bluuebunny/crossref_metadata_2025_split huggingface dataset:
Segmentation and quantitative analysis of optical coherence tomography (OCT) images of laser burned skin based on deep learning
Authors: Wu Jingyuan , Ma Qiong , Zhou Xun , Wei Yu , Liu Zhibo , Kang Hongxiang 
Published: May 2024

Abstract
Evaluation of skin recovery is an important step in the treatment of burns. However, conventional methods only observe the surface of the skin and cannot quantify the injury volume. Optical coherence tomography (OCT) is a non-invasive, non-contact, real-time technique. Swept source OCT uses near infrared light and analyzes the intensity of light echo at different depths to generate images from optical interference signals. To quantify the dynamic recovery of skin burns over time, laser induced skin burns in mice were evaluated using deep learning of Swept source OCT images. A laser-induced mouse skin thermal injury model was established in thirty Kunming mice, and OCT images of normal and burned areas of mouse skin were acquired at day 0, day 1, day 3, day 7, and day 14 after laser irradiation. This resulted in 7000 normal and 1400 burn B-scan images which were divided into training, validation, and test sets at 8:1.5:0.5 ratio for the normal data and 8:1:1 for the burn data. Normal images were manually annotated, and the deep learning U-Net model (verified with PSPNe and HRNet models) was used to segment the skin into three layers: the dermal epidermal layer, subcutaneous fat layer, and muscle layer. For the burn images, the models were trained to segment just the damaged area. Three-dimensional reconstruction technology was then used to reconstruct the damaged tissue and calculate the damaged tissue volume. The average IoU value and f-score of the normal tissue layer U-Net segmentation model were 0.876 and 0.934 respectively. The IoU value of the burn area segmentation model reached 0.907 and f-score value reached 0.951. Compared with manual labeling, the U-Net model was faster with higher accuracy for skin stratification. OCT and U-Net segmentation can provide rapid and accurate analysis of tissue changes and clinical guidance in the treatment of burns.
Effect of toroidal mode coupling on explosive dynamics of m/n = 3/1 double tearing mode
Authors: LU 路 Xingqiang 兴强 , GAO 高 Ge 歌 , MA 马 Zhiwei 志为 , GUO 郭 Wei 玮 , LI 李 Xin 鑫 
Published: May 2024

Abstract
The CLT code was used to quantitatively study the impact of toroidal mode coupling on the explosive dynamics of the m/n = 3/1 double tearing mode. The focus of this study was on explosive reconnection processes, in which the energy bursts and the main mode no longer dominates when the separation between two rational surfaces is relatively large in the medium range. The development of higher m and n modes is facilitated by a relatively large separation between two rational surfaces, a small q
min (the minimum value of the safety factor), or low resistivity. The relationships between the higher m and n mode development, explosive reconnection rate, and position exchange of 3/1 islands are summarized for the first time. Separation plays a more important role than q
min in enhancing the development of higher m and n modes. At a relatively large separation, the good development of higher m and n modes greatly reduces the reconnection rate and suppresses the development of the main mode, resulting in the main mode not being able to develop sufficiently large to generate the position changes of 3/1 islands.
Roadmap on methods and software for electronic structure based simulations in chemistry and materials
Authors: Blum Volker , Asahi Ryoji , Autschbach Jochen , Bannwarth Christoph , Bihlmayer Gustav , Blügel Stefan , Burns Lori A , Crawford T Daniel , Dawson William , de Jong Wibe Albert , Draxl Claudia , Filippi Claudia , Genovese Luigi , Giannozzi Paolo , Govind Niranjan , Hammes-Schiffer Sharon , Hammond Jeff R , Hourahine Benjamin , Jain Anubhav , Kanai Yosuke , Kent Paul R C , Larsen Ask Hjorth , Lehtola Susi , Li Xiaosong , Lindh Roland , Maeda Satoshi , Makri Nancy , Moussa Jonathan , Nakajima Takahito , Nash Jessica A , Oliveira Micael J T , Patel Pansy D , Pizzi Giovanni , Pourtois Geoffrey , Pritchard Benjamin P , Rabani Eran , Reiher Markus , Reining Lucia , Ren Xinguo , Rossi Mariana , Schlegel H Bernhard , Seriani Nicola , Slipchenko Lyudmila V , Thom Alexander , Valeev Edward F , Van Troeye Benoit , Visscher Lucas , Vlček Vojtěch , Werner Hans-Joachim , Williams-Young David B , Windus Theresa L. 
Published: May 2024

Abstract
This Roadmap article provides a succinct, comprehensive overview of the state of electronic structure (ES) methods and software for molecular and materials simulations. Seventeen distinct sections collect insights by 51 leading scientists in the field. Each contribution addresses the status of a particular area, as well as current challenges and anticipated future advances, with a particular eye towards software related aspects and providing key references for further reading. Foundational sections cover density functional theory and its implementation in real-world simulation frameworks, Green’s function based many-body perturbation theory, wave-function based and stochastic ES approaches, relativistic effects and semiempirical ES theory approaches. Subsequent sections cover nuclear quantum effects, real-time propagation of the ES, challenges for computational spectroscopy simulations, and exploration of complex potential energy surfaces. The final sections summarize practical aspects, including computational workflows for complex simulation tasks, the impact of current and future high-performance computing architectures, software engineering practices, education and training to maintain and broaden the community, as well as the status of and needs for ES based modeling from the vantage point of industry environments. Overall, the field of ES software and method development continues to unlock immense opportunities for future scientific discovery, based on the growing ability of computations to reveal complex phenomena, processes and properties that are determined by the make-up of matter at the atomic scale, with high precision.
Femtosecond laser-induced nanoparticle implantation into flexible substrate for sensitive and reusable microfluidics SERS detection
Authors: Hu Yongxiang , Zhou Yu , Luo Guohu , Li Dege , Qu Minni 
Published: May 2024

Abstract
Surface-enhanced Raman spectroscopy (SERS) microfluidic system, which enables rapid detection of chemical and biological analytes, offers an effective platform to monitor various food contaminants and disease diagnoses. The efficacy of SERS microfluidic systems is greatly dependent on the sensitivity and reusability of SERS detection substrates to ensure repeated use for prolonged periods. This study proposed a novel process of femtosecond laser nanoparticle array (NPA) implantation to achieve homogeneous forward transfer of gold NPA on a flexible polymer film and accurately integrated it within microfluidic chips for SERS detection. The implanted Au-NPA strips show a remarkable electromagnetic field enhancement with the factor of 9 × 108 during SERS detection of malachite green (MG) solution, achieving a detection limit lower than 10 ppt, far better than most laser-prepared SERS substrates. Furthermore, Au-NPA strips show excellent reusability after several physical and chemical cleaning, because of the robust embedment of laser-implanted NPA in flexible substrates. To demonstrate the performance of Au-NPA, a SERS microfluidic system is built to monitor the online oxidation reaction between MG/NaClO reactants, which helps infer the reaction path. The proposed method of nanoparticle implantation is more effective than the direct laser structuring technique. It provides better performance for SERS detection, robustness of detection, and substrate flexibility and has a wider range of applications for microfluidic systems without any negative impact.
Sustainable engineering of human hair fiber-composite in engineering application
Authors: S Dinesh , M Narasimharajan , R Ramadoss , C Elanchezhian 
Published: May 2024

Abstract
The hybrid composite material, resulting from the fusion of human hair and Kevlar 29 synthetic fiber in a 10:1 ratio, reinforced with epoxy resin using the hand layup technique, underwent meticulous mechanical and thermal testing. The investigation encompassed tensile, flexural, and impact property evaluations, elucidating a notable enhancement in mechanical strength with a diminishing synthetic fiber content. Concomitantly, the composite exhibited commendable heat resistance in heat deflection tests, making it potentially suitable for applications in elevated temperature environments. Water absorption properties were scrutinized, revealing a correlation between reduced synthetic fiber content and diminished water absorption, implying heightened durability in diverse conditions. Microstructure analysis through SEM provided intricate insights into the internal composition. In summation, the composite, featuring a higher proportion of natural fibers and a lower proportion of synthetics, demonstrated superior mechanical attributes, optimal heat resistance, and reduced water absorption, showcasing its applicability in automotive and engineering domains, particularly in the fabrication of vehicle panels and components. The aim of this research represents a significant advancement in composite materials, offering a cost-effective solution that balances mechanical strength and thermal resistance. It suggests the potential for high-performance materials by combining natural and synthetic fibers, promising versatility across various applications.
Neuromorphic place cells
Authors: Chen Zhaoqi , Nasrallah Alia , Etienne-Cummings Ralph 
Published: May 2024

Abstract
A neuromorphic simultaneous localization and mapping (SLAM) system shows potential for more efficient implementation than its traditional counterpart. At the mean time a neuromorphic model of spatial encoding neurons in silicon could provide insights on the functionality and dynamic between each group of cells. Especially when realistic factors including variations and imperfections on the neural movement encoding are presented to challenge the existing hypothetical models for localization. We demonstrate a mixed-mode implementation for spatial encoding neurons including theta cells, egocentric place cells, and the typical allocentric place cells. Together, they form a biologically plausible network that could reproduce the localization functionality of place cells observed in rodents. The system consists of a theta chip with 128 theta cell units and an FPGA implementing 4 networks for egocentric place cells formation that provides the capability for tracking on a 11 by 11 place cell grid. Experimental results validate the robustness of our model when suffering from as much as 18% deviation, induced by parameter variations in analog circuits, from the mathematical model of theta cells. We provide a model for implementing dynamic neuromorphic SLAM systems for dynamic-scale mapping of cluttered environments, even when subject to significant errors in sensory measurements and real-time analog computation. We also suggest a robust approach for the network topology of spatial cells that can mitigate neural non-uniformity and provides a hypothesis for the function of grid cells and the existence of egocentric place cells.
Becoming Professional
Authors: Hebbar N. Nandini 
Published: May 2024

Abstract
This chapter marks a move from models of education to its contents (and discontents). Particular attention has been paid to the way university pedagogy has evolved to mould a new prototype of the private-sector engineer—fluent in English, familiar with global lifestyles, and skilled in management strategies. Training in this model has been adopted widely in engineering colleges under pressure to produce graduates who are ‘employable’, and not merely proficient in the manufacture and use of technology. Though students critique aspects of this training as being incompatible with local social mores (which the college enforces in everyday life), they also learn to focus on the self as the key locus of change. Existing class and caste inequalities are often reproduced and people with existing cultural and social capital are often determined to be more ‘employable’, contributing to a widening gap between those with capital and those without it.
Engineering Aspirations and Lives of Youth
Authors: Hebbar N. Nandini 
Published: May 2024

Abstract
The concluding chapter revisits some of the key understandings arrived through the ethnographic research and shows the realities of higher education in contemporary India. Despite the manifold increase in the number of institutions and the subsequent entry of a large number of first-generation learners, hierarchies and inequalities are largely reproduced. Women are able to enter education only under conditions that enhance their families’ respectability. Groups that do not have explicit markers of cultural and social capital struggle to find employment. Neither upward mobility nor personal success nor transformations sought through entry into engineering colleges are easily achieved. The case studies help to make clear some of the seeming paradoxes such as why despite a drastic increase in women’s access to higher education, their enrolment in the workforce is at an all-time low; why young men and women struggle to find jobs despite having a professional education degree, and why even marriage choices are often made along the grain of caste.
rNMPID: a database for riboNucleoside MonoPhosphates in DNA
Authors: Yang Jingcheng , Sun Mo , Ran Zihan , Yang Taehwan , Kundnani Deepali L , Storici Francesca , Xu Penghao 
Published: May 2024

Abstract

Motivation
Ribonucleoside monophosphates (rNMPs) are the most abundant non-standard nucleotides embedded in genomic DNA. If the presence of rNMP in DNA cannot be controlled, it can lead to genome instability. The actual regulatory functions of rNMPs in DNA remain mainly unknown. Considering the association between rNMP embedment and various diseases and cancer, the phenomenon of rNMP embedment in DNA has become a prominent area of research in recent years.

Results
We introduce the rNMPID database, which is the first database revealing rNMP-embedment characteristics, strand bias, and preferred incorporation patterns in the genomic DNA of samples from bacterial to human cells of different genetic backgrounds. The rNMPID database uses datasets generated by different rNMP-mapping techniques. It provides the researchers with a solid foundation to explore the features of rNMP embedded in the genomic DNA of multiple sources, and their association with cellular functions, and, in future, disease. It also significantly benefits researchers in the fields of genetics and genomics who aim to integrate their studies with the rNMP-embedment data.

Availability and implementation
rNMPID is freely accessible on the web at https://www.rnmpid.org.
Network depth affects inference of gene sets from bacterial transcriptomes using denoising autoencoders
Authors: Kion-Crosby Willow , Barquist Lars 
Published: May 2024

Abstract

Summary
The increasing number of publicly available bacterial gene expression data sets provides an unprecedented resource for the study of gene regulation in diverse conditions, but emphasizes the need for self-supervised methods for the automated generation of new hypotheses. One approach for inferring coordinated regulation from bacterial expression data is through neural networks known as denoising autoencoders (DAEs) which encode large datasets in a reduced bottleneck layer. We have generalized this application of DAEs to include deep networks and explore the effects of network architecture on gene set inference using deep learning. We developed a DAE-based pipeline to extract gene sets from transcriptomic data in Escherichia coli, validate our method by comparing inferred gene sets with known pathways, and have used this pipeline to explore how the choice of network architecture impacts gene set recovery. We find that increasing network depth leads the DAEs to explain gene expression in terms of fewer, more concisely defined gene sets, and that adjusting the width results in a tradeoff between generalizability and biological inference. Finally, leveraging our understanding of the impact of DAE architecture, we apply our pipeline to an independent uropathogenic E.coli dataset to identify genes uniquely induced during human colonization.

Availability and implementation
https://github.com/BarquistLab/DAE_architecture_exploration.
Chainsaw: protein domain segmentation with fully convolutional neural networks
Authors: Wells Jude , Hawkins-Hooker Alex , Bordin Nicola , Sillitoe Ian , Paige Brooks , Orengo Christine 
Published: May 2024

Abstract

Motivation
Protein domains are fundamental units of protein structure and play a pivotal role in understanding folding, function, evolution, and design. The advent of accurate structure prediction techniques has resulted in an influx of new structural data, making the partitioning of these structures into domains essential for inferring evolutionary relationships and functional classification.

Results
This article presents Chainsaw, a supervised learning approach to domain parsing that achieves accuracy that surpasses current state-of-the-art methods. Chainsaw uses a fully convolutional neural network which is trained to predict the probability that each pair of residues is in the same domain. Domain predictions are then derived from these pairwise predictions using an algorithm that searches for the most likely assignment of residues to domains given the set of pairwise co-membership probabilities. Chainsaw matches CATH domain annotations in 78% of protein domains versus 72% for the next closest method. When predicting on AlphaFold models, expert human evaluators were twice as likely to prefer Chainsaw’s predictions versus the next best method.

Availability and implementation
github.com/JudeWells/Chainsaw.
Importance of resection margin after resection of colorectal liver metastases in the era of modern chemotherapy: population-based cohort study
Authors: Östrand Emil , Rystedt Jenny , Engstrand Jennie , Frühling Petter , Hemmingsson Oskar , Sandström Per , Sternby Eilard Malin , Tingstedt Bobby , Buchwald Pamela 
Published: May 2024

Abstract

Background
Resection margin has been associated with overall survival following liver resection for colorectal liver metastasis. The aim of this study was to examine how resection margins of 0.0 mm, 0.1–0.9 mm and ≥1 mm influence overall survival in patients resected for colorectal liver metastasis in a time of modern perioperative chemotherapy and surgery.

Methods
Using data from the national registries Swedish Colorectal Cancer Registry and Swedish National Quality Registry for Liver, Bile Duct and Gallbladder Cancer, patients that had liver resections for colorectal liver metastasis between 2009 and 2013 were included. In patients with a narrow or unknown surgical margin the original pathological reports were re-reviewed. Factors influencing overall survival were analysed using a Cox proportional hazard model.

Results
A total of 754 patients had a known margin status, of which 133 (17.6%) patients had a resection margin &lt;1 mm. The overall survival in patients with a margin of 0 mm or 0.1–0.9 mm was 42 (95% c.i. 31 to 53) and 48 (95% c.i. 35 to 62) months respectively, compared with 75 (95% c.i. 65 to 85) for patients with ≥1 mm margin, P &lt; 0.001. Margins of 0 mm or 0.1–0.9 mm were associated with poor overall survival in the multivariable analysis, HR 1.413 (95% c.i. 1.030 to 1.939), P = 0.032, and 1.399 (95% c.i. 1.025 to 1.910), P = 0.034, respectively.

Conclusions
Despite modern chemotherapy the resection margin is still an important factor for the survival of patients resected for colorectal liver metastasis, and a margin of ≥1 mm is needed to achieve the best possible outcome.
Coronary revascularization and sex differences in cardiovascular mortality after myocardial infarction in 12 high and middle-income European countries
Authors: Cenko Edina , Yoon Jinsung , Bergami Maria , Gale Chris P , Vasiljevic Zorana , Vavlukis Marija , Kedev Sasko , Miličić Davor , Dorobantu Maria , Badimon Lina , Manfrini Olivia , Bugiardini Raffaele 
Published: May 2024

Abstract

Background
Existing data on female sex and excess cardiovascular mortality after myocardial infarction (MI) mostly come from high-income countries (HICs). This study aimed to investigate how sex disparities in treatments and outcomes vary across countries with different income levels.

Methods
Data from the ISACS Archives registry included 22 087 MI patients from 6 HICs and 6 middle-income countries (MICs). MI data were disaggregated by clinical presentation: ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI). The primary outcome was 30-day mortality.

Results
Among STEMI patients, women in MICs had nearly double the 30-day mortality rate of men [12.4% vs. 5.8%; adjusted risk ratio (RR) 2.30, 95% CI 1.98–2.68]. This difference was less pronounced in HICs (6.8% vs. 5.1%; RR 1.36, 95% CI 1.05–1.75). Despite more frequent treatments and timely revascularization in MICs, sex-based mortality differences persisted even after revascularization (8.0% vs. 4.1%; RR 2.05, 95% CI, 1.68–2.50 in MICs and 5.6% vs. 2.6%; RR 2.17, 95% CI, 1.48–3.18) in HICs. Additionally, women from MICs had higher diabetes rates compared to HICs (31.8% vs. 25.1%, standardized difference = 0.15). NSTEMI outcomes were relatively similar between sexes and income groups.

Conclusions
Sex disparities in mortality rates following STEMI are more pronounced in MICs compared to HICs. These disparities cannot be solely attributed to sex-related inequities in revascularization. Variations in mortality may also be influenced by sex differences in socioeconomic factors and baseline comorbidities.
Poor-Quality Daily Social Encounters, Daily Stress, and Subjective Cognitive Decline Among Older Adults
Authors: Jang Heejung , Hill Nikki L , Turner Jennifer R , Bratlee-Whitaker Emily , Jeong Mijin , Mogle Jacqueline 
Published: May 2024

Abstract

Background and Objectives
Although prior research has shown that social relationships and daily stress are strongly associated with cognitive function, few studies have explored the link between the quality of daily social encounters and subjective cognitive decline (SCD). The present study explores whether the quality of older adults’ daily social encounters is associated with SCD through daily stress.

Research Design and Methods
This study used data from 254 adults aged 70 or older (Mage = 76.5 years, SD = 4.4; 67.7% women) who completed the Einstein Aging Study, a 2-week experience sampling study. Multilevel mediation analyses were conducted to account for daily measurements nested within individuals. We tested the indirect effect of the quality of daily social encounters on SCD through daily stress levels.

Results
There was a significant positive association between ambivalent and neutral social encounters and daily stress levels at both the within- and between-person levels. Between-person daily stress was, in turn, associated with greater SCD. Specifically, there was a significant indirect path from ambivalent social encounters to SCD through daily stress.

Discussion and Implications
This study contributes to a more detailed understanding of how the quality of daily social encounters can influence cognition via increased exposure to daily stress. The findings suggest that emotional support may be crucial to preserving perceptions of older adults’ cognitive functioning.