Monte Carlo Inverse RNA Folding.

The inverse RNA folding problem deals with designing a sequence of nucleotides that will fold into a desired target structure. Generalized Nested Rollout Policy Adaptation (GNRPA) is a Monte Carlo search algorithm for optimizing a sequence of choices. It learns a playout policy to intensify the search of the state space near the current best sequence. The algorithm uses a prior on the possible actions so as to perform non uniform playouts when learning the instance of problem at hand. We trained a transformer neural network on the inverse RNA folding problem using the Rfam database. This network is used to generate a prior for every Eterna100 puzzle. GNRPA is used with this prior to solve some of the instances of the Eterna100 dataset. The transformer prior gives better result than handcrafted heuristics.

Empowering healthcare education: A multilingual ontology for medical informatics and digital health (MIMO) integrated to artificial intelligence powered training in smart hospitals.

Objective: A comprehensive understanding of professional and technical terms is essential to achieving practical results in multidisciplinary projects dealing with health informatics and digital health. The medical informatics multilingual ontology (MIMO) initiative has been created through international cooperation. MIMO is continuously updated and comprises over 3700 concepts in 37 languages on the Health Terminology/Ontology Portal (HeTOP). Methods: We conducted case studies to assess the feasibility and impact of integrating MIMO into real-world healthcare projects. In HosmartAI, MIMO is used to index technological tools in a dedicated marketplace and improve partners' communication. Then, in SaNuRN, MIMO supports the development of a "Catalog and Index of Digital Health Teaching Resources" (CIDHR) backing digital health resources retrieval for health and allied health students. Results: In HosmartAI, MIMO facilitates the indexation of technological tools and smooths partners' interactions. In SaNuRN within CIDHR, MIMO ensures that students and practitioners access up-to-date, multilingual, and high-quality resources to enhance their learning endeavors. Conclusion: Integrating MIMO into training in smart hospital projects allows healthcare students and experts worldwide with different mother tongues and knowledge to tackle challenges facing the health informatics and digital health landscape to find innovative solutions improving initial and continuous education.

Valley-Polarized Topological Phases with In-Plane Magnetization.

The coexistence of valley polarization and topology has considerably facilitated the applications of 2D materials toward valleytronics device technology. However, isolated and distinct valleys are required to observe the valley-related quantum phenomenon. Herein, we report a new mechanism to generate in-plane magnetization direction-dependent isolated valley carriers by preserving or breaking the mirror symmetry in a 2D system. First-principle calculations are carried out on a prototype material, W2MnC2O2 MXene, to demonstrate the mechanism. A valley-coupled topological phase transition among Weyl semimetal, valley-polarized quantum anomalous Hall insulator, and topological semimetal is observed by manipulating the in-plane magnetization directions in W2MnC2O2. Monte Carlo simulations of W2MnC2O2 show that the estimated Curie temperature is around 170 K, indicating the possibility of observing valley-polarized topological states at higher temperatures. Our finding provides a generalized platform for investigating the valley and topological physics, which is extremely important for future quantum information processing applications.

Fusion of spectral and topographic features for land use mapping using a machine learning framework for a regional scale application.

This study investigated the dynamics of land use and land cover (LULC) modelling, mapping, and assessment in the Kegalle District of Sri Lanka, where policy decision-making is crucial in agricultural development where LULC temporal datasets are not readily available. Employing remotely sensed datasets and machine learning algorithms, the work presented here aims to compare the accuracy of three classification approaches in mapping LULC categories across the time in the study area primarily using the Google Earth Engine (GEE). Three classifiers namely random forest (RF), support vector machines (SVM), and classification and regression trees (CART) were used in LULC modelling, mapping, and change analysis. Different combinations of input features were investigated to improve classification performance. Developed models were optimised using the grid search cross-validation (CV) hyperparameter optimisation approach. It was revealed that the RF classifier constantly outstrips SVM and CART in terms of accuracy measures, highlighting its reliability in classifying the LULC. Land cover changes were examined for two periods, from 2001 to 2013 and 2013 to 2022, implying major alterations such as the conversion of rubber and coconut areas to built-up areas and barren lands. For suitable classification with higher accuracy, the study suggests utilising high spatial resolution satellite data, advanced feature selection approaches, and a combination of several spatial and spatial-temporal data sources. The study demonstrated practical applications of derived temporal LULC datasets for land management practices in agricultural development activities in developing nations.

Automated Neural Architecture Search for Cardiac Amyloidosis Classification from [18F]-Florbetaben PET Images.

Medical image classification using convolutional neural networks (CNNs) is promising but often requires extensive manual tuning for optimal model definition. Neural architecture search (NAS) automates this process, reducing human intervention significantly. This study applies NAS to [18F]-Florbetaben PET cardiac images for classifying cardiac amyloidosis (CA) sub-types (amyloid light chain (AL) and transthyretin amyloid (ATTR)) and controls. Following data preprocessing and augmentation, an evolutionary cell-based NAS approach with a fixed network macro-structure is employed, automatically deriving cells' micro-structure. The algorithm is executed five times, evaluating 100 mutating architectures per run on an augmented dataset of 4048 images (originally 597), totaling 5000 architectures evaluated. The best network (NAS-Net) achieves 76.95% overall accuracy. K-fold analysis yields mean ± SD percentages of sensitivity, specificity, and accuracy on the test dataset: AL subjects (98.7 ± 2.9, 99.3 ± 1.1, 99.7 ± 0.7), ATTR-CA subjects (93.3 ± 7.8, 78.0 ± 2.9, 70.9 ± 3.7), and controls (35.8 ± 14.6, 77.1 ± 2.0, 96.7 ± 4.4). NAS-derived network performance rivals manually determined networks in the literature while using fewer parameters, validating its automatic approach's efficacy.

The evolution of public attention in acute kidney injury and continuous renal replacement therapy: trends analysis from 2004 to 2024.

Background: Acute kidney injury (AKI) and the need for Continuous Renal Replacement Therapy (CRRT) are critically important health concerns. This study analyzes global and regional Internet search queries to understand public attention in AKI and CRRT over time. Methods: We used Google Trends™ to analyze search queries for AKI and CRRT from January 2004 to March 2024. The study examined global trends and detailed insights from the United States, including state-by-state breakdowns. We identified patterns, peaks of attention, and temporal trends in public attention, comparing regional variations across the US and top-ranking countries worldwide. Results: Global attention in AKI peaked in October 2022, with Portugal, Zambia, and Spain showing the highest regional attention. Within the United States, peak attention was in February 2008. Tennessee, Pennsylvania, and West Virginia were the top states that paid attention to AKI. Attention in CRRT peaked globally in March 2024. South Korea, Saudi Arabia, and Bahrain have led the global attention to CRRT. In the United States, peak attention was in April 2020. West Virginia, Tennessee, and Kentucky showed the highest state-specific attention in CRRT. Conclusions: This study reveals significant temporal and geographical variations in online search patterns for AKI and CRRT, suggesting evolving public attention to these critical health issues. This knowledge can guide the development of targeted public health initiatives, enhance medical education efforts, and help healthcare systems tailor their approach to improving awareness and outcomes in kidney health across diverse populations.

Individual differences in direction-selective motion adaptation revealed by change-detection performance.

The motion aftereffect (MAE) and motion adaptation in general are usually considered to be universal phenomena. However, in a preliminary study using a bias-free measure of the MAE we found some individuals who showed at best a weak effect of adaptation. These same individuals also performed poorly in a "change detection" test of motion adaptation based on visual search, leading to the conjecture that there is a bimodality in the population with respect to motion adaptation. The present study tested this possibility by screening 102 participants on two versions of the change-detection task while also considering potential confounding factors including eye movements, practice-based improvements, and deficits in visual search ability. The 5 strongest and the 5 weakest change detectors were selected for further testing of motion detection and contrast detection after adaptation. Data showed an inverse association between change-detection ability and performance in the motion-detection task. We extend previous findings by also showing i) the weakest change detectors exhibit less direction selectivity in their contrast thresholds after adapting to drifting gratings and ii) the ability to detect change in motion direction correlates with the ability to detect change in spatial orientation. Group differences between the strongest and weakest change detectors cannot be attributed to a lack of practice, nor can they be explained by poor fixation ability. Our results suggest genuine individual differences in the degree to which adaptation is specific to stimulus orientation and direction of motion.

Analyzing and Optimizing the Distribution of Blood Lead Level Testing for Children in New York City: A Data-Driven Approach.

This study investigates blood lead level (BLL) rates and testing among children under 6 years of age across the 42 neighborhoods in New York City from 2005 to 2021. Despite a citywide general decline in BLL rates, disparities at the neighborhood level persist and are not addressed in the official reports, highlighting the need for this comprehensive analysis. In this paper, we analyze the current BLL testing distribution and cluster the neighborhoods using a k-medoids clustering algorithm. We propose an optimized approach that improves resource allocation efficiency by accounting for case incidences and neighborhood risk profiles using a grid search algorithm. Our findings demonstrate statistically significant improvements in case detection and enhanced fairness by focusing on under-served and high-risk groups. Additionally, we propose actionable recommendations to raise awareness among parents, including outreach at local daycare centers and kindergartens, among other venues.

Osteoarthritis year in review 2024: biomechanics.

OBJECTIVE: We aimed to systematically review and summarize the literature of the past year on OA and biomechanics, to highlight gaps and challenges, and to present some promising approaches and developments. METHODS: A systematic literature search was conducted using Pubmed and the Web of Science Core Collection. We included original articles and systematic reviews on OA and biomechanics in human subjects published between April 2023 and April 2024. RESULTS: Of the 155 studies that met the inclusion criteria, 9 were systematic reviews and 146 were original (mostly cross-sectional) studies that included a total of 6488 patients and 1921 controls with a mean age of 57.5 and 44.7 years, respectively. Promising advances have been made in medical imaging of affected soft tissue structures, the relationship between soft tissue properties and biomechanical changes in OA, new technologies to facilitate easier assessment of ambulatory biomechanics, and personalized physics-based models that also include complex chemical and mechanobiological mechanisms, all of which are relevant to gaining mechanistic insights into the pathophysiology of OA. CONCLUSIONS: There is still an unmet need for larger longitudinal data sets that combine clinical, radiological, and biomechanical outcomes to characterize the biomechanical fingerprint that underlies the trajectory of functional decline and biomechanical phenotypes of OA. In addition, criteria and guidelines for control groups, as well as methods and standards for model verification allowing for comparisons between studies are needed.

Effects of perceived stress on college students' sleep quality: a moderated chain mediation model.

BACKGROUND: Perceived Stress has been shown as a key contributor to sleep quality, but the underlying mechanism between perceived stress and sleep quality remains unknown. This study aimed to investigate the impact of perceived stress on sleep quality of college students and the chain mediating roles of presence of meaning in life (PML) and depression, as well as the moderating role of search for meaning in life (SML). METHODS: Participants were 8178 college students (4599 boys and 3579 girls; Mage = 19.10 years, SD = 1.08) who completed self-report questionnaire, including the Perceived Stress Scale (PSS), the Pittsburgh Sleep Quality Index (PSQI), the Meaning in Life Questionnaire (MLQ), and the Patient Health Questionnaire-9 (PHQ-9). RESULTS: The results showed that higher perceived stress was directly related to poorer sleep quality. This negative impact on sleep quality was mediated through the chained roles of PML and depression. Additionally, the study found that SML moderates the influence of perceived stress, PML and depression on sleep quality. Specifically, for individuals actively search for meaning, the adverse effects of perceived stress and depression on sleep quality are diminished. Concurrently, the positive influence of PML on sleep quality is enhanced. CONCLUSION: This study revealed that the PML and depression mediate the effect of perceived stress on sleep quality, with SML playing a significant protective role. These results emphasize the necessity of integrating strategies to enhance PML and SML into interventions designed to improve emotion management and sleep quality among college students.

Urban growth simulation and scenario projection for the arid regions using heuristic cellular automata.

Arid regions tend to form compact urban patterns that have significant implications on urban growth and future urban patterns. Spatial simulation and projection using cellular automata (CA)-based models are important for achieving sustainable urban development in arid regions. In response to this need, we developed a new CA model (GSA-CA) using the gravitational search algorithm (GSA) to capture and project urban growth patterns in arid regions. We calibrated the GSA-CA model for the arid city of Urumqi in Northwest China from 2000 to 2010, and validated the model from 2010 to 2020, and then applied to project urban growth in 2040. The results indicated that the optimal performance of the model was achieved when the fraction of the population was 0.5. GSA-CA achieved an overall accuracy of 98.42% and a figure of merit (FOM) of 43.03% for the year 2010, and an overall accuracy of 98.52% with FOM of 37.64% for 2020. The results of the study help to adjust urban planning and development policies. The developed model has the potential to be employed in simulating urban growth and future scenarios in arid regions globally, including Northwest China and Africa.

Persistent resampling of external information despite 25 repetitions of the same visual search templates.

We commonly load visual working memory minimally when to-be-remembered information remains available in the external world. In visual search, this is characterised by participants frequently resampling previously encoded templates, which helps minimize cognitive effort and improves task performance. If all search templates have been rehearsed many times, they should become strongly represented in memory, possibly eliminating the benefit of reinspections. To test whether repetition indeed leads to less resampling, participants searched for sets of 1, 2, and 4 continuously available search templates. Critically, each unique set of templates was repeated 25 trials consecutively. Although the number of inspections and inspection durations initially decreased strongly when a template set was repeated, behaviour largely stabilised between the tenth and last repetition: Participants kept resampling templates frequently. In Experiment 2, participants performed the same task, but templates became unavailable after 15 repetitions. Strikingly, accuracy remained high even when templates could not be inspected, suggesting that resampling was not strictly necessary in later repetitions. We further show that seemingly 'excessive' resampling behaviour had no direct within-trial benefit to speed nor accuracy, and did not improve performance on long-term memory tests. Rather, we argue that resampling was partially used to boost metacognitive confidence regarding memory representations. As such, eliminating the benefit of minimizing working memory load does not eliminate the persistence with which we sample information from the external world - although the underlying reason for resampling behaviour may be different.

A Linear Regression Approach for Best Scanline Determination in the Object to Image Space Transformation Using Pushbroom Images.

The use of linear array pushbroom images presents a new challenge in photogrammetric applications when it comes to transforming object coordinates to image coordinates. To address this issue, the Best Scanline Search/Determination (BSS/BSD) field focuses on obtaining the Exterior Orientation Parameters (EOPs) of each individual scanline. Current solutions are often impractical for real-time tasks due to their high time requirements and complexities. This is because they are based on the Collinearity Equation (CE) in an iterative procedure for each ground point. This study aims to develop a novel BSD framework that does not need repetitive usage of the CE with a lower computational complexity. The Linear Regression Model (LRM) forms the basis of the proposed BSD approach and uses Simulated Control Points (SCOPs) and Simulated Check Points (SCPs). The proposed method is comprised of two main steps: the training phase and the test phase. The SCOPs are used to calculate the unknown parameters of the LR model during the training phase. Then, the SCPs are used to evaluate the accuracy and execution time of the method through the test phase. The evaluation of the proposed method was conducted using ten various pushbroom images, 5 million SCPs, and a limited number of SCOPs. The Root Mean Square Error (RMSE) was found to be in the order of ten to the power of negative nine (pixel), indicating very high accuracy. Furthermore, the proposed approach is more robust than the previous well-known BSS/BSD methods when handling various pushbroom images, making it suitable for practical and real-time applications due to its high speed, which only requires 2-3 s of time.

TrajectoryNAS: A Neural Architecture Search for Trajectory Prediction.

Autonomous driving systems are a rapidly evolving technology. Trajectory prediction is a critical component of autonomous driving systems that enables safe navigation by anticipating the movement of surrounding objects. Lidar point-cloud data provide a 3D view of solid objects surrounding the ego-vehicle. Hence, trajectory prediction using Lidar point-cloud data performs better than 2D RGB cameras due to providing the distance between the target object and the ego-vehicle. However, processing point-cloud data is a costly and complicated process, and state-of-the-art 3D trajectory predictions using point-cloud data suffer from slow and erroneous predictions. State-of-the-art trajectory prediction approaches suffer from handcrafted and inefficient architectures, which can lead to low accuracy and suboptimal inference times. Neural architecture search (NAS) is a method proposed to optimize neural network models by using search algorithms to redesign architectures based on their performance and runtime. This paper introduces TrajectoryNAS, a novel neural architecture search (NAS) method designed to develop an efficient and more accurate LiDAR-based trajectory prediction model for predicting the trajectories of objects surrounding the ego vehicle. TrajectoryNAS systematically optimizes the architecture of an end-to-end trajectory prediction algorithm, incorporating all stacked components that are prerequisites for trajectory prediction, including object detection and object tracking, using metaheuristic algorithms. This approach addresses the neural architecture designs in each component of trajectory prediction, considering accuracy loss and the associated overhead latency. Our method introduces a novel multi-objective energy function that integrates accuracy and efficiency metrics, enabling the creation of a model that significantly outperforms existing approaches. Through empirical studies, TrajectoryNAS demonstrates its effectiveness in enhancing the performance of autonomous driving systems, marking a significant advancement in the field. Experimental results reveal that TrajcetoryNAS yields a minimum of 4.8 higger accuracy and 1.1* lower latency over competing methods on the NuScenes dataset.

SciScribe: Automating & Contextualizing Literature Reviews in Cardiac Surgery.

OBJECTIVES: The task of writing structured content reviews and guidelines has grown stronger and more complex. We propose to go beyond search tools, toward curation tools, by automating time-consuming and repetitive steps of extracting and organizing information. METHODS: SciScribe is built as an extension of IBM's Deep Search platform, which provides document processing and search capabilities. This platform was used to ingest and search full-content publications from PubMed Central (PMC) and official, structured records from the ClinicalTrials and OpenPayments databases. Author names and NCT numbers, mentioned within the publications, were used to link publications to these official records as context. Search strategies involve traditional keyword-based search as well as natural language question and answering via large language models (LLMs). RESULTS: SciScribe is a web-based tool that helps accelerate literature reviews through key features: 1. Accumulate a personal collection from publication sources, such as PMC or other sources; 2. Incorporate contextual information from external databases into the presented papers, promoting a more informed assessment by readers. 3. Semantic question and answering of a document to quickly assess relevance and hierarchical organization. 4. Semantic question answering for each document within a collection, collated into tables. CONCLUSIONS: Emergent language processing techniques open new avenues to accelerate and enhance the literature review process, for which we have demonstrated a use case implementation within cardiac surgery. SciScribe automates and accelerates this process, mitigates errors associated with repetition and fatigue, as well as contextualizes results by linking relevant external data sources, instantaneously.

Reliability of Serum-Derived Connectome Indicators in Identifying Cirrhosis.

Patients with cirrhosis face a heightened risk of complications, underscoring the importance of identification. We have developed a Connectome strategy that combines metabolites with peptide spectral matching (PSM) in proteomics to integrate metabolomics and proteomics, identifying specific metabolites bound to blood proteins in cirrhosis using open search proteomics methods. Analysis methods including Partial Least Squares Discriminant Analysis (PLS-DA), Uniform Manifold Approximation and Projection (UMAP), and hierarchical clustering were used to distinguish significant differences among the Cirrhosis group, Chronic Hepatitis B (CHB) group, and Healthy group. In this study, we identified 81 cirrhosis-associated connectomes and established an effective model distinctly distinguishing cirrhosis from chronic hepatitis B and healthy samples, confirmed by PLS-DA, hierarchical clustering analysis, and UMAP analysis, and further validated using six new cirrhosis samples. We established a Unified Indicator for Identifying cirrhosis, including tyrosine, Unnamed_189.2, thiazolidine, etc., which not only enables accurate identification of cirrhosis groups but was also further validated using six new cirrhosis samples and extensively supported by other cirrhosis research data (PXD035024). Our study reveals that characteristic cirrhosis connectomes can reliably distinguish cirrhosis from CHB and healthy groups. The established unified cirrhotic indicator facilitates the identification of cirrhosis cases in both this study and additional research data.

Considerations on expanding criminal offender DNA databases with Y-STR profiles.

Although national criminal offender DNA databases (NCODDs) including autosomal short tandem repeats (STRs) have been a successful tool to identify criminals for decades in many countries, yet there are many criminal cases they cannot solve. In cases with mixed male-female samples, particularly sexual assault, expanding NCODDs with Y-chromosomal STR (Y-STR) profiles allows database matching in the absence of autosomal STR profiles. Although Y-STR matches are not individual-specific, this can be largely overcome with rapidly mutating Y-STRs (RM Y-STR) allowing separation of paternally related men. Expanding NCODDs with Y-STR profiles is also beneficial for law enforcement in cases without known suspects via familial searching. Expanding NCODDs with Y-STR profiles may raise concerns about genetic privacy and fundamental human rights. A legal analysis of the European Convention on Human Rights revealed that when primarily for reidentifying convicted sex offenders, it would be in line with the case law of the European Court of Human Rights, while a generalized approach primarily for familial searching and involving all types of offenders may not. This paper aims to stimulate a debate among various stakeholders regarding the benefits and risks of expanding NCODDs with Y-STR profiles that in some countries has already been practically implemented.

Multi-dimensional epidemiology and informatics data on COVID-19 wave at the end of zero COVID policy in China.

Background: China exited strict Zero-COVID policy with a surge in Omicron variant infections in December 2022. Given China's pandemic policy and population immunity, employing Baidu Index (BDI) to analyze the evolving disease landscape and estimate the nationwide pneumonia hospitalizations in the post Zero COVID period, validated by hospital data, holds informative potential for future outbreaks. Methods: Retrospective observational analyses were conducted at the conclusion of the Zero-COVID policy, integrating internet search data alongside offline records. Methodologies employed were multidimensional, encompassing lagged Spearman correlation analysis, growth rate assessments, independent sample T-tests, Granger causality examinations, and Bayesian structural time series (BSTS) models for comprehensive data scrutiny. Results: Various diseases exhibited a notable upsurge in the BDI after the policy change, consistent with the broader trajectory of the COVID-19 pandemic. Robust connections emerged between COVID-19 and diverse health conditions, predominantly impacting the respiratory, circulatory, ophthalmological, and neurological domains. Notably, 34 diseases displayed a relatively high correlation (r > 0.5) with COVID-19. Among these, 12 exhibited a growth rate exceeding 50% post-policy transition, with myocarditis escalating by 1,708% and pneumonia by 1,332%. In these 34 diseases, causal relationships have been confirmed for 23 of them, while 28 garnered validation from hospital-based evidence. Notably, 19 diseases obtained concurrent validation from both Granger causality and hospital-based data. Finally, the BSTS models approximated approximately 4,332,655 inpatients diagnosed with pneumonia nationwide during the 2 months subsequent to the policy relaxation. Conclusion: This investigation elucidated substantial associations between COVID-19 and respiratory, circulatory, ophthalmological, and neurological disorders. The outcomes from comprehensive multi-dimensional cross-over studies notably augmented the robustness of our comprehension of COVID-19's disease spectrum, advocating for the prospective utility of internet-derived data. Our research highlights the potential of Internet behavior in predicting pandemic-related syndromes, emphasizing its importance for public health strategies, resource allocation, and preparedness for future outbreaks.