bluuebunny/crossref_metadata_2025_split huggingface dataset:
Overcoming the permeability-selectivity challenge in water purification using two-dimensional cobalt-functionalized vermiculite membrane
Authors: Tian Mengtao , Liu Yi , Zhang Shaoze , Yu Can , Ostrikov Kostya , Zhang Zhenghua 
Published: January 2024

AbstractClean water and sanitation are major global challenges highlighted by the UN Sustainable Development Goals. Water treatment using energy-efficient membrane technologies is one of the most promising solutions. Despite decades of research, the membrane permeability-selectivity trade-off remains the major challenge for synthetic membranes. To overcome this challenge, here we develop a two-dimensional cobalt-functionalized vermiculite membrane (Co@VMT), which innovatively combines the properties of membrane filtration and nanoconfinement catalysis. The Co@VMT membrane demonstrates a high water permeance of 122.4 L·m−2·h−1·bar−1, which is two orders of magnitude higher than that of the VMT membrane (1.1 L·m−2·h−1·bar−1). Moreover, the Co@VMT membrane is applied as a nanofluidic advanced oxidation process platform to activate peroxymonosulfate (PMS) for degradation of several organic pollutants (dyes, pharmaceuticals, and phenols) and shows excellent degradation performance (~100%) and stability (for over 107 h) even in real-world water matrices. Importantly, safe and non-toxic effluent water quality is ensured by the Co@VMT membrane/PMS system without brine, which is totally different from the molecular sieving-based VMT membrane with the concentrated pollutants remaining in the brine. This work can serve as a generic design blueprint for the development of diverse nanofluidic catalytic membranes to overcome the persistent membrane permeability-selectivity issue in water purification.
Dynamic coordination engineering of 2D PhenPtCl2 nanosheets for superior hydrogen evolution
Authors: Shao Gonglei , Jing Changfei , Ma Zhinan , Li Yuanyuan , Dang Weiqi , Guo Dong , Wu Manman , Liu Song , Zhang Xu , He Kun , Yuan Yifei , Luo Jun , Dai Sheng , Xu Jie , Zhou Zhen 
Published: January 2024

AbstractExploring the dynamic structural evolution of electrocatalysts during reactions represents a fundamental objective in the realm of electrocatalytic mechanism research. In pursuit of this objective, we synthesized PhenPtCl2 nanosheets, revealing a N2-Pt-Cl2 coordination structure through various characterization techniques. Remarkably, the electrocatalytic performance of these PhenPtCl2 nanosheets for hydrogen evolution reaction (HER) surpasses that of the commercial Pt/C catalyst across the entire pH range. Furthermore, our discovery of the dynamic coordination changes occurring in the N2-Pt-Cl2 active sites during the electrocatalytic process, as clarified through in situ Raman and X-ray photoelectron spectroscopy, is particularly noteworthy. These changes transition from Phen-Pt-Cl2 to Phen-Pt-Cl and ultimately to Phen-Pt. The Phen-Pt intermediate plays a pivotal role in the electrocatalytic HER, dynamically coordinating with Cl- ions in the electrolyte. Additionally, the unsaturated, two-coordinated Pt within Phen-Pt provides additional space and electrons to enhance both H+ adsorption and H2 evolution. This research illuminates the intricate dynamic coordination evolution and structural adaptability of PhenPtCl2 nanosheets, firmly establishing them as a promising candidate for efficient and tunable electrocatalysts.
Deep convolutional neural networks to restore single-shot electron microscopy images
Authors: Lobato I. , Friedrich T. , Van Aert S. 
Published: January 2024

AbstractAdvanced electron microscopy techniques, including scanning electron microscopes (SEM), scanning transmission electron microscopes (STEM), and transmission electron microscopes (TEM), have revolutionized imaging capabilities. However, achieving high-quality experimental images remains a challenge due to various distortions stemming from the instrumentation and external factors. These distortions, introduced at different stages of imaging, hinder the extraction of reliable quantitative insights. In this paper, we will discuss the main sources of distortion in TEM and S(T)EM images, develop models to describe them, and propose a method to correct these distortions using a convolutional neural network. We validate the effectiveness of our method on a range of simulated and experimental images, demonstrating its ability to significantly enhance the signal-to-noise ratio. This improvement leads to a more reliable extraction of quantitative structural information from the images. In summary, our findings offer a robust framework to enhance the quality of electron microscopy images, which in turn supports progress in structural analysis and quantification in materials science and biology.
Breaking the mold with RNA—a “RNAissance” of life science
Authors: Jones Charles H. , Androsavich John R. , So Nina , Jenkins Matthew P. , MacCormack Derek , Prigodich Andrew , Welch Verna , True Jane M. , Dolsten Mikael 
Published: January 2024

AbstractIn the past decade, RNA therapeutics have gone from being a promising concept to one of the most exciting frontiers in healthcare and pharmaceuticals. The field is now entering what many call a renaissance or “RNAissance” which is being fueled by advances in genetic engineering and delivery systems to take on more ambitious development efforts. However, this renaissance is occurring at an unprecedented pace, which will require a different way of thinking if the field is to live up to its full potential. Recognizing this need, this article will provide a forward-looking perspective on the field of RNA medical products and the potential long-term innovations and policy shifts enabled by this revolutionary and game-changing technological platform.
Seawater physics and chemistry along the Med-SHIP transects in the Mediterranean Sea in 2016
Authors: Schroeder Katrin , Kovačević Vedrana , Civitarese Giuseppe , Velaoras Dimitris , Álvarez Marta , Tanhua Toste , Jullion Loïc , Coppola Laurent , Bensi Manuel , Ursella Laura , Santinelli Chiara , Giani Michele , Chiggiato Jacopo , Aly-Eldeen Mohamed , Assimakopoulou Georgia , Bachi Giancarlo , Bogner Boie , Borghini Mireno , Cardin Vanessa , Cornec Marin , Giannakourou Antonia , Giannoudi Louisa , Gogou Alexandra , Golbol Melek , Hazan Or , Karthäuser Clarissa , Kralj Martina , Krasakopoulou Evangelia , Matić Frano , Mihanović Hrvoje , Muslim Stipe , Papadopoulos Vassilis P. , Parinos Constantine , Paulitschke Anne , Pavlidou Alexandra , Pitta Elli , Protopapa Maria , Rahav Eyal , Raveh Ofrat , Renieris Panagiotis , Reyes-Suarez Nydia C. , Rousselaki Eleni , Silverman Jacop , Souvermezoglou Ekaterini , Urbini Lidia , Zeri Christina , Zervoudaki Soultana 
Published: January 2024

AbstractThe Mediterranean Sea has been sampled irregularly by research vessels in the past, mostly by national expeditions in regional waters. To monitor the hydrographic, biogeochemical and circulation changes in the Mediterranean Sea, a systematic repeat oceanographic survey programme called Med-SHIP was recommended by the Mediterranean Science Commission (CIESM) in 2011, as part of the Global Ocean Ship-based Hydrographic Investigations Program (GO-SHIP). Med-SHIP consists of zonal and meridional surveys with different frequencies, where comprehensive physical and biogeochemical properties are measured with the highest international standards. The first zonal survey was done in 2011 and repeated in 2018. In addition, a network of meridional (and other key) hydrographic sections were designed: the first cycle of these sections was completed in 2016, with three cruises funded by the EU project EUROFLEETS2. This paper presents the physical and chemical data of the meridional and key transects in the Western and Eastern Mediterranean Sea collected during those cruises.
Effects of sodium selenite, cysteamine, bacterially synthesized Se-NPs, and cysteamine loaded on Se-NPs on ram sperm cryopreservation
Authors: Salimi Tannaz , Hajarian Hadi , Karamishabankareh Hamed , Soltani Leila 
Published: January 2024

AbstractDuring the cryopreservation of sperm, the production of highly reactive oxygen species (ROS) can reduce their viability and fertility. However, the addition of antioxidants can help reduce the harmful effects of ROS. One such antioxidant is selenium, which is a co-factor of the glutathione peroxidase enzyme that is effective in scavenging ROS. Cysteamine can also take part in the structure of this enzyme. The use of nanoparticles can be less toxic to cells than their salt form. To this end, researchers synthesized Se-NPs using the streptococcus bacteria and loaded cysteamine onto the synthesized Se-NPs. The biosynthesis of Se-NPs and cysteamine loaded on Se-NPs was confirmed by UV–visible spectroscopy, X-ray diffraction (EDX), Fourier transforms infrared (FTIR) spectroscopy, and Field Emission Scanning Electron Microscope (FE-SEM). For cryopreservation, ram semen samples were diluted, and different concentrations (0, 1, 5, 25, and 125 µg/mL) of cysteamine, Se-NPs, cysteamine loaded on Se-NPs, and sodium selenite were added. An extender containing no supplement was considered as control group. After cooling the semen samples, they were frozen and stored in liquid nitrogen for evaluation. The samples were thawed and analyzed for mobility, viability, membrane and DNA integrity, and sperm abnormalities, as well as malondialdehyde level (MDA) and superoxide dismutase (SOD). The data was processed using SPSS, and a significance level of p < 0.05 was considered. The results of this experiment showed that adding 1 μg/mL of cysteamine loaded on Se-NPs to the diluent significantly increased the motility, viability, and membrane integrity and SOD of spermatozoa compared to the other treatment groups and control group, and reduced the abnormality, apoptosis, and MDA level of spermatozoa in comparison with the other treatment groups and control group (p < 0.05). In conclusion, the addition of cysteamine loaded on Se-NPs was found to improve the quality of ram sperm after cryopreservation.
Promising sensors for pharmaceutical pollutant adsorption using Clar’s goblet-based 2D membranes
Authors: Sakr Mahmoud A. S. , Saad Mohamed A. , Abd-Elkader Omar H. , Abdelsalam Hazem , Zhang Qinfang 
Published: January 2024

AbstractThis study focuses on the design of new 2D membranes from connected Clar’s Goblet as a potential sensor for pharmaceutical pollutants, specifically the painkiller drugs aspirin, paracetamol, ibuprofen, and diclofenac. The electronic, optical, and interaction properties are investigated using density functional theory calculations. The Clar’s Goblet membranes (CGMs) that were chosen are semiconductors with an energy gap of around 1.5 eV, according to energy gap calculations and density of states. Molecular electrostatic potential (ESP) analysis shows that CGMs have electrophilic and nucleophilic sites, suggesting their suitability for interacting with pharmaceutical pollutants. The adsorption energies confirm the chemical adsorption of pharmaceutical pollutants with diclofenac showing the strongest adsorption. The UV–Vis absorption spectra of CGMs-drug complexes are analyzed, revealing a redshift compared to the absorption spectrum of CGMs alone, confirming the adsorption of these drugs. Further analysis using hole/electron examinations indicates that the type of excitation is local excitation rather than charge transfer excitation. This study quantitatively characterized hole and electron distribution in excited states using various indices. The analysis revealed local excitation transitions and significant charge transfer between the CGMs molecule and pharmaceutical pollutants. Additionally, non-covalent interaction analysis indicates the presence of van der Waals interactions, highlighting the adsorption behavior of the drugs. These results demonstrate the potential of CGMs as a highly sensitive sensor for pharmaceutical pollutants.
Regional patient transfer patterns matter for the spread of hospital-acquired pathogens
Authors: Xia Hanjue , Horn Johannes , Piotrowska Monika J. , Sakowski Konrad , Karch André , Kretzschmar Mirjam , Mikolajczyk Rafael 
Published: January 2024

AbstractPathogens typically responsible for hospital-acquired infections (HAIs) constitute a major threat to healthcare systems worldwide. They spread via hospital (or hospital-community) networks by readmissions or patient transfers. Therefore, knowledge of these networks is essential to develop and test strategies to mitigate and control the HAI spread. Until now, no methods for comparing healthcare networks across different systems were proposed. Based on healthcare insurance data from four German federal states (Bavaria, Lower Saxony, Saxony and Thuringia), we constructed hospital networks and compared them in a systematic approach regarding population, hospital characteristics, and patient transfer patterns. Direct patient transfers between hospitals had only a limited impact on HAI spread. Whereas, with low colonization clearance rates, readmissions to the same hospitals posed the biggest transmission risk of all inter-hospital transfers. We then generated hospital-community networks, in which patients either stay in communities or in hospitals. We found that network characteristics affect the final prevalence and the time to reach it. However, depending on the characteristics of the pathogen (colonization clearance rate and transmission rate or even the relationship between transmission rate in hospitals and in the community), the studied networks performed differently. The differences were not large, but justify further studies.
High-dimensional single photon based quantum secure direct communication using time and phase mode degrees
Authors: Ahn Byungkyu , Park Jooyoun , Lee Jonghyun , Lee Sangrim 
Published: January 2024

AbstractQuantum secure direct communication (QSDC) can guarantee security using the characteristics of quantum mechanics even when a message is directly transmitted through a quantum channel without using a secret key. However, the transmission rate of the QSDC is limited by the dead time of a single photon detector (SPD) as well as channel loss over the distance. To overcome this limited transmission rate, we propose a high-dimensional single photon-based QSDC protocol that applies two optical degrees of freedom: time and phase state. First, an N-dimensional time and phase state generation method that considers the dead time is proposed to minimize the measurement loss of a transmitted message. Second, among the two types of quantum states, the phase state with relatively low measurement efficiency is used only for eavesdropping detection, and the time state is used for sending messages with differential delay time bin-based encoding techniques. Lastly, we propose an efficient method for measuring N-dimensional time and phase-based quantum states and recovering classical bit information. This study performs security analysis against various attacks, and verifies the transmission rate improvement effect through simulation. The result indicates that our proposal can guarantee higher security and transmission rates compared to the conventional DL04 QSDC.
Brain areas lipidomics in female transgenic mouse model of Alzheimer's disease
Authors: Ferré-González Laura , Balaguer Ángel , Roca Marta , Ftara Artemis , Lloret Ana , Cháfer-Pericás Consuelo 
Published: January 2024

AbstractLipids are the major component of the brain with important structural and functional properties. Lipid disruption could play a relevant role in Alzheimer’s disease (AD). Some brain lipidomic studies showed significant differences compared to controls, but few studies have focused on different brain areas related to AD. Furthermore, AD is more prevalent in females, but there is a lack of studies focusing on this sex. This work aims to perform a lipidomic study in selected brain areas (cerebellum, amygdala, hippocampus, entire cortex) from wild-type (WT, n = 10) and APPswe/PS1dE9 transgenic (TG, n = 10) female mice of 5 months of age, as a model of early AD, to identify alterations in lipid composition. A lipidomic mass spectrometry-based method was optimized and applied to brain tissue. As result, some lipids showed statistically significant differences between mice groups in cerebellum (n = 68), amygdala (n = 49), hippocampus (n = 48), and the cortex (n = 22). In addition, some lipids (n = 15) from the glycerolipid, phospholipid, and sphingolipid families were statistically significant in several brain areas simultaneously between WT and TG. A selection of lipid variables was made to develop a multivariate approach to assess their discriminant potential, showing high diagnostic indexes, especially in cerebellum and amygdala (sensitivity 70–100%, sensibility 80–100%).
Machine learning approaches for estimating interfacial tension between oil/gas and oil/water systems: a performance analysis
Authors: Yousefmarzi Fatemeh , Haratian Ali , Mahdavi Kalatehno Javad , Keihani Kamal Mostafa 
Published: January 2024

AbstractInterfacial tension (IFT) is a key physical property that affects various processes in the oil and gas industry, such as enhanced oil recovery, multiphase flow, and emulsion stability. Accurate prediction of IFT is essential for optimizing these processes and increasing their efficiency. This article compares the performance of six machine learning models, namely Support Vector Regression (SVR), Random Forests (RF), Decision Tree (DT), Gradient Boosting (GB), Catboosting (CB), and XGBoosting (XGB), in predicting IFT between oil/gas and oil/water systems. The models are trained and tested on a dataset that contains various input parameters that influence IFT, such as gas-oil ratio, gas formation volume factor, oil density, etc. The results show that SVR and Catboost models achieve the highest accuracy for oil/gas IFT prediction, with an R-squared value of 0.99, while SVR outperforms Catboost for Oil/Water IFT prediction, with an R-squared value of 0.99. The study demonstrates the potential of machine learning models as a reliable and resilient tool for predicting IFT in the oil and gas industry. The findings of this study can help improve the understanding and optimization of IFT forecasting and facilitate the development of more efficient reservoir management strategies.
Novel nerve-sparing robot-assisted radical prostatectomy with endopelvic fascia preservation and long-term outcomes for a single surgeon
Authors: Maruo Masafumi , Goto Yusuke , Miyazaki Kanetaka , Inoue Atsushi , Kurokawa Koichiro , Enomoto Akiko , Tanaka Satoki , Katsura Sota , Sugawara Sho , Fuse Miki , Chiba Kazuto , Imamura Yusuke , Sakamoto Shinichi , Nagata Maki , Ichikawa Tomohiko 
Published: January 2024

AbstractAlthough novel techniques for avoiding incontinence during robot-assisted radical prostatectomy have been developed, long-term oncological outcomes are unknown. The objective of this study was to determine the long-term oncological outcomes and functional outcomes of novel nerve-sparing robot-assisted radical prostatectomy with endopelvic fascia preservation for a single surgeon. Data from 100 patients who underwent structure-preserving prostatectomies performed by a single surgeon were retrospectively analyzed. The median console time was 123 min. Bilateral nerve-sparing was performed in 43% of patients underwent, and 57% underwent unilateral nerve-sparing surgery. Most patients (96%) reached complete pad-zero urinary continence by one year after surgery. Satisfactory erectile function was achieved in 97% of patients who underwent bilateral nerve-sparing surgery, and 80% of patients who underwent unilateral nerve-sparing surgery. The surgical margin was positive for 25% of patients, and the biochemical recurrence-free rate at 5 years was 77%. The cancer-specific survival rate was 100% during the median follow-up period of 4.5 years. Clavien-Dindo grade III complications occurred in 1% of cases. The outcomes for novel nerve-sparing robot-assisted radical prostatectomy with endopelvic fascia preservation were similar to previously reported oncological outcomes, with satisfactory functional outcomes. This operative method may be useful for patients who are eligible for nerve-sparing surgery.
An elliptical nanoantenna array plasmonic metasurface for efficient solar energy harvesting
Authors: Ashrafi-Peyman Zahra , Jafargholi Amir , Moshfegh Alireza Z. 
Published: January 2024

A novel ultrathin plasmonic metasurface absorber, composed of TiN/GaN/TiN elliptical nanoantenna arrays, is designed to excite localized surface plasmon resonances for near-perfect broadband optical absorption in both air and water environments.
On the explicit Hermitian solutions of the continuous‐time algebraic Riccati matrix equation for controllable systems
Authors: Zhang Liangyin , Chen Michael Z. Q. , Gao Zhiwei , Ma Lifeng 
Published: January 2024

AbstractThis paper proposes explicit solutions for the algebraic Riccati matrix equation. For single‐input systems in controllable canonical form, the explicit Hermitian solutions of the non‐homogeneous Riccati equation are obtained using the entries of the system matrix, the closed‐loop system matrix, and the weighting matrix. The unknown entries of the closed‐loop system matrix are solved by scalar quadratic equations. For a homogeneous Riccati equation with a zero weighting matrix, the explicit solutions are proposed analytically in terms of the system eigenvalues. The advantages of the explicit solutions are threefold: first, if the system is controllable, the solution is directly given and the invariant subspaces of the Hamiltonian matrix are not required; second, if the system is near singularity, the explicit solution has higher numerical precision compared with the solution computed by numerical algorithms; third, for a real system in the controllable canonical form, the non‐negativity can be analysed for the explicit almost stabilizing solution.
Clean, performance‐robust, and performance‐sensitive historical information based adversarial self‐distillation
Authors: Li Shuyi , Hu Hongchao , Huo Shumin , Liang Hao 
Published: January 2024

AbstractAdversarial training suffers from poor effectiveness due to the challenging optimisation of loss with hard labels. To address this issue, adversarial distillation has emerged as a potential solution, encouraging target models to mimic the output of the teachers. However, reliance on pre‐training teachers leads to additional training costs and raises concerns about the reliability of their knowledge. Furthermore, existing methods fail to consider the significant differences in unconfident samples between early and late stages, potentially resulting in robust overfitting. An adversarial defence method named Clean, Performance‐robust, and Performance‐sensitive Historical Information based Adversarial Self‐Distillation (CPr & PsHI‐ASD) is presented. Firstly, an adversarial self‐distillation replacement method based on clean, performance‐robust, and performance‐sensitive historical information is developed to eliminate pre‐training costs and enhance guidance reliability for the target model. Secondly, adversarial self‐distillation algorithms that leverage knowledge distilled from the previous iteration are introduced to facilitate the self‐distillation of adversarial knowledge and mitigate the problem of robust overfitting. Experiments are conducted to evaluate the performance of the proposed method on CIFAR‐10, CIFAR‐100, and Tiny‐ImageNet datasets. The results demonstrate that the CPr&PsHI‐ASD method is more effective than existing adversarial distillation methods in enhancing adversarial robustness and mitigating robust overfitting issues against various adversarial attacks.