bluuebunny/crossref_metadata_2025_split huggingface dataset:
Current-induced zero-field domain wall depinning in cylindrical nanowires
Authors: Moreno Julian A. , Kosel Jurgen 
Published: November 2022

AbstractMulti-segmented cylindrical nanowires have properties that make them attractive for high-density, high-speed logic and memory applications. Investigations of the current-induced domain wall motion in cylindrical nanowires have, so far, typically been conducted with a background magnetic field. However, if performed at zero external field, they would be much more viable for their use in prospective electronic devices. Here, we present an all-magneto electrical method to consistently pin domain walls in multi-segmented nanowires and induce their de-pinning using current pulses. The experiments were conducted with compositionally modulated three-segmented nickel/cobalt/nickel and two-segmented cobalt/nickel nanowires of 190 and 150 nm diameter, respectively, where the soft/hard magnetic texture has been fairly studied. We find that for the 3 segmented nanowire, the domain wall can be de-pinned independent of the polarity of the pulse, while for the 2 segmented nanowire the domain wall de-pins only for one polarity. Applying current pulses of 1 × 1012 A/m2, we use a pulse width of 22 ns to estimate a lower boundary for the domain wall speed of 634.54 m/s in cobalt. We study the resistive heating effect from the DC measurement current to find a temperature increase of no more than 2 °C after more than 20 h of tests.
Retinal functional ultrasound imaging (rfUS) for assessing neurovascular alterations: a pilot study on a rat model of dementia
Authors: Morisset Clementine , Dizeux Alexandre , Larrat Benoit , Selingue Erwan , Boutin Herve , Picaud Serge , Sahel Jose-Alain , Ialy-Radio Nathalie , Pezet Sophie , Tanter Mickael , Deffieux Thomas 
Published: November 2022

AbstractFifty million people worldwide are affected by dementia, a heterogeneous neurodegenerative condition encompassing diseases such as Alzheimer’s, vascular dementia, and Parkinson’s. For them, cognitive decline is often the first marker of the pathology after irreversible brain damage has already occurred. Researchers now believe that structural and functional alterations of the brain vasculature could be early precursors of the diseases and are looking at how functional imaging could provide an early diagnosis years before irreversible clinical symptoms. In this preclinical pilot study, we proposed using functional ultrasound (fUS) on the retina to assess neurovascular alterations non-invasively, bypassing the skull limitation. We demonstrated for the first time the use of functional ultrasound in the retina and applied it to characterize the retinal hemodynamic response function in vivo in rats following a visual stimulus. We then demonstrated that retinal fUS could measure robust neurovascular coupling alterations between wild-type rats and TgF344-AD rat models of Alzheimer’s disease. We observed an average relative increase in blood volume of 21% in the WT versus 37% for the TG group (p = 0.019). As a portable, non-invasive and inexpensive technique, rfUS is a promising functional screening tool in clinics for dementia years before symptoms.
MAX: a simple, affordable, and rapid tissue clearing reagent for 3D imaging of wide variety of biological specimens
Authors: Lee Boram , Lee Ju-Hyun , Kim Dai Hyun , Kim Eun Sil , Seo Bo Kyoung , Rhyu Im Joo , Sun Woong 
Published: November 2022

AbstractTransparency of biological specimens is crucial to obtaining detailed 3-dimensional images and understanding the structure and function of biological specimens. This transparency or tissue clearing can be achieved by adjusting the refractive index (RI) with embedding media and removing light barriers such as lipids, inorganic deposits, and pigments. Many currently available protocols consist of multiple steps to achieve sufficient transparency, making the process complex and time-consuming. Thus, in this study, we tailored the recipe for RI adjustment media named MAX based on the recently reported MACS protocol to achieve a single-step procedure, especially for ECM-rich tissues. This was achieved by the improvement of the tissue penetrability of the RI-matching reagent by combining MXDA with sucrose or iodixanol. While this was sufficient for the 3D imaging in many applications, MAX can also be combined with modular processes for de-lipidation, de-coloration, and de-calcification to further maximize the transparency depending on the special features of the tissues. Our approach provides an easy alternative for tissue clearing and 3D imaging.
Open pollution routing problem of logistics distribution in medical union based on differential search algorithm
Authors: Quan Xiaoxiao , Pang Yongsheng , Chen Jiansheng , Chu Xianghua , Shangguan Lina 
Published: November 2022

AbstractMedical care is a guarantee of people's daily life. Improving healthcare contributes to people's well-being. However, healthcare resources are characterized by uneven distribution. Financially well-off areas will have higher quality health care resources. Most of the medical resources are concentrated in public general hospitals, however, primary care institutions can hardly meet the growing needs of people. To solve this problem, Medical Union achieves efficient deployment of resources by integrating various medical institutions in the same area. In the process of logistics integration of the medical union, the scale of logistics distribution expands accordingly. Transportation vehicles have high operating costs and produce greenhouse gases in the process of distribution. The optimization of the driving path of logistics distribution vehicles can reduce the operating cost, fuel consumption and carbon emission. To solve this kind of decentralized and complex vehicle routing problem, this paper proposes a pollution routing problem model considering electrical vehicle usage, customer's soft time window expectation, open path and carbon cost. A modified Differential Search Algorithm with the comprehensive learning strategy and dynamic Cauchy variation strategy is advanced to solve the problem. Results show that the improved algorithm has good solving performance, and verifies the rationality of the proposed model, which will help to reduce carbon emissions and save the logistics and operating costs of medical devices.
An influential node identification method considering multi-attribute decision fusion and dependency
Authors: Chen Chao-Yang , Tan Dingrong , Meng Xiangyi , Gao Jianxi 
Published: November 2022

AbstractIt is essential to study the robustness and centrality of interdependent networks for building reliable interdependent systems. Here, we consider a nonlinear load-capacity cascading failure model on interdependent networks, where the initial load distribution is not random, as usually assumed, but determined by the influence of each node in the interdependent network. The node influence is measured by an automated entropy-weighted multi-attribute algorithm that takes into account both different centrality measures of nodes and the interdependence of node pairs, then averaging for not only the node itself but also its nearest neighbors and next-nearest neighbors. The resilience of interdependent networks under such a more practical and accurate setting is thoroughly investigated for various network parameters, as well as how nodes from different layers are coupled and the corresponding coupling strength. The results thereby can help better monitoring interdependent systems.
Collective motion of active particles exhibiting non-reciprocal orientational interactions
Authors: Knežević Miloš , Welker Till , Stark Holger 
Published: November 2022

AbstractWe present a Brownian dynamics study of a 2D bath of active particles interacting among each other through usual steric interactions and, additionally, via non-reciprocal avoidant orientational interactions. We motivate them by the fact that the two flagella of the alga Chlamydomonas interact sterically with nearby surfaces such that a torque acts on the alga. As expected, in most cases such interactions disrupt the motility-induced particle clustering in active baths. Surprisingly, however, we find that the active particles can self-organize into collectively moving flocks if the range of non-reciprocal interactions is close to that of steric interactions. We observe that the flocking motion can manifest itself through a variety of structural forms, spanning from single dense bands to multiple moderately-dense stripes, which are highly dynamic. The flocking order parameter is found to be only weakly dependent on the underlying flock structure. Together with the variance of the local-density distribution, one can clearly group the flocking motion into the two separate band and dynamic-stripes states.
The solvent chosen for the manufacturing of electrospun polycaprolactone scaffolds influences cell behavior of lung cancer cells
Authors: Polonio-Alcalá Emma , Casanova-Batlle Enric , Puig Teresa , Ciurana Joaquim 
Published: November 2022

AbstractThe development of a trustworthy in vitro lung cancer model is essential to better understand the illness, find novel biomarkers, and establish new treatments. Polycaprolactone (PCL) electrospun nanofibers are a cost-effective and ECM-like approach for 3D cell culture. However, the solvent used to prepare the polymer solution has a significant impact on the fiber morphology and, consequently, on the cell behavior. Hence, the present study evaluated the effect of the solvent employed in the manufacturing on the physical properties of 15%-PCL electrospun scaffolds and consequently, on cell behavior of NCI-H1975 lung adenocarcinoma cells. Five solvents mixtures (acetic acid, acetic acid-formic acid (3:1, v/v), acetone, chloroform-ethanol (7:3, v/v), and chloroform-dichloromethane (7:3, v/v)) were tested. The highest cell viability ($$\overline{x }$$

x
¯

= 33.4%) was found for cells cultured on chloroform-ethanol (7:3) PCL scaffolds. Chloroform-dichloromethane (7:3) PCL scaffolds exhibited a roughness that enhanced the quality of electrospun filament, in terms of cell viability. Our findings highlighted the influence of the solvent on fiber morphology and protein adsorption capacity of nanofilaments. Consequently, these features directly affected cell attachment, morphology, and viability.
Helicobacter pylori infection eradication for nonalcoholic fatty liver disease: a randomized controlled trial
Authors: Yu Ying-ying , Tong Yu-ling , Wu Ling-yan , Yu Xin-yan 
Published: November 2022

AbstractPrevious studies have suggested that Helicobacter pylori (H. pylori) infection is associated with nonalcoholic fatty liver disease (NAFLD). The purpose of the present study was to investigate the effect of H. pylori eradication treatment on NAFLD patients. Two hundred NAFLD patients who tested positive for H. pylori infection were randomized into the H. pylori eradication treatment group or the control group. Metabolic and inflammatory parameters and FibroScan were measured in all subjects at baseline and 1 year after treatment. At 1 year after treatment, the decrease in metabolic indicators, such as fasting blood glucose, glycosylated haemoglobin, homeostasis model assessment of insulin resistance (HOMA-IR), triglycerides, body mass index and controlled attenuation parameter values, were more obvious in the treatment group. Moreover, the inflammatory indicators white blood count and high-sensitivity C-reactive protein (hs-CRP) and the inflammatory factors interleukin 6 (IL-6) and tumour necrosis factor-α (TNF-α) were also significantly decreased. H. pylori eradication can further reduce the metabolic indices of NAFLD and the degree of liver steatosis. H. pylori infection may participate in the occurrence and development of NAFLD through its influence on inflammatory factors. Thus, checking for the presence of H. pylori infection in patients at risk of NAFLD may be beneficial.
AndroMalPack: enhancing the ML-based malware classification by detection and removal of repacked apps for Android systems
Authors: Rafiq Husnain , Aslam Nauman , Aleem Muhammad , Issac Biju , Randhawa Rizwan Hamid 
Published: November 2022

AbstractDue to the widespread usage of Android smartphones in the present era, Android malware has become a grave security concern. The research community relies on publicly available datasets to keep pace with evolving malware. However, a plethora of apps in those datasets are mere clones of previously identified malware. The reason is that instead of creating novel versions, malware authors generally repack existing malicious applications to create malware clones with minimal effort and expense. This paper investigates three benchmark Android malware datasets to quantify repacked malware using package names-based similarity. We consider 5560 apps from the Drebin dataset, 24,533 apps from the AMD and 695,470 apps from the AndroZoo dataset for analysis. Our analysis reveals that 52.3% apps in Drebin, 29.8% apps in the AMD and 42.3% apps in the AndroZoo dataset are repacked malware. Furthermore, we present AndroMalPack, an Android malware detector trained on clones-free datasets and optimized using Nature-inspired algorithms. Although trained on a reduced version of datasets, AndroMalPack classifies novel and repacked malware with a remarkable detection accuracy of up to 98.2% and meagre false-positive rates. Finally, we publish a dataset of cloned apps in Drebin, AMD, and AndrooZoo to foster research in the repacked malware analysis domain.
Design and evaluation of additive manufactured highly efficient inclined-wing type continuous mixer
Authors: Baek Seoung-Ho , Yang Jung-Ho , Ha Cheol-Woo , Shim Patrick Y. , Yong Son , Park Sang-Hu 
Published: November 2022

AbstractWe develop a novel milli-scale mixer (tilted-wings mixing unit, TWM unit) based on the design for additive manufacturing (DfAM). The proposed tilted-wings mixer has basically designed to have three separate wings that split and combine fluids in order to mix together effectively. Its structure is simple for easy fabrication: two major design parameters of angle among three wings and connecting angle between tilted-unit, which are optimized using the computational fluid dynamics (CFD) analysis. From the CFD analysis, we obtain the best-combined mixing module from analyses of various combinations of TWM units for a highly effective mixing ratio. The mixing ratio of three combined units reaches near 100%, which is validated by the experiment and analysis. We believe that the proposed milli-scale mixer can be utilized in diverse chemical continuous mixers and reactors for minimizing of use of chemicals that can pollute the environment.
Enhanced survival prediction using explainable artificial intelligence in heart transplantation
Authors: Lisboa Paulo J. G. , Jayabalan Manoj , Ortega-Martorell Sandra , Olier Ivan , Medved Dennis , Nilsson Johan 
Published: November 2022

AbstractThe most limiting factor in heart transplantation is the lack of donor organs. With enhanced prediction of outcome, it may be possible to increase the life-years from the organs that become available. Applications of machine learning to tabular data, typical of clinical decision support, pose the practical question of interpretation, which has technical and potential ethical implications. In particular, there is an issue of principle about the predictability of complex data and whether this is inherent in the data or strongly dependent on the choice of machine learning model, leading to the so-called accuracy-interpretability trade-off. We model 1-year mortality in heart transplantation data with a self-explaining neural network, which is benchmarked against a deep learning model on the same development data, in an external validation study with two data sets: (1) UNOS transplants in 2017–2018 (n = 4750) for which the self-explaining and deep learning models are comparable in their AUROC 0.628 [0.602,0.654] cf. 0.635 [0.609,0.662] and (2) Scandinavian transplants during 1997–2018 (n = 2293), showing good calibration with AUROCs of 0.626 [0.588,0.665] and 0.634 [0.570, 0.698], respectively, with and without missing data (n = 982). This shows that for tabular data, predictive models can be transparent and capture important nonlinearities, retaining full predictive performance.
Machine learning based risk prediction for Parkinson's disease with nationwide health screening data
Authors: Park You Hyun , Suh Jee Hyun , Kim Yong Wook , Kang Dae Ryong , Shin Jaeyong , Yang Seung Nam , Yoon Seo Yeon 
Published: November 2022

AbstractAlthough many studies have been conducted on machine learning (ML) models for Parkinson’s disease (PD) prediction using neuroimaging and movement analyses, studies with large population-based datasets are limited. We aimed to propose PD prediction models using ML algorithms based on the National Health Insurance Service-Health Screening datasets. We selected individuals who participated in national health-screening programs > 5 times between 2002 and 2015. PD was defined based on the ICD-code (G20), and a matched cohort of individuals without PD was selected using a 1:1 random sampling method. Various ML algorithms were applied for PD prediction, and the performance of the prediction models was compared. Neural networks, gradient boosting machines, and random forest algorithms exhibited the best average prediction accuracy (average area under the receiver operating characteristic curve (AUC): 0.779, 0.766, and 0.731, respectively) among the algorithms validated in this study. The overall model performance metrics were higher in men than in women (AUC: 0.742 and 0.729, respectively). The most important factor for predicting PD occurrence was body mass index, followed by total cholesterol, glucose, hemoglobin, and blood pressure levels. Smoking and alcohol consumption (in men) and socioeconomic status, physical activity, and diabetes mellitus (in women) were highly correlated with the occurrence of PD. The proposed health-screening dataset-based PD prediction model using ML algorithms is readily applicable, produces validated results, and could be a useful option for PD prediction models.
Current global population size, post-whaling trend and historical trajectory of sperm whales
Authors: Whitehead Hal , Shin Megan 
Published: November 2022

AbstractThe sperm whale lives in most deep ice-free waters of the globe. It was targeted during two periods of whaling peaking in the 1840’s and 1960’s. Using a habitat suitability model, we extrapolated estimates of abundance from visual and acoustic surveys to give a global estimate of 736,053 sperm whales (CV = 0.218) in 1993. Estimates of trends in the post-whaling era suggest that: whaling, by affecting the sex ratio and/or the social cohesion of females, reduced recovery rates well after whaling ceased; preferentially-targeted adult males show the best evidence of recovery, presumably due to recruitment from breeding populations; several decades post-whaling, sperm whale populations not facing much human impact are recovering slowly, but populations may be declining in areas with substantial anthropogenic footprint. A theta-logistic population model enhanced to simulate spatial structure and the non-removal impacts of whaling indicated a pre-whaling population of 1,949,698 (CV = 0.178) in 1710 being reduced by whaling, and then then recovering a little to about 844,761 (CV = 0.209) in 2022. There is much uncertainty about these numbers and trends. A larger population estimate than produced by a similar analysis in 2002 is principally due to a better assessment of ascertainment bias.
Metagenomic analysis of diarrheal stools in Kolkata, India, indicates the possibility of subclinical infection of Vibrio cholerae O1
Authors: Takahashi Eizo , Motooka Daisuke , Nakamura Shota , Miyoshi Shin-ichi , Chowdhury Goutam , Mukhopadhyay Asish K. , Dutta Shanta , Morita Daichi , Iida Tetsuya , Okamoto Keinosuke 
Published: November 2022

AbstractWe examined the stools of 23 patients in Kolkata, who were diagnosed as cholera patients becauseVibrio choleraeO1 was detected from their stools by culturing methods, and further explored by metagenomic sequencing analysis. Subsequently, the presence of the gene encoding A subunit of cholera toxin (ctxA) and the cholera toxin (CT) level in these stool samples were examined.ctxAwas examined by both metagenomic sequencing analysis and polymerase chain reaction. In these examinations, two samples did not show positive in any of these tests. The metagenomic analysis showed that the genes forStreptococcus pneumoniaeandSalmonella entericawere present in the stools of these two patients, respectively. Therefore, these two patients were not considered to have diarrhea due toV. choleraeinfection. From these results, we predicted that some Kolkata residents harbor a small number ofV. choleraein their intestines as a form of subclinical infection withV. cholerae. Next, we analyzed the stool samples of 22 diarrhea patients from whichV. choleraewas not isolated. The results showed that 3 of the patients seemed to have subclinical infection ofV. choleraebased on the amount of the genes. These results indicated that subclinical infections withV. choleraeO1 occur in Kolkata.
Joint probability calculation of the lateral velocity distribution in strong field ionization process
Authors: Ivanov I. A. , Kim Kyung Taec 
Published: November 2022

AbstractWe describe an approach to the description of the time-development of the process of strong field ionization of atoms based on the calculation of the joint probability of occurrence of two events, event B being finding atom in the ionized state after the end of the laser pulse, event A being finding a particular value of a given physical observable at a moment of time inside the laser pulse duration. As an example of such an physical observable we consider lateral velocity component of the electron’s velocity. Our approach allows us to study time-evolution of the lateral velocity distribution for the ionized electron during the interval of the laser pulse duration. We present results of such a study for the cases of target atomic systems with short range Yukawa and Coulomb interactions.