bluuebunny/crossref_metadata_2025 huggingface dataset:
Improved value iteration network for path planning
Authors: Chai Shijia
Published: November 2023

In this study, a series of improved path planning algorithms are designed for path planning tasks in autonomous control based on deep reinforcement learning. The Value Iteration Network (VIN) is used to deal with the path planning problem. Origin VIN performs well on small size maps, but when it comes to a bigger size of map on test set, the success rate decreased. In order to solve the problem that origin VIN lacks long-distance multi-step planning ability on large maps and generalization ability is insufficient, a three-step improvement was made. First of all, in view of the inconvenient data flow and the disappearance of gradients caused by the network being too deep, the jump connection structure is used to obtain the deeper VIN, in which the accuracy of the experiment is improved. Secondly, with the purpose of solving the problem that the complexity of the model is greatly increased due to the deepening of the network, Batch normalization is used to obtain a new network with dueling architecture plus batch normalization layer, which further accelerates the convergence speed of the network. Third, to deal with the global path planning problem on the big map, the hierarchical network structure is adopted for hierarchical value iteration, and the Hierarchical Structure VIN is obtained. In Hierarchical Structure VIN, the long-term planning ability and generalization ability of the algorithm have been significantly improved, and the algorithm could figure out the large-scale and complex path planning problem.
Research on application of model ensemble in sports image classification based on environmental information
Authors: Cui Jiacheng
Published: November 2023

In recent years, a deep convolutional neural network has been widely used in the field of image classification. However, training a satisfactory network is very arduous, not only to tune the hyper-parameters in the network but also to avoid the overfitting problem caused by a deep neural network. Another point is that it is difficult for a neural network to learn subtle details without human annotation. Therefore this paper proposed a preferable classification algorithm for sports classification tasks that combines the deep neural network with the object detection algorithm to obtain the prediction result. In this paper, the author compared the differences between classification directly using neural networks and a modified model ensemble approach to classification from a holistic perspective, as well as elaborating the advantages and disadvantages of the two approaches. The conclusion shows that the use of the improved model ensemble classification algorithm performs better than the direct use of neural networks and also achieves a high degree of accuracy in the test set.
Research advanced in the visual SLAM methods under indoor environment
Authors: Lu Zhengsheng , Wang Jinchang , Zhang Zelin , Zhu Conghua
Published: November 2023

Simultaneous localization and mapping (SLAM) have been a hotspot study topic in the computer vision community these days, which aims to locate the agent’s position and perceive the surrounding environment to build a map. In Visual SLAM, the camera is the external sensor used, creating an environment map while locating itself. Compared with radar and other rangefinders, the visual sensor is usually tiny and has low power consumption, which has been the mainstream framework in SLAM research task. This paper starts from the system overview of visual SLAM, introduces the landmark achievements and research hotspots of visual SLAM, and discusses the key issues and three research methods based on visual SLAM in indoor environment. Meanwhile, the applications of visual SLAM in dynamic scenes and large indoor environments are listed. Finally, the trend of its future development is prospected.
On the convergence acceleration of vanilla Generative Adversarial Network
Authors: Si Hang
Published: November 2023

Generative Adversarial Network (GAN) is very successful and widely used in both academic and industrial fields. It is inspired by game theory where two models, a generator and a critic, compete with each other, learn from each other, and make each other stronger and stronger. Most improvements focused on stability and there are already many different models such as deep convolutional GAN (DCGAN) and Wassertein GAN (WGAN) that have succeeded. But the speed of the convergence during training process is also an essential ability that needs to be enhanced. In this paper, a decreasing noise is added to the MNIST data and these new data are delivered to the discriminator of the vanilla GAN to accelerate the training process. The result shows that this change could accelerate the convergence and not affect the results’ quality a lot. And this improvement should be general enough to apply to the other models.
Comprehensive study and analysis for StarGAN and StarGAN-v2 in the task of image generation
Authors: Cai Yuanan , Li Yepeng , Zhang Zhenyu
Published: November 2023

With the continuous development of deep learning and artificial intelligence industry, more and more neural networks have been developed. Among them, one typical network widely used is StarGAN. It solves the problem of requiring multiple generators and discriminators for multiple domains and style transitions. In this article, we will analyze and explore StarGAN v1 and StarGAN v2. including StarGAN’s development history, advantages and disadvantages in the application, etc. At the same time, we will compare StarGAN with other neural networks such as CycleGAN and MSGAN through some discrimination criteria, and clearly demonstrate the influence of different networks on the generation of image data sets through quantitative and qualitative analysis. In addition, we also found a representative code on some of the actual uses of StarGAN for hair replacement through online sources. Since this code may be used to confuse the use of StarGAN v1 and v2, we will also improve this code in the article and put forward suggestions for changes. In this paper, we carried out the specific analysis of StarGAN’s loss function based on the existing data and put forward some relevant insights. By taking the data we searched for StarGAN, it can be found that StarGAN could use as few resources as possible to achieve clearer and more varied results during the image generation task
Explore the role and emphasis of K-Means, Decision Tree and Distance Based algorithms in data exception detection
Authors: Chen Xiaotian
Published: November 2023

K-Means, Decision Tree and Distance-Based algorithms are 3 important ways of classifying data. These three algorithms have different methods and focus on data classification. Therefore, they are always applied into different scenarios. K-Means algorithm is to divide three-dimensional data or two-dimensional data into several clusters to facilitate subsequent data processing and analysis. Decision Tree is based on the “tree” structure to make decisions. It is an important classification and regression method in data mining technology. It is a prediction analysis model expressed in the form of tree structure (including binary tree and multi tree). When it comes to the Distance-Based algorithm, it is a common anomaly detection method applicable to various data domains. It defines outliers based on the nearest neighbor distance. The purpose of this paper is to explore the kernel of three anomaly detection algorithms through an example of data anomaly detection. Therefore, in this paper, all three algorithms’ commonalities and differences will be discussed and illustrated through a case of data exception handling.
Researches advanced in the SLAM for dynamic environment
Authors: Li Ying
Published: November 2023

Simultaneous Localization and Mapping (SLAM) has always been a popular topic in the computer vision community, as it seeks to forecast the location of agents and use sensors to detect their surroundings in order to construct maps and perform navigation. The majority of SLAM algorithms assume that objects in the environment are immobile or in slow motion, whereas the SLAM system in a dynamic world remains an unanswered question. When constructing a 3D point cloud map by sequentially accumulating scanning data, dynamic objects typically leave undesirable traces in the map. The traces of these dynamic items operate as impediments, hindering the positioning and navigation performance of mobile vehicles. In this work, we discuss the most recent advancements in SLAM in a dynamic context. Specifically, this paper examines three characteristics of the most significant negative effects that the dynamic environment has on SLAM. Then, according to the dynamic degree, solutions are presented for various dynamic items, including the design concept, basic framework, advantages, and disadvantages. We conclude by discussing the existing research difficulties in the subject of dynamic SLAM and its projected development path.
Simulations of quantum dynamics with fermionic phase-space representations using numerical matrix factorizations as stochastic gauges
Authors: Rousse François , Fasi Massimiliano , Dmytryshyn Andrii , Gulliksson Mårten , Ögren Magnus
Published: November 2023

The Gaussian phase-space representation can be used to implement quantum dynamics for fermionic particles numerically. To improve numerical results, we explore the use of dynamical diffusion gauges in such implementations. This is achieved by benchmarking quantum dynamics of few-body systems against independent exact solutions. A diffusion gauge is implemented here as a so-called noise-matrix, which satisfies a matrix equation defined by the corresponding Fokker–Planck equation of the phase-space representation. For the physical systems with fermionic particles considered here, the numerical evaluation of the new diffusion gauges allows us to double the practical simulation time, compared with hitherto known analytic noise-matrices. This development may have far reaching consequences for future quantum dynamical simulations of many-body systems.
Comparative evaluation of alternatives for management of wood wastes by using multi-criteria decision tools
Authors: Sayın Fatma Ece , Topaloğlu Gülay , Ozbay Bilge , Ozbay Ismail
Published: November 2023

In Turkey, the wood industry is a prevailing sector generating economically valuable by-products such as sawdust, wood chips and wood shavings. The utilization of these materials supports an important contribution to the revival of the economy. Selection of the best management alternative considering economic and environmental factors synchronously requires the application of statistical decision-making methods. The significance of this study is to determine the suitability of promising alternatives for the utilization of wood wastes (production of pellet, panel, cemented wood composites and pyrolysis) by using multi-criteria decision-making methods (PROMETHEE and FPROMETHEE). Super Decision Program was used to evaluate the importance of the selected criteria (environmental impact, operation risk, installation cost, applicability, operation cost and market competition) based on data obtained from the 9-scale questionnaire. In PROMETHEE and FPROMETHEE applications, both equally weighted and calculated importance of criteria was considered. According to results of AHP, installation cost was the most important criterion with 31% ratio. Results of the study showed that pellet production was the best alternative in all applications with the highest-ranking values. Wood-based panel production was followed by pellet production; however, it was seen that the production of cemented wood composites and pyrolysis are not suitable for the utilization of wood wastes.
Soil oribatid mite community structure and its relationship with environmental factors in different agricultural land-use types in Shibing Karst
Authors: Gong Tong , Yin Xiumei , Chen Hu
Published: November 2023

The karst world natural heritage site is characterized by fragility due to severe rocky desertification. Buffer zone functions as the ecological barrier of the core area of the heritage site, and can play an ecological filtering role in the core area. Therefore, buffer zones are of great ecological significance. In order to reveal the community characteristics of soil oribatid mites in different agricultural land-use in the buffer zone of Shibing Karst World Natural Heritage and their relationship with environmental factors, soil oribatid mites in the buffer zone of corn field (CF), golden pear garden (GP) and tobacco field (TF) were collected in September 2021. A total of 1220 oribatid mites belonging to 79 genera and 41 families were found in the study. Our key results include (1) The family number, genus number, individual number, and individual density of GP are higher (2) Scheloribates is the dominant group, 22 genera such as Oppiella, Tectocepheus, and Xylobates are common groups, and 56 genera such as Neoribates and Protoribates are rare groups. (3) The Diversity index of GP is higher than that of other cultivated land types. Community similarity analysis shows that the similarity between GP and CF is high, but all habitats are at a medium level of dissimilarity. (4) Analysis of the ecological group of oribatid mites shows that Poronota is more abundant than Macropylina and Gymnonota. (5) The Diversity index, richness index, family and genus level of oribatid mites were significantly positively correlated with soil humidity (SH) and the number of genera, individuals and individual density were significantly positively correlated with total phosphorus (TP). Redundancy analysis indicates that total phosphorus (TP), total potassium (TK), and soil organic matter (SOM) are the main environmental factors affecting the distribution of soil oribatid mites communities. Based on the above research results, it can be seen that there are differences in the community structure of soil oribatid mites in different agricultural land-use in the study area. The use of dominant genera of mites can preliminarily indicate the soil ecological environment of different agricultural land-use. The research results provide basic data for the biodiversity protection of soil animals in different agricultural land-use in the buffer zone of Shibing Karst World Natural Heritage.
Artificial intelligent identification of apatite fission tracks based on machine learning
Authors: Ren Zuoting , Li Shichao , Xiao Perry , Yang Xiaopeng , Wang Hongtao
Published: November 2023

Over the past half century, apatite fission track (AFT) thermochronometry has been widely used in the studies of thermal histories of Earth’s uppermost crust. The acquired thermal histories in turn can be used to quantify many geologic processes such as erosion, sedimentary burial, and tectonic deformation. However, the current practice of acquiring AFT data has major limitations due to the use of traditional microscopes by human operators, which is slow and error-prone. This study uses the local binary pattern feature based on the OpenCV cascade classifier and the faster region-based convolutional neural network model based on the TensorFlow Object Detection API, these two methods offer a means for the rapid identification and measurement of apatite fission tracks, leading to significant improvements in the efficiency and accuracy of track counting. We employed a training dataset consisting of 50 spontaneous fission track images and 65 Durango standard samples as training data for both techniques. Subsequently, the performance of these methods was evaluated using additional 10 spontaneous fission track images and 15 Durango standard samples, which resulted in higher Precision, Recall, and F1-Score values. Through these illustrative examples, we have effectively demonstrated the higher accuracy of these newly developed methods in identifying apatite fission tracks. This suggests their potential for widespread applications in future apatite fission track research.
Integrating AlphaFold and deep learning for atomistic interpretation of cryo-EM maps
Authors: Dai Xin , Wu Longlong , Yoo Shinjae , Liu Qun
Published: November 2023

Interpretation of cryo-electron microscopy (cryo-EM) maps requires building and fitting 3D atomic models of biological molecules. AlphaFold-predicted models generate initial 3D coordinates; however, model inaccuracy and conformational heterogeneity often necessitate labor-intensive manual model building and fitting into cryo-EM maps. In this work, we designed a protein model-building workflow, which combines a deep-learning cryo-EM map feature enhancement tool, CryoFEM (Cryo-EM Feature Enhancement Model) and AlphaFold. A benchmark test using 36 cryo-EM maps shows that CryoFEM achieves state-of-the-art performance in optimizing the Fourier Shell Correlations between the maps and the ground truth models. Furthermore, in a subset of 17 datasets where the initial AlphaFold predictions are less accurate, the workflow significantly improves their model accuracy. Our work demonstrates that the integration of modern deep learning image enhancement and AlphaFold may lead to automated model building and fitting for the atomistic interpretation of cryo-EM maps.
CENTRE: a gradient boosting algorithm for Cell-type-specific ENhancer-Target pREdiction
Authors: Rapakoulia Trisevgeni , Lopez Ruiz De Vargas Sara , Omgba Persia Akbari , Laupert Verena , Ulitsky Igor , Vingron Martin
Published: November 2023

Motivation

Identifying target promoters of active enhancers is a crucial step for realizing gene regulation and deciphering phenotypes and diseases. Up to now, several computational methods were developed to predict enhancer gene interactions, but they require either many epigenomic and transcriptomic experimental assays to generate cell-type (CT)-specific predictions or a single experiment applied to a large cohort of CTs to extract correlations between activities of regulatory elements. Thus, inferring CT-specific enhancer gene interactions in unstudied or poorly annotated CTs becomes a laborious and costly task.

Results

Here, we aim to infer CT-specific enhancer target interactions, using minimal experimental input. We introduce Cell-specific ENhancer Target pREdiction (CENTRE), a machine learning framework that predicts enhancer target interactions in a CT-specific manner, using only gene expression and ChIP-seq data for three histone modifications for the CT of interest. CENTRE exploits the wealth of available datasets and extracts cell-type agnostic statistics to complement the CT-specific information. CENTRE is thoroughly tested across many datasets and CTs and achieves equivalent or superior performance than existing algorithms that require massive experimental data.

Availability and implementation

CENTRE’s open-source code is available at GitHub via https://github.com/slrvv/CENTRE.
Genetic model of UBA5 deficiency highlights the involvement of both peripheral and central nervous systems and identifies widespread mitochondrial abnormalities
Authors: Serrano Rita J , Oorschot Viola , Palipana Dashika , Calcinotto Vanessa , Sonntag Carmen , Ramm Georg , Bryson-Richardson Robert J
Published: November 2023

Variants in UBA5 have been reported to cause neurological disease with impaired motor function, developmental delay, intellectual disability and brain pathology as recurrent clinical manifestations. UBA5 encodes a ubiquitin-activating-like enzyme that activates ufmylation, a post-translational ubiquitin-like modification pathway, which has been implicated in neurodevelopment and neuronal survival. The reason behind the variation in severity and clinical manifestations in affected individuals and the signal transduction pathways regulated by ufmylation that compromise the nervous system remains unknown. Zebrafish have emerged as a powerful model to study neurodegenerative disease due to its amenability for in vivo analysis of muscle and neuronal tissues, high-throughput examination of motor function and rapid embryonic development allowing an examination of disease progression. Using clustered regularly interspaced short palindromic repeats-associated protein 9 genome editing, we developed and characterized zebrafish mutant models to investigate disease pathophysiology. uba5 mutant zebrafish showed a significantly impaired motor function accompanied by delayed growth and reduced lifespan, reproducing key phenotypes observed in affected individuals. Our study demonstrates the suitability of zebrafish to study the pathophysiology of UBA5-related disease and as a powerful tool to identify pathways that could reduce disease progression. Furthermore, uba5 mutants exhibited widespread mitochondrial damage in both the nervous system and the skeletal muscle, suggesting that a perturbation of mitochondrial function may contribute to disease pathology.
Electric vehicle battery capacity degradation and health estimation using machine-learning techniques: a review
Authors: Das Kaushik , Kumar Roushan
Published: November 2023

Lithium-ion batteries have an essential characteristic in consumer electronics applications and electric mobility. However, predicting their lifetime performance is a difficult task due to the impact of operating and environmental conditions. Additionally, state-of-health (SOH) and remaining-useful-life (RUL) predictions have developed into crucial components of the energy management system for lifetime prediction to guarantee the best possible performance. Due to the non-linear behaviour of the health prediction of electric vehicle batteries, the assessment of SOH and RUL has therefore become a core research challenge for both business and academics. This paper introduces a comprehensive analysis of the application of machine learning in the domain of electric vehicle battery management, emphasizing state prediction and ageing prognostics. The objective is to provide comprehensive information about the evaluation, categorization and multiple machine-learning algorithms for predicting the SOH and RUL. Additionally, lithium-ion battery behaviour, the SOH estimation approach, key findings, advantages, challenges and potential of the battery management system for different state estimations are discussed. The study identifies the common challenges encountered in traditional battery management and provides a summary of how machine learning can be employed to address these challenges.
A pragmatic retrofitting approach to enhancing the thermal, energy and economic performance of an educational building: a case study in Malaysia
Authors: Alamin Etaib , Kamaruzaman Natrah , Kamar Haslinda Mohamed
Published: November 2023

Building retrofit procedures play a crucial role in improving the energy performance and economic indicators of a building. In this context, an energy audit is typically recommended, but it is seldom used as a comprehensive approach due to the complexity and associated costs. This article aims to conduct a holistic energy audit approach for a university building in Malaysia, with the objective of diagnosing energy efficiency deficiencies, identifying areas of energy waste and proposing practical retrofit measures accordingly. The approach involved multiple stages, including measurements, surveys and simulation work. Eight energy-saving measures were proposed, targeting improvements in envelope elements, cooling and lighting systems, and operation and control. The Design-Builder software was utilized for energy simulation, assessing the annual energy savings. Economic evaluation indices, such as net present value and simple payback period, were used to assess the economic feasibility of the measures. The results demonstrated significant potential for energy reduction, with each measure achieving annual energy reductions ranging from 2% to 18%, and a cumulative impact of 41% on annual energy consumption when combined. The investment payback period for the energy-saving measures varied from 0.8 to 8.9 years, with a payback period of 3.9 years for the combined energy-saving measures. Furthermore, the net present value was positive, indicating the economic feasibility of investing in the proposed energy-saving measures. These findings provide valuable energy-saving opportunities that can be applied to improve similar buildings on the university campus.
Application of data science in the prediction of solar energy for the Amazon basin: a study case
Authors: Ferreira Marques André Luis , José Teixeira Márcio , Valencia de Almeida Felipe , Pizzigatti Corrêa Pedro Luiz
Published: November 2023

The need for renewable energy sources has challenged most countries to comply with environmental protection actions and to handle climate change. Solar energy figures as a natural option, despite its intermittence. Brazil has a green energy matrix with significant expansion of solar form in recent years. To preserve the Amazon basin, the use of solar energy can help communities and cities improve their living standards without new hydroelectric units or even to burn biomass, avoiding harsh environmental consequences. The novelty of this work is using data science with machine-learning tools to predict the solar incidence (W.h/m²) in four cities in Amazonas state (north-west Brazil), using data from NASA satellites within the period of 2013–22. Decision-tree-based models and vector autoregressive (time-series) models were used with three time aggregations: day, week and month. The predictor model can aid in the economic assessment of solar energy in the Amazon basin and the use of satellite data was encouraged by the lack of data from ground stations. The mean absolute error was selected as the output indicator, with the lowest values obtained close to 0.20, from the adaptive boosting and light gradient boosting algorithms, in the same order of magnitude of similar references.
Cardiac rupture during the course of treatment for acute purulent pericarditis caused by Staphylococcus aureus: a case report
Authors: Katahira Ryutaro , Sano Hiroyuki , Tanimura Kosuke , Okita Yutaka
Published: November 2023

Background

Purulent pericarditis is rare in the modern era of antibiotics. However, it is a rapidly progressive, life-threatening disease with complications, including cardiac tamponade and left ventricular pseudoaneurysm.

Case summary

A 44-year-old female was admitted with a pontine haemorrhage. On the 25th day of admission, she developed a fever along with chest pain and dyspnoea. Transthoracic echocardiography and computed tomography revealed a large pericardial effusion, leading to the diagnosis of cardiac tamponade. Pericardiocentesis was performed, resulting in the drainage of 750 mL of blood-stained fluid. Blood and pericardial fluid cultures were positive for Staphylococcus aureus; therefore, ceftriaxone was administered. On the 49th day, she became febrile again, and computed tomography showed increased pericardial effusion. Transthoracic echocardiography confirmed the large pericardial effusion and revealed a pseudoaneurysm on the inferior of the left ventricular wall, with blood flowing from the pseudoaneurysm into the pericardial space. Urgent surgical intervention was performed to repair a myocardial defect as a left ventricular pseudoaneurysm had ruptured in the pericardium. The patient recovered and was transferred to another hospital for rehabilitation after 108 days of hospitalization.

Discussion

Purulent pericarditis can be a lethal complication; therefore, careful follow-up and strict adherence to therapeutic strategies, including the use of imaging technologies such as echocardiography, are important.
In vivo optical coherence tomography visualisation of coronary artery embolism caused by BioGlue in a middle-aged woman with Marfan syndrome who underwent the Bentall procedure: a case report
Authors: Hamaguchi Yukihiro , Enomoto Soichiro , Kondo Hirokazu , Tamura Toshihiro
Published: November 2023

Background

Coronary artery embolism caused by BioGlue is a rare complication; therefore, its diagnosis and treatment remain undefined.

Case summary

A 47-year-old woman underwent ascending aortic replacement and coronary artery bypass grafting (CABG) for type A acute aortic dissection involving the right coronary artery ostium in 2017. Subsequently, she was diagnosed with Marfan syndrome. Five years later in 2022, she underwent aortic arch replacement, the Bentall procedure, and repeat CABG because of aortic root enlargement and aortic regurgitation progression. Twelve days after surgery, coronary computed tomography angiography (CCTA) revealed left anterior descending (LAD) artery stenosis, whereas pre-operative CCTA was normal. On post-operative day 13, coronary angiography revealed 99% LAD artery stenosis. Intravascular ultrasound (IVUS) showed a non-echoic mass with clear margins, and optical coherence tomography (OCT) demonstrated a crystalloid mass. Both images suggested that the embolus was inorganic matter, suspected as being the surgical adhesive BioGlue. We could not remove the embolus by repeated thrombectomy; therefore, drug-eluting stent implantation was performed. Seven months after surgery, she had no symptoms, and CCTA confirmed stent patency.

Discussion

To our knowledge, this is the first case report to describe BioGlue embolism observed by OCT. We performed an in vitro study using a blood vessel model, and the obtained OCT image was very similar to the in vivo image. Although BioGlue embolism is a rare complication, it should be considered in cases of perioperative myocardial infarction of uncertain aetiology, and coronary imaging is useful for diagnosis.
External validation of a deep learning algorithm for automated echocardiographic strain measurements
Authors: Myhre Peder L , Hung Chung-Lieh , Frost Matthew J , Jiang Zhubo , Ouwerkerk Wouter , Teramoto Kanako , Svedlund Sara , Saraste Antti , Hage Camilla , Tan Ru-San , Beussink-Nelson Lauren , Fermer Maria L , Gan Li-Ming , Hummel Yoran M , Lund Lars H , Shah Sanjiv J , Lam Carolyn S P , Tromp Jasper
Published: November 2023

Aims

Echocardiographic strain imaging reflects myocardial deformation and is a sensitive measure of cardiac function and wall-motion abnormalities. Deep learning (DL) algorithms could automate the interpretation of echocardiographic strain imaging.

Methods and results

We developed and trained an automated DL-based algorithm for left ventricular (LV) strain measurements in an internal dataset. Global longitudinal strain (GLS) was validated externally in (i) a real-world Taiwanese cohort of participants with and without heart failure (HF), (ii) a core-lab measured dataset from the multinational prevalence of microvascular dysfunction-HF and preserved ejection fraction (PROMIS-HFpEF) study, and regional strain in (iii) the HMC-QU-MI study of patients with suspected myocardial infarction. Outcomes included measures of agreement [bias, mean absolute difference (MAD), root-mean-squared-error (RMSE), and Pearson’s correlation (R)] and area under the curve (AUC) to identify HF and regional wall-motion abnormalities. The DL workflow successfully analysed 3741 (89%) studies in the Taiwanese cohort, 176 (96%) in PROMIS-HFpEF, and 158 (98%) in HMC-QU-MI. Automated GLS showed good agreement with manual measurements (mean ± SD): −18.9 ± 4.5% vs. −18.2 ± 4.4%, respectively, bias 0.68 ± 2.52%, MAD 2.0 ± 1.67, RMSE = 2.61, R = 0.84 in the Taiwanese cohort; and −15.4 ± 4.1% vs. −15.9 ± 3.6%, respectively, bias −0.65 ± 2.71%, MAD 2.19 ± 1.71, RMSE = 2.78, R = 0.76 in PROMIS-HFpEF. In the Taiwanese cohort, automated GLS accurately identified patients with HF (AUC = 0.89 for total HF and AUC = 0.98 for HF with reduced ejection fraction). In HMC-QU-MI, automated regional strain identified regional wall-motion abnormalities with an average AUC = 0.80.

Conclusion

DL algorithms can interpret echocardiographic strain images with similar accuracy as conventional measurements. These results highlight the potential of DL algorithms to democratize the use of cardiac strain measurements and reduce time-spent and costs for echo labs globally.
Henry VII and the Tower of London: the context of the ‘confession’ of Sir James Tyrell in 1502
Authors: Thornton Tim
Published: November 2023

The recent development of studies of the itineraries of English monarchs has enhanced understanding of a range of aspects of their kingship, as well as of the road and river transport network. In the case of Henry VII, study of the king’s movements allows for a better understanding of his preferences between his residences in and near his capital, and the reasons for these choices. It also sheds light on a particularly controversial episode, the alleged confession by Sir James Tyrell in the Tower of London in 1502 of responsibility for the murders of the ‘princes in the Tower’.
Reducible Fibers of Polynomial Maps
Authors: König Joachim , Neftin Danny
Published: November 2023

For a degree $n$ polynomial $f\in {\mathbb {Q}}[x]$, the elements in the fiber $f^{-1}(a)\subseteq {\mathbb {C}}$ are of degree $n$ over ${\mathbb {Q}}$ for most values $a\in {\mathbb {Q}}$ by Hilbert’s irreducibility theorem. Determining the set of exceptional $a$’s without this property is a long standing open problem that is closely related to the Davenport–Lewis–Schinzel problem (1959) on reducibility of variable separated polynomials. As opposed to a previous work that mostly concerns indecomposable $f$, we answer both problems for decomposable $f=f_{1}\circ \cdots \circ f_{r}$, as long as the indecomposable factors $f_{i}\in {\mathbb {Q}}[x]$ are of degree $\geq 5$ and are not $x^{n}$ or a Chebyshev polynomial composed with linear polynomials.
When an International Organization Fails to Legitimate: The Decline of the OSCE
Authors: Schuette Leonard , Dijkstra Hylke
Published: November 2023

Various international organizations have recently faced legitimacy crises, but many have demonstrated resilience and relegitimated their rule. The Organization for Security and Cooperation in Europe (OSCE) is an exception. It is clearly an organization in decline and is on the brink of irrelevance. The closure of its Special Monitoring Mission to Ukraine in April 2022 in the wake of the Russian attack is only the latest manifestation of the organization’s long-term legitimacy crisis. Based on the case of the OSCE, this article contributes to the study of legitimacy crises to better understand when such crises can lead to decline. Drawing on twenty interviews with senior officials, the analysis suggests that the OSCE’s failure to (re)legitimate has two interrelated causes: (1) the organization’s institutional weaknesses and impeded leadership have prevented OSCE actors from engaging in effective legitimation practices, and (2) the heterogeneous and largely zero-sum preferences of the OSCE participating states have made them unwilling audiences for (re)legitimation practices. In doing so, the article contributes to our comprehension of the consequences of legitimacy crises.
Mental health and care utilization in survivors of adolescent and young adult cancer
Authors: Baclig Nikita V , Comulada Warren Scott , Ganz Patricia A
Published: November 2023

Background

Adolescent and young adult cancer survivors experience mental health challenges, yet little is known about the evolution of these difficulties. This study explored mental health symptoms and utilization among long-term adolescent and young adult cancer survivors.

Methods

Using 30 432 respondents from the 2019 National Health Interview Survey, this study compared adults with a history of adolescent and young adult cancer (diagnosed when patients were between 15 and 39 years of age) to adults without adolescent and young adult cancer. Mental health symptom severity was measured using the Patient Health Questionnaire depression scale and 7-item Generalized Anxiety Disorder questionnaires. Care utilization constituted psychotherapy and mental health medication use. Inverse propensity score weights were used to balance demographics and combined with survey weights. Descriptive statistics, multivariable generalized linear models, and structural equation modeling with 2-sided tests were used for analysis.

Results

We compared 639 adolescent and young adult survivors with 29 793 controls. Survivors were, on average, 20.5 years (SE = 0.74) past their cancer diagnosis dates. After adjusting for survey and propensity score weights, adolescent and young adult survivors reported more severe depression (incidence rate ratio = 1.42, 95% confidence interval [CI] = 1.09 to 1.84, P < .01) and anxiety (incidence rate ratio = 1.85, 95% CI = 1.55 to 2.21, P < .001). They were more likely to use psychotherapy (odds ratio = 1.91, 95% CI = 1.16 to 3.17, P < .05) and mental health medications (odds ratio = 1.89, 95% CI = 1.15 to 3.11, P < .05). Time since diagnosis was negatively associated with symptoms and utilization. Structural equation modeling demonstrated mediation of utilization effect by symptom severity.

Conclusions

Adolescent and young adult survivors experience worse mental health in late survivorship, despite small improvements over time. We highlight the importance of survivorship care that addresses the long-term mental health needs of these survivors.
Pediatric Outpatient Parenteral Antimicrobial Therapy (OPAT): Experience of Families During Implementation of the Program
Authors: Freire D , Ramos L , Alvarado D
Published: November 2023

Background

Experience with ambulatory intravenous antimicrobial therapy in pediatrics is reported since 1973. This modality avoids hospitalization or helps for early discharge in a patient who, in addition to antibiotics, has no other criteria to remain hospitalized. Even though it reduces the risks and costs that hospitalization entails and allows the patient to remain inserted in his family its use is not widespread in Latin America. We report the experience of families during the implementation of outpatient parenteral antibiotic therapy (OPAT) in pediatric oncologic patients at Solca Guayaquil.

Methods

A pilot program for outpatient parenteral antibiotic therapy (OPAT) was started in March 2022. We included patients with a permanent central venous access (port-a-cath) who were otherwise clinical stable (afebrile > 48h, increasing absolute neutrophil count (>500 in hematologic malignancies, >100 in solid tumors), no transfusion requirement) and completed a social questionnaire described previously (SUAU C, Thelma et al) with a score > 9. Patients had follow up with pediatric infectious diseases once a week during therapy and received a certificate of completion on their last day with a QR code to complete an anonymous online survey regarding their experience with the program.

Results

From March to November 2022 there were 6 patients enrolled. Their mean age 9.6 years, 3 (50%) were male and mean duration of therapy 7.7 days. Response rate was 100%. Ronald Mc. Donald's house hosted 5 (83%) patients while 1 (17%) was staying at home. There was 1 (16.7%) readmission 72h after completion of the program. No disfunction of catheter, secondary infection or deaths were reported. Return to regular activities was possible for 4 (67%) of patients. The experience was considered excellent, good or average in 3(50%), 2(33%) and 1(17%) cases. Quality of care during OPAT was perceived as exceeding expectations, meeting expectations and no opinion in 2(33%), 3(50%), and 1(17%) respectively. Support received by hospital staff was pondered excellent and good in 3 (50%) each. All of the patients agreed that OPAT was better than staying admitted however 1 (17%) declared that would not take this modality again (referred as "stressful").

Conclusion

The newly implemented OPAT program was perceived overall as a positive experience for patients and their families, there were no major complications. The feedback provided helped to improve the logistics of the program.
Antibiotic Therapy for Pneumonia in the Neonatal Intensive Care Unit: Is 5 Days Enough?
Authors: Lewald Zachery S , Prusakov Pavel , Magers Jacqueline K , Kielt Matthew , Sánchez Pablo J
Published: November 2023

Background

Antibiotic therapy provided to infants in the NICU is associated with both short-and-long-term adverse consequences. In 2019, the Neonatal Antimicrobial Stewardship Program (NEO-ASP) at Nationwide Children’s Hospital (NCH), Columbus, OH, recommended a five-day antibiotic course with a time-out for treatment of pneumonia in the seven NCH network NICUs.

Objective

To determine the duration of antibiotic therapy for pneumonia after implementation of a 5-day antibiotic treatment course and describe its safety among infants in the NICU.

Methods

Prospective surveillance of antibiotic therapy provided to infants in 7 NCH NICUs (1, level 4; 5, Level 3; 1, level 2) who were diagnosed with pneumonia by the attending neonatologist based on clinical and radiographic abnormalities from 8/2020 to 4/2022. Pertinent clinical, laboratory, and microbiologic data were obtained from electronic health records. Infants were excluded if they had positive bacterial cultures of blood, urine, or cerebrospinal fluid. The length of therapy (LOT) was defined as any calendar day that the infant received ≥1 antibiotic while definitive treatment course was the duration of appropriate antibiotic therapy. Safety was defined a priori by re-initiation of antibiotic therapy within 14 days after discontinuation of the initial treatment and/or mortality (overall/sepsis-related).

Results

From 8/2020 to 4/2022, NICU infants (66% male) were diagnosed with 136 episodes of pneumonia at a median (IQR) age of 2 (1-20) days. 61 (45%) of episodes were late onset at ≥3 days of age. All infants had sterile cultures of blood, urine, and cerebrospinal fluid, if performed. Their median (IQR) birth weight and gestational age were 2780 (1090-3450) g and 36 (28-38) wk, respectively. At the time of pneumonia diagnosis, 44% of infants were receiving CPAP while 55% were on mechanical ventilation (11% high frequency oscillatory ventilation). The median (IQR) LOT was 6 (5-6) days and length of definitive treatment was 5 (5-5) days. Overall mortality was 5% (7/128) with 2% of deaths being sepsis-related (n=3; Table 1). The majority of pneumonia episodes (88%; n=120) were treated with 5 days of definitive antibiotic therapy and 22 (16%) episodes had antibiotics restarted within 14 days (Table 1). There was no significant difference in the composite safety outcome between ≤5 vs. 6-20 days of definitive antibiotic therapy (p=0.29).

Table 1. Safety assessments of definitive antibiotic treatment for 136 pneumonia episodes in 128 infants

Table 2. Underlying conditions, antibiotic duration, and cause of death among 7 infants in the NICU

Conclusion(s)

There was excellent adherence (88%) to the NEO-ASP recommendation of a 5-day definitive treatment course for pneumonia and in addition, the intervention seemed safe.
Determination of Seasonal Influenza Intrahost Viral Diversity as a Function of Host Immune Status
Authors: Leonard Ashley Sobel , Mendoza Lydia , Odom John Audrey R , Hensley Scott E
Published: November 2023

Background

Seasonal influenza viruses evolve predominately via antigenic drift, the slow accumulations of genetic mutations leading to changes in key antigenic sites. Due to the accumulation of antigenic changes, influenza vaccines must be updated each year and, even then, can suffer from limited efficacy due to strain mismatch. Despite high levels of genetic diversity seen across the population, most studies show that intrahost viral diversity in humans following natural infection is limited. It is unclear where the population-level diversity in influenza virus originates. Previous studies have shown that host characteristics, such as pre-existing immunity, may affect the intrahost viral evolution during a single infection. As very young children have limited immunologic memory, we hypothesize these infections may serve as a source of viral diversity.

Methods

In this study, we characterize influenza genetic diversity in the pediatric population as a function of age and vaccine status. We use next generation sequencing to assess intrahost influenza virus diversity in 200 clinical isolates generated during the 2017-2018 flu season from children between 6 months and 18 years of age. We quantify intrahost diversity as the number and frequency of mutations of influenza virus present within each clinical isolate. The clinical samples were obtained from left over influenza-positive nasopharyngeal swabs from the Children's Hospital of Philadelphia Infectious Disease Diagnostics Laboratory. We stratify these isolates by host subject age and vaccine status (with respect to that season's influenza vaccine).

Results

The isolates sequenced thus far fall within the 6B.1A, 6B.1A.1, and 6B.1A.5b clades for H1N1 and 3C.2a.1, 3C.2a2, and 3C.3a1 clades for H3N2, as expected. Thus far, there does not appear to be a significant relationship between the number of variants detected and either the age or vaccine status of the subject for either H3N2 or H1N1, though this may change as additional isolates are sequenced.

Conclusions

Through ongoing studies, we evaluate how host age and vaccination status influences intrahost viral diversity. Using clinical metadata, we investigate the association between viral diversity and the duration of symptoms and disease severity. By identifying a reservoir of influenza diversity, we will be better able to evaluate influenza strains for inclusion in the seasonal vaccine.
Clinical Outcome of Neutropenic Colitis in Neutropenic Colitis in Pediatric Patients with Pediatric Patients with Leukemia in a Third Level Leukemia in a Third Level Hospital in Mexico Hospital in Mexico
Authors: Martinez M E , Alonso A , Martinez M E , Alonso A
Published: November 2023

Objectives

To know the incidence and number of To know the incidence and number of patients with complications of patients with complications of neutropenic colitis. neutropenic colitis.

Study type and design

Descriptive, cross-sectional and retrospective study during the period retrospective study during the period from June 2018 to May 2021. from June 2018 to May 2021.

Material and methods

In a third level hospital where the In a third level hospital where the population is adults and pediatric in population is adults and pediatric in Mexico city, data were obtained of Mexico city, data were obtained of clinical records of pediatric patients clinical records of pediatric patients with hematological cancer aged 1 to 17 with hematological cancer aged 1 to 17 years of both genders with clinical years of both genders with clinical data of neutropenic colitis. data of neutropenic colitis. For the calculation of the sample, the For the calculation of the sample, the Epi Info program was used with the Epi Info program was used with the formula for descriptive studies and 59 formula for descriptive studies and 59 files are required.

Results

Total patients 75, male 56%, patients with ALL 87%, the majority of patients had fever and pain 91%, we found ultrasound positive with colitis en 93%, the majority of patients had treatment with cytarabine and methotrexate 60%, the initial treatment for neutropenic colitis was cefepime/metronidazole in 40%, the average days of treatment was 12 days, complications was found in 31%, and the recovery of patients was 100%.

Conclusion

Patients who are managed with antimetabolites Patients who are managed with antimetabolites have a 10 times greater risk of presenting have a 10 times greater risk of presenting sepsis. sepsis. The main etiological agents continue to be gram The main etiological agents continue to be gram negative microorganisms. negative microorganisms. Despite severe neutropenia, no increase in Despite severe neutropenia, no increase in sepsis was observed. sepsis was observed. It is important to take into account the It is important to take into account the chemotherapy scheme to detect early patients chemotherapy scheme to detect early patients who may be at risk of presenting a complication. who may be at risk of presenting a complication. 13% presented septic shock, however, there 13% presented septic shock, however, there were no reported deaths.