value in characterizing DMD. Methods: Patients (n = 85) underwent ultrasound examinations of gastrocnemius through Nakagami, homodyned K (HK), and information entropy imaging. The hybrid QUS radiomics extracted, selected, and integrated the retained features derived from each QUS image for classification of ambulatory function using support vector machine. Nested five fold cross-validation of the data was conducted, with the rotational process repeated 50 times. The performance was assessed by averaging the areas under the receiver operating characteristic curve (AUROC). Results: Radiomics enhanced the average AUROC of B-scan, Nakagami, HK, and entropy imaging to 0.790, 0.911, 0.869, and 0.890, respectively. By contrast, the hybrid QUS radiomics using HK and entropy images for diagnosing ambulatory function in DMD patients achieved a superior average AUROC of 0.971 (p <</bold> 0.001 compared with conventional radiomics analysis). Conclusions: The proposed hybrid QUS radiomics incorporates microstructure-related backscattering information from various envelope statistics models to effectively enhance the performance of DMD assessment.
Agroecology: The Ecology of Sustainable Food Systems

Introduction to Agroecology Case for Fundamental Change in Agriculture Agroecology and the Agroecosystem Concept Plants and Abiotic Factors of the Environment The Plant Light Temperature Humidity and Rainfall Wind Soil Water in the Soil Fire A More Complete Autecological Perspective Biotic Factors The Environmental Complex Heterotrophic Organisms System-Level Interactions Population Ecology of Agroecosystems Genetic Resources in Agroecosystems Species Interactions in Crop Communities Agroecosystem Diversity Disturbance, Succession, and Agroecosystem Management Animals in Agroecosystems Energetics of Agroecosystems Landscape Diversity The Transition to Sustainability Converting to Ecologically Based Management Indicators of Sustainability Bringing about a Sustainable World Food System Agriculture, Society, and Agroecology Community and Culture in the Remaking of the Food System From Sustainable Agroecosystems to a Sustainable Food System Glossary References Index
Stakeholders’ Perceptions Regarding Digital Therapeutics Reimbursement in South Korea: Qualitative Study

Background Digital therapeutics (DTx) are therapeutic interventions driven by software and directly provided to patients, allowing them to manage their health with ease in any setting. A growing interest in DTx has spurred a discussion concerning their reimbursement pathways. However, DTx are still at a premature stage, with insufficient evidence on effectiveness, efficiency, and safety. Currently, although industries desire to quickly enter the market, especially by getting their products reimbursed by the National Health Insurance (NHI) fund, the NHI is cautious about DTx due to their uncertainties. Thus, public discussion and social consensus are crucial in deciding whether to reimburse DTx by the NHI fund. Objective This study examined multiple stakeholders’ awareness and attitudes toward DTx and perceptions of regulatory pathways for adopting DTx. Methods In-depth interviews were conducted with 11 stakeholders in South Korea (industry: n=4, health care: n=3, academia: n=2, and consumer: n=2) using semistructured guidelines. They were purposively sampled to identify individuals with expertise in DTx and NHI policies. The interviews were conducted either in person or via a videoconference for 45-70 minutes. Qualitative data were analyzed using directed content analysis, which uses interview guidelines as an analytical framework. Results Findings were divided into three categories: (1) awareness and attitude toward DTx, (2) perception of whether DTx are worth entering the market and being reimbursed by the NHI fund, and (3) perception of how to enter the market and how to reimburse DTx by the NHI fund if they are worth it. Although consumer stakeholders were not familiar with the basic concept of DTx, the other stakeholders understood it thoroughly. However, all participants showed positive attitudes and acceptance of DTx. Most of them responded that DTx are worth entering the market, but they could not reach an agreement on the pathways for DTx to enter the market. Although participants were in favor of the reimbursement of DTx in principle, they responded that a conservative approach is required due to insufficient clinical evidence for DTx. Conclusions We found that stakeholders in South Korea had positive attitudes toward DTx, perceived them as worth using, and agreed to allow them to enter the market. The main issue was not the problem of the technology itself but the difference in opinion as to the pathways for reimbursement. Therefore, this study concluded that the NHI fund, which is operated very conservatively, is insufficient to quickly adopt and implement DTx. Various reimbursement methods, including tax-based financing, raising innovation funds for new technologies, and pilot studies using the NHI fund, should be used to rapidly generate clinical evidence and reduce the uncertainties of DTx to secure a stable market.
Perioperative opioid prescribing and iatrogenic opioid use disorder and overdose: a state-of-the-art narrative review

Background/importance Considerable attention has been paid to identifying and mitigating perioperative opioid-related harms. However, rates of postsurgical opioid use disorder (OUD) and overdose, along with associated risk factors, have not been clearly defined. Objective Evaluate the evidence connecting perioperative opioid prescribing with postoperative OUD and overdose, compare these data with evidence from the addiction literature, discuss the clinical impact of these conditions, and make recommendations for further study. Evidence review State-of-the-art narrative review. Findings Nearly all evidence is from large retrospective studies of insurance claims and Veterans Health Administration (VHA) data. Incidence rates of new OUD within the first year after surgery ranged from 0.1% to 0.8%, while rates of overdose events ranged from 0.01% to 0.8%. Higher rates were seen among VHA patients, which may reflect differences in data completeness and/or risk factors. Identified risk factors included those related to substance use (preoperative opioid use; non-opioid substance use disorders; preoperative sedative, anxiolytic, antidepressant, and gabapentinoid use; and postoperative new persistent opioid use (NPOU)); demographic attributes (chiefly male sex, younger age, white race, and Medicaid or no insurance coverage); psychiatric comorbidities such as depression, bipolar disorder, and PTSD; and certain medical and surgical factors. Several challenges related to the use of administrative claims data were identified; there is a need for more granular retrospective studies and, ideally, prospective cohorts to assess postoperative OUD and overdose incidence with greater accuracy. Conclusions Retrospective data suggest an incidence of new postoperative OUD and overdose of up to 0.8% during the first year after surgery, but prospective studies are lacking.
Cristae formation is a mechanical buckling event controlled by the inner mitochondrial membrane lipidome

Cristae are high‐curvature structures in the inner mitochondrial membrane (IMM) that are crucial for ATP production. While cristae‐shaping proteins have been defined, analogous lipid‐based mechanisms have yet to be elucidated. Here, we combine experimental lipidome dissection with multi‐scale modeling to investigate how lipid interactions dictate IMM morphology and ATP generation. When modulating phospholipid (PL) saturation in engineered yeast strains, we observed a surprisingly abrupt breakpoint in IMM topology driven by a continuous loss of ATP synthase organization at cristae ridges. We found that cardiolipin (CL) specifically buffers the inner mitochondrial membrane against curvature loss, an effect that is independent of ATP synthase dimerization. To explain this interaction, we developed a continuum model for cristae tubule formation that integrates both lipid and protein‐mediated curvatures. This model highlighted a snapthrough instability, which drives IMM collapse upon small changes in membrane properties. We also showed that cardiolipin is essential in low‐oxygen conditions that promote PL saturation. These results demonstrate that the mechanical function of cardiolipin is dependent on the surrounding lipid and protein components of the IMM.
An economic evaluation of two cervical screening algorithms in Belgium: HR-HPV primary compared to HR-HPV and liquid-based cytology co-testing

Objective To assess the costs and benefits of two algorithms for cervical cancer screening in Belgium (1) high-risk human papillomavirus (HR-HPV) primary screening and (2) HR-HPV and liquid-based cytology (LBC) co-testing. Methods A decision tree was adapted from published work and parameterised using HORIZON study data and Belgian cost and population data. The theoretical model represents two different screening algorithms for a cohort of 577 846 women aged 25–64 attending routine cervical screening. Scenario analyses were used to explore the impact of including vaccinated women and alternative pricing approaches. Uncertainty analyses were conducted. Results The cost per woman screened was €113.50 for HR-HPV primary screening and €101.70 for co-testing, representing a total cost of €65 588 573 and €58 775 083, respectively, for the cohort; a 10% difference. For one screening cycle, compared to HR-HPV primary, co-testing resulted in 13 173 more colposcopies, 67 731 more HR-HPV tests and 477 020 more LBC tests. Co-testing identified 2351 more CIN2+ cases per year (27% more than HR-HPV primary) and 1602 more CIN3+ cases (24% more than HR-HPV primary) than HR-HPV primary. Conclusion In Belgium, a co-testing algorithm could increase cervical pre-cancer detection rates compared to HR-HPV primary. Co-testing would cost less than HR-HPV primary if the cost of the HPV test and LBC were cost-neutral compared to the current cost of LBC screening but would cost more if the cost per HPV test and LBC were the same in both co-testing and HR-HPV primary strategies.
Assimilation of Satellite-Observed Snow Albedo in a Land Surface Model

This study assesses the impact of assimilating satellite-observed snow albedo on the Noah land surface model (LSM)-simulated fluxes and snow properties. A direct insertion technique is developed to assimilate snow albedo into Noah and is applied to three intensive study areas in North Park (Colorado) that are part of the 2002/03 Cold Land Processes Field Experiment (CLPX). The assimilated snow albedo products are 1) the standard Moderate Resolution Imaging Spectrometer (MODIS) product (MOD10A1) and 2) retrievals from MODIS observations with the recently developed Pattern-Based Semiempirical (PASS) approach. The performance of the Noah simulations, with and without assimilation, is evaluated using the in situ measurements of snow albedo, upward shortwave radiation, and snow depth. The results show that simulations with albedo assimilation agree better with the measurements. However, because of the limited impact of snow albedo updates after subsequent snowfall, the mean (or seasonal) error statistics decrease significantly for only two of the three CLPX sites. Though the simulated snow depth and duration for the snow season benefit from the assimilation, the greatest improvements are found in the simulated upward shortwave radiation, with root mean squared errors reduced by about 30%. As such, this study demonstrates that assimilation of satellite-observed snow albedo can improve LSM simulations, which may positively affect the representation of hydrological and surface energy budget processes in runoff and numerical weather prediction models.
Characterization of a Far-Infrared Kinetic Inductance Detector Prototype for PRIMA

The PRobe far-Infrared Mission for Astrophysics (PRIMA) is under study as a potential far-IR space mission, featuring actively cooled optics, and both imaging and spectroscopic instrumentation. To fully take advantage of the low background afforded by a cold telescope, spectroscopy with PRIMA requires detectors with a noise equivalent power (NEP) better than $1 \times 10^{-19}$ W Hz$^{-1/2}$. To meet this goal we are developing large format arrays of kinetic inductance detectors (KIDs) to work across the $25-250$ micron range. Here we present the design and characterization of a single pixel prototype detector optimized for $210$ micron. The KID consists of a lens-coupled aluminum inductor-absorber connected to a niobium interdigitated capacitor to form a 2 GHz resonator. We measure the performance of this detector with optical loading in the $0.01 - 300$ aW range. At low loading the detector achieves an NEP of $9\times10^{-20}$ W Hz$^{-1/2}$ at a 10 Hz readout frequency, and the lens-absorber system achieves a good optical efficiency. An extrapolation of these measurements suggest this detector may remain photon noise limited at up to 20 fW of loading, offering a high dynamic range for PRIMA observations of bright astronomical sources.
Accumulating Hints for Flavour Violating Higgses at the Electroweak Scale

A combined explanation of the deviations from Standard Model predictions in $h\to e\tau$, $h\to \mu\tau$, $b\to s \ell^+ \ell^-$, the $W$ mass and $R({D^{(*)}})$ as well as the excess in $t\to bH^+(130\,{\rm GeV})\to b\overline{b}c$ is proposed: We show that a Two-Higgs-Doublet Model with non-minimal flavour violation can simultaneously explain these hints for new physics without violating the stringent bounds from e.g. $\mu\to e\gamma$, $\tau\to \mu\gamma$, $B_s-\bar B_s$ mixing, $b\to s\gamma$, low mass di-jet and $pp\to H^+H^-\to \tau^+\tau^-\nu\bar\nu$ searches. Furthermore, a shift in the SM Higgs coupling strength to tau leptons as well as a non-zero $t\to hc$ rate is predicted, as preferred by recent measurements. We propose three benchmark points providing such a simultaneous explanation and calculate their predictions, including collider signatures which can be tested with upcoming LHC run-3 data.
InterVLS: Interactive Model Understanding and Improvement with Vision-Language Surrogates

Deep learning models are widely used in critical applications, highlighting the need for pre-deployment model understanding and improvement. Visual concept-based methods, while increasingly used for this purpose, face challenges: (1) most concepts lack interpretability, (2) existing methods require model knowledge, often unavailable at run time. Additionally, (3) there lacks a no-code method for post-understanding model improvement. Addressing these, we present InterVLS. The system facilitates model understanding by discovering text-aligned concepts, measuring their influence with model-agnostic linear surrogates. Employing visual analytics, InterVLS offers concept-based explanations and performance insights. It enables users to adjust concept influences to update a model, facilitating no-code model improvement. We evaluate InterVLS in a user study, illustrating its functionality with two scenarios. Results indicates that InterVLS is effective to help users identify influential concepts to a model, gain insights and adjust concept influence to improve the model. We conclude with a discussion based on our study results.
Unveiling Safety Vulnerabilities of Large Language Models

As large language models become more prevalent, their possible harmful or inappropriate responses are a cause for concern. This paper introduces a unique dataset containing adversarial examples in the form of questions, which we call AttaQ, designed to provoke such harmful or inappropriate responses. We assess the efficacy of our dataset by analyzing the vulnerabilities of various models when subjected to it. Additionally, we introduce a novel automatic approach for identifying and naming vulnerable semantic regions - input semantic areas for which the model is likely to produce harmful outputs. This is achieved through the application of specialized clustering techniques that consider both the semantic similarity of the input attacks and the harmfulness of the model's responses. Automatically identifying vulnerable semantic regions enhances the evaluation of model weaknesses, facilitating targeted improvements to its safety mechanisms and overall reliability.
Training Multi-layer Neural Networks on Ising Machine

As a dedicated quantum device, Ising machines could solve large-scale binary optimization problems in milliseconds. There is emerging interest in utilizing Ising machines to train feedforward neural networks due to the prosperity of generative artificial intelligence. However, existing methods can only train single-layer feedforward networks because of the complex nonlinear network topology. This paper proposes an Ising learning algorithm to train quantized neural network (QNN), by incorporating two essential techinques, namely binary representation of topological network and order reduction of loss function. As far as we know, this is the first algorithm to train multi-layer feedforward networks on Ising machines, providing an alternative to gradient-based backpropagation. Firstly, training QNN is formulated as a quadratic constrained binary optimization (QCBO) problem by representing neuron connection and activation function as equality constraints. All quantized variables are encoded by binary bits based on binary encoding protocol. Secondly, QCBO is converted to a quadratic unconstrained binary optimization (QUBO) problem, that can be efficiently solved on Ising machines. The conversion leverages both penalty function and Rosenberg order reduction, who together eliminate equality constraints and reduce high-order loss function into a quadratic one. With some assumptions, theoretical analysis shows the space complexity of our algorithm is $\mathcal{O}(H^2L + HLN\log H)$, quantifying the required number of Ising spins. Finally, the algorithm effectiveness is validated with a simulated Ising machine on MNIST dataset. After annealing 700 ms, the classification accuracy achieves 98.3%. Among 100 runs, the success probability of finding the optimal solution is 72%. Along with the increasing number of spins on Ising machine, our algorithm has the potential to train deeper neural networks.
IC-SECURE: Intelligent System for Assisting Security Experts in Generating Playbooks for Automated Incident Response

Security orchestration, automation, and response (SOAR) systems ingest alerts from security information and event management (SIEM) system, and then trigger relevant playbooks that automate and orchestrate the execution of a sequence of security activities. SOAR systems have two major limitations: (i) security analysts need to define, create and change playbooks manually, and (ii) the choice between multiple playbooks that could be triggered is based on rules defined by security analysts. To address these limitations, recent studies in the field of artificial intelligence for cybersecurity suggested the task of interactive playbook creation. In this paper, we propose IC-SECURE, an interactive playbook creation solution based on a novel deep learning-based approach that provides recommendations to security analysts during the playbook creation process. IC-SECURE captures the context in the form of alert data and current status of incomplete playbook, required to make reasonable recommendation for next module that should be included in the new playbook being created. We created three evaluation datasets, each of which involved a combination of a set of alert rules and a set of playbooks from a SOAR platform. We evaluated IC-SECURE under various settings, and compared our results with two state-of-the-art recommender system methods. In our evaluation IC-SECURE demonstrated superior performance compared to other methods by consistently recommending the correct security module, achieving precision@1>0.8 and recall@3>0.92
Predicting Age from White Matter Diffusivity with Residual Learning

Imaging findings inconsistent with those expected at specific chronological age ranges may serve as early indicators of neurological disorders and increased mortality risk. Estimation of chronological age, and deviations from expected results, from structural magnetic resonance imaging (MRI) data has become an important proxy task for developing biomarkers that are sensitive to such deviations. Complementary to structural analysis, diffusion tensor imaging (DTI) has proven effective in identifying age-related microstructural changes within the brain white matter, thereby presenting itself as a promising additional modality for brain age prediction. Although early studies have sought to harness DTI’s advantages for age estimation, there is no evidence that the success of this prediction is owed to the unique microstructural and diffusivity features that DTI provides, rather than the macrostructural features that are also available in DTI data. Therefore, we seek to develop white-matter-specific age estimation to capture deviations from normal white matter aging. Specifically, we deliberately disregard the macrostructural information when predicting age from DTI scalar images, using two distinct methods. The first method relies on extracting only microstructural features from regions of interest (ROIs). The second applies 3D residual neural networks (ResNets) to learn features directly from the images, which are non-linearly registered and warped to a template to minimize macrostructural variations. When tested on unseen data, the first method yields mean absolute error (MAE) of 6.11 ± 0.19 years for cognitively normal participants and MAE of 6.62 ± 0.30 years for cognitively impaired participants, while the second method achieves MAE of 4.69 ± 0.23 years for cognitively normal participants and MAE of 4.96 ± 0.28 years for cognitively impaired participants. We find that the ResNet model captures subtler, non-macrostructural features for brain age prediction.