bluuebunny/crossref_metadata_2025_split huggingface dataset:
Study of drug resistance-associated genetic mutations, and phylo-genetic analysis of HCV in the Province of Sindh, Pakistan
Authors: Faiz Sirmast , Irfan Muhammad , Farooq Saba , Khan Ishtiaq Ahmad , Iqbal Hana’a , Wahab Atia-tul , Shakeel Muhammad , Gong Peng , Iftner Thomas , Choudhary M. Iqbal 
Published: July 2023

AbstractCurrent management of HCV infection is based on Direct-Acting Antiviral Drugs (DAAs). However, resistance-associated mutations, especially in the NS3 and NS5B regions are gradually decreasing the efficacy of DAAs. The aim of the current study was to identify such mutations in the NS3, and NS5B genes in DAAs treatment-naïve Pakistani chronic HCV 3a patients. Peripheral blood samples were collected from 233 chronic HCV 3a patients at different tertiary care hospitals in Karachi, Pakistan, between August 2020 to September 2021. PCR-amplified target regions of the NS3/NS5B gene were subjected to Sanger sequencing to identify resistance-associated mutations. Phylogenetic analysis of the identified amino acid sequences was performed using HCV3a sequences of the global population in the virus pathogen resource (VIPR) database. Sequence analysis identified five amino acid mutations, Leu36Pro, Gln41His, Gln80Lys/Arg, Ala156Tyr, and Gln168Arg in the NS3 region, and two mutations Leu159Phe and Cys316Arg in the NS5B region. Phylogenetic analysis revealed a high genetic diversity in the studied isolates. Overall, the prevalence of resistance-associated substitutions was almost similar to other geographic regions worldwide. This data could be helpful in selecting the most effective treatment regimen for HCV chronically infected people in Pakistan.
Fusing the 3’UTR of seed storage protein genes leads to massive recombinant protein accumulation in seeds
Authors: Kanai Masatake , Sugiyama Masaya , Kondo Maki , Yamada Kenji , Nishimura Mikio , Mano Shoji 
Published: July 2023

AbstractThe demand for recombinant proteins is rising dramatically, and effective production systems are currently being developed. The production of recombinant proteins in plants is a promising approach due to its low cost and low risk of contamination of the proteins with endotoxins or infectious agents from the culture serum. Plant seeds primarily accumulate seed storage proteins (SSPs), which are transcribed and translated from a few genes; therefore, the mechanism underlying SSP accumulation has been studied to help devise ways to increase recombinant protein production. We found that the 3’UTR of SSP genes are essential for SSP accumulation and can be used in the production of recombinant proteins inArabidopsis. Fusion of the 3’UTR of SSP genes to the 3’ ends of DNA sequences encoding recombinant proteins enables massive accumulation of recombinant proteins with enzymatic activity inArabidopsisseeds. This method is also applicable to the production of human Interferon Lambda-3 (IFN-lambda 3), a candidate biopharmaceutical compound against hepatitis C infection. Considering the low cost and ease of protein production inArabidopsis, as well as the rapid growth of this plant, our method is useful for large-scale preparation of recombinant proteins for both academic research and biopharmaceutical production.
Association between enhanced carbonyl stress and decreased apparent axonal density in schizophrenia by multimodal white matter imaging
Authors: Son Shuraku , Arai Makoto , Toriumi Kazuya , Andica Christina , Matsuyoshi Daisuke , Kamagata Koji , Aoki Shigeki , Kawashima Takahiko , Kochiyama Takanori , Okada Tomohisa , Fushimi Yasutaka , Nakamoto Yuji , Kobayashi Yuko , Murai Toshiya , Itokawa Masanari , Miyata Jun 
Published: July 2023

AbstractCarbonyl stress is a condition featuring increased rich reactive carbonyl compounds, which facilitate the formation of advanced glycation end products including pentosidine. We previously reported the relationship between enhanced carbonyl stress and disrupted white matter integrity in schizophrenia, although which microstructural component is disrupted remained unclear. In this study, 32 patients with schizophrenia (SCZ) and 45 age- and gender-matched healthy volunteers (HC) were recruited. We obtained blood samples for carbonyl stress markers (plasma pentosidine and serum pyridoxal) and multi-modal magnetic resonance imaging measures of white matter microstructures including apparent axonal density (intra-cellular volume fraction (ICVF)) and orientation (orientation dispersion index (ODI)), and inflammation (free water (FW)). In SCZ, the plasma pentosidine level was significantly increased. Group comparison revealed that mean white matter values were decreased for ICVF, and increased for FW. We found a significant negative correlation between the plasma pentosidine level and mean ICVF values in SCZ, and a significant negative correlation between the serum pyridoxal level and mean ODI value in HC, regardless of age. Our results suggest an association between enhanced carbonyl stress and axonal abnormality in SCZ.
Development and validation of a multiplex electrochemiluminescence immunoassay to evaluate dry eye disease in rat tear fluids
Authors: Compagnone Agnese , Matheeussen An , De Vooght Linda , Cos Paul 
Published: July 2023

AbstractDry eye disease (DED) is a challenge in ophthalmology. Rat models represent valuable tools to study the pathophysiology and to develop novel treatments. A major challenge in DED research is detecting multiple biomarkers in a low tear volume sample. Multiplex immunoassays for DED rat research are missing. We have developed a multiplex electrochemiluminescence immunoassay (ECLIA) to detect three biomarkers for DED: MMP-9, IL-17 and ICAM-1. Tears, used as matrix, were collected from six healthy Wistar rats. Assays were run based on the U-Plex Meso Scale Diagnostics (MSD) platform, by two independent operators according to the EMA guideline on bioanalytical method validation. Linear mixed, regression models were fit to perform the statistical analysis on the range of concentrations for the chosen analytes. During optimization, it has observed that incubation time, temperature and agitation affected the robustness of the protocol. ECLIA optimum conditions include the use of antibodies at 0.5 µg/ml concentration and 1 h incubation at room temperature with shaking. Precision met the acceptance criteria in the chosen range: 1062–133 pg/ml for ICAM-1, 275–34.4 pg/ml for IL-17, 1750–219 pg/ml for MMP-9. Accuracy and linearity were acceptable for a broader range. This is the first report of a validated ECLIA that allows measurements of three relevant DED biomarkers in rat tear fluids.
Research on the air supply adjustment technology of breath-following powered air-purifying respirators
Authors: Teng Guangping , Shi Guoqing , Zhu Jintuo , Zhao Caijun 
Published: July 2023

AbstractIn the hope of reducing the air supply flow of the powered air-purifying respirator (PAPR) and extending the service life of the filter, a breath-following powered air-purifying respirator (BF-PAPR) that can dynamically adjust the air supply flow according to the breathing flow is proposed. The BF-PAPR changes the air supply flow by adjusting the speed of the variable-frequency centrifugal fan according to the air velocity at the half mask outlet (vhm) monitored by the modular wind speed transmitter. In the study, the air supply flow adjustment model of the BF-PAPR is developed. It is found that the filtration resistance barely influences vhm. In addition, under the same mean inhalation flow, the minimum outlet air velocity increases first and then decreases with the increase of the duty cycle variation coefficient (λ), while the maximum outlet air velocity decreases first and then increases. Moreover, the minimum air supply flow of the BF-PAPR is achieved when the standard value of the air velocity is 13.4 m/s and the value of λ is 1. The BF-PAPR can reduce the air supply flow by 6.5%-8.6% and the energy consumption by approximately 20% compared with the PAPR, which is beneficial for reducing the usage cost and extending the continuous working time.
A frequent ice-free Arctic is likely to occur before the mid-21st century
Authors: Shen Zili , Zhou Wen , Li Jinxiao , Chan Johnny C. L. 
Published: July 2023

AbstractAlthough the trend of sea-ice extent under global warming has been studied extensively in recent years, most climate models have failed to capture the recent rapid change in the Arctic environment, which has brought into question the reliability of climate model projections of sea ice and suggested a potential shift in Arctic climate dynamics. Here, based on the results of a time-variant emergent constraint method with a weighting scheme, we show that an ice-free Arctic might occur earlier (by at least 5 ~ 10 years) than previously estimated. In other words, Arctic ice will likely disappear before the 2050 s. The observationally constrained date for an ice-free Arctic in September under fossil-fuel-based development (i.e., Shared Socioeconomic Pathway (SSP) 5–8.5) scenarios yields a central estimate of 2050–2054 with a 66% confidence range (equivalent to the IPCC’s ‘likely’ range) of 2037–2066, while an ice-free Arctic will likely occur for another 20 years and 11 years under ambitious mitigation scenarios (i.e., SSP2-4.5) and SSP3-7.0. An ice-free Arctic is unlikely to occur under the sustainable development scenario (i.e., SSP1-2.6). Looking forward, this time-variant emergent constraint may also help detect tipping points in the climate system. Our findings provide useful information to help policy makers cope with climate change.
Optimization of a triazine-based acceptor (CN-T2T) as the electron transport layer for highly efficient near-infrared perovskite light-emitting diodes
Authors: Kurniawan Ade , Lee Chih-Chien , Iskandar Johan , Liu Chih-Yi , Pal Bhola Nath , Cheng Hsin-Ming , Liu Shun-Wei , Biring Sajal 
Published: July 2023

High EQE of 18.01% and radiance of 366.76 W sr−1 m−2 were achieved by simply controlling the thickness of uncommon exciplex electron acceptor 3′,3′′′,3′′′′′-(1,3,5-triazine-2,4,6-triyl) tris(([1,1′-biphenyl]-3-carbonitrile)) (CN-T2T) as alternate electron transport layer for MAPbI3 perovskite LED.
System value assessment method of energy storage system for multi‐services of power system considering battery degradation
Authors: Yang Yinguo , Wang Yenan , Lu Qiuyu , Ruan Guangchun , Lin Yingming , Wang Xuan , Xie Pingping , Zhong Haiwang 
Published: July 2023

AbstractThe energy storage system (ESS) is a promising technology to address issues caused by the large‐scale deployment of renewable energy. Deploying ESS is a business decision that requires potential revenue assessment. Current value assessment methods focus on the energy storage owner or the electricity utility. The system value of the ESS needs to be fully considered to gain a broad understanding of benefits across the whole power system. Thus, this study proposes a system value assessment method of grid‐integrated ESS to quantify the total system value‐avoided cost based on an improved DC power flow model considering transmission losses. Four typical applications (production cost saving, upgrade deferral, environmental benefit, and transmission loss saving) are chosen to represent the system value of the ESS across the whole power system. In addition, the co‐optimisation model considering the “source‐grid‐storage” coordination operation and battery capacity degradation, is proposed for peak shaving and frequency regulation based on the Chinese power market rule. The proposed method is tested in simulation and experimental studies.
A conditionally constrained compound sub‐gradient method for distributed energy coordination
Authors: Jie Xu , Sun Sizhou 
Published: July 2023

AbstractA conditionally constrained compound (CCC) sub‐gradient method typically involves solutions by multi‐agents, which have important applications in distributed energy resources (DER). The proposed DER system structure model uses operating state switching to analyze the characteristics of the model transformation. The work proposes that energy storage (ES) transformation is the main factor that influences the transformation in the DER model. For solving this optimization problem, this study uses a compound sub‐gradient method that is distributed among the agents. Compared with the conventional analysis methods, a system configuration and equivalent model of distributed power is obtained using the sub‐gradient method, and the power distribution characteristics of the DER in the off‐grid state are further given. Using a time‐based representation, this method can be applied to complex distributed new energy access applications and uses an iterative compound step ladder algorithm with a specific step size for switching. The compound distributed gradient algorithm further determines the selected range of algorithm parameters. Finally, the results of the convergence rate explicitly characterize the effectiveness of the method.
Urban traffic congestion alleviation system based on millimeter wave radar and improved probabilistic neural network
Authors: Yang Bo , Zhang Hua , Du Mengxin , Wang Anna , Xiong Kai 
Published: July 2023

AbstractThe millimeter‐wave radar sensor is widely used for urban traffic surveillance because of its weather resistance and high detection accuracy. Methods such as fuzzy theory, pattern recognition, and artificial neural networks have been integrated into the research of traffic state discrimination. However, research on systematically describing the fusion of sensors and traffic state discrimination algorithms to alleviate urban road congestion is still lacking, especially based on millimeter‐wave radar. Thus, the authors propose an urban traffic congestion alleviation system framework. First, the design and deployment of the millimeter‐wave radar system, including waveforms, signal processing flow, and target tracking, are demonstrated to achieve vehicle information acquisition and output. Then, the appropriate traffic parameters are obtained by analysing traffic state influencing factors and the radar data characteristics. Finally, a traffic conditions identification algorithm combining spectral clustering and neural network algorithm is presented to realise road congestion level classification. The system is applied to real urban intersections rather than simulation or approximate real simulation. According to the current road congestion level, regulate the traffic light state to achieve road vehicle driving command. Experiments show that the proposed system can effectively reduce road congestion by 20% compared to the current fixed traffic light system.
Aktuelle Trends in der Beugesehnenchirurgie – Ergebnisse
einer nationalen Online-Umfrage
Authors: Haas-Lützenberger Elisabeth Maria , Strolla Johannes , Giunta Riccardo , Volkmer Elias 
Published: July 2023

ZusammenfassungInnerhalb der letzten 50 Jahre hat sich eine Trendwende in der
Beugesehnenchirurgie vollzogen. Nach Einführung der 2-Strang-Kernnaht
wurde in den 90igern die 4-Strang-Technik, später sogar die
6-Strang-Technik propagiert. Die vorliegende Studie wertet eine Online Umfrage
unter den DGH Mitgliedern zur eingesetzten Nahttechnik von
Beugesehnenverletzungen Zone 2 aus. Ziel war es, ein realistisches Bild der
aktuellen nationalen Versorgungsrealität in Deutschland zu
ermitteln.
Material und Methoden Mittels Online-Umfrage, welche per Email-Link an
alle DGH Mitglieder ausgesandt wurde, wurden 7 Fragen zur aktuell
durchgeführten Nahttechnik bei Beugesehnenverletzungen der Hand in Zone
2 gestellt. Es haben insgesamt 155 DGH Mitglieder aus Deutschland an der Umfrage
teilgenommen.
Ergebnisse 155 Bögen wurden vollständig beantwortet und in
die Auswertung eingeschlossen. Die Teilnehmer zeigten einen ausgeglichenen
Anteil an Unfallchirurgen, Plastischen Chirurgen und ausschließlich
handchirurgisch tätigen Chirurgen. Immerhin 53% der Teilnehmer
favorisieren eine 4-Strang-Naht. 21% führen noch immer bevorzugt
eine 2-Strang-Naht, 10% eine 6-Strang-Naht durch. Die Technik nach
Kirchmayr-Kessler oder Modifikation davon wird von 52% der Teilnehmer
angewandt, 6% setzen die Technik nach M-Tang, und 10% eine
„andere Technik“ ein. 98% der Teilnehmer bejahten die
Frage nach einer zusätzlichen zirkulären epitendinösen
Adaptationsnaht. In Bezug auf das Nahtmaterial variierten die Meinungen
stärker: 68% verwenden einen resorbierbaren, monofilen Faden wie
z. B. PDS. Knapp ein Viertel (23%) nutzen einen nicht
resorbierbaren monofilen Faden wie z. B. Prolene. Die Frage, ob eine
Anpassung der Nahttechnik aufgrund neuer Studien innerhalb der letzten 7 Jahre
stattgefunden habe, bejahten 40%.
Schlußfolgerung Die Beugesehnenchirurgie hat sich durch intensive
Forschungsentwicklungen in den letzten Jahren erheblich verändert.
Während lange Jahre die 2-Strang-Naht das Maß aller Dinge
darstellte, konnten wir zeigen, dass viele handchirurgisch tätige
Kollegen in Deutschland ihre Nahttechnik zur Versorgung von
Beugesehnenverletzungen in Zone 2 modernisiert haben. Aktuell wird eine
4-Strang-Naht mit monofilem, resorbierbaren Nahtmaterial zuzüglich einer
zirkulären Adaptationsnaht von der Mehrheit der befragten DGH Mitglieder
bevorzugt. Die momentan auf den Jahrestagungen und Kongressen viel diskutierte
Naht nach M-Tang wird lediglich von 6% der Befragten eingesetzt. Unsere
Ergebnisse legen somit nahe, dass der wissenschaftliche Diskurs einen
wesentlichen Einfluss auf die Wahl der chirurgischen Technik und das
Nahtmaterial hat, wenngleich die Versorgungsrealität der aktuellen
theoretischen Diskussion erst mit einigen Jahren Verspätung folgt.
Optical waveguide power splitter with adjustable splitting ratio using non-Hermitian shortcuts to adiabaticity
Authors: Feng Yu-xin , Wu Jin-lei , Ren Bin , Han Jin-xuan , Tang Shuai , Song Jie , Jiang Yong-yuan 
Published: July 2023

Abstract
Versatile optical devices with smaller space footprint are crucial for integrated optics. In this work, we design a dual-waveguide power splitter with adjustable splitting ratio depending on the input ports. The modulated gain and loss are imposed on two waveguides to nullify the non-adiabatic coupling using non-Hermitian shortcut to adiabaticity technique. Simulation results reveal that the length of the power splitter can be shortened to about one ninth compared to the one along the adiabatic path, meanwhile, the proposed splitter has good robustness against possible fabrication errors.
Two-dimensional derivatives obtained by oxidation of MXene for simulating the oxidative properties of natural enzymes
Authors: Peng Qiang , Jiang Nian , Qian Ling , Yue Wanqing 
Published: July 2023

Abstract
Oxidation-derived nanoparticles (CDs/TiO
x
) of Ti3C2 were prepared for the first time by simple oxidation of Ti3C2. CDs/TiO
x
was a two-dimensional hybrid of amorphous carbon and titanium oxides with a lateral dimension of about 50 nm. H2O2 was used as the oxygenator and the reduction product was H2O, which was environmentally friendly and inexpensive. Carboxyl, carbonyl, and hydroxyl groups are formed naturally during the oxidation derivative process. The decreased size increases the specific surface area and provides the possibility for the abundant presence of functional groups. The oxidation process converts MXene from reducing to oxidizing and achieves the ability of the derivatives to mimic peroxidase. Compared with natural horseradish peroxidase, the Michaelis constant for H2O2 was ten-fold lower. A cascade catalytic reaction system of glucose oxidase with CDs/TiO
x
was constructed, and the generated H2O2 could be further used to catalyze the oxidation of nicotinamide adenine dinucleotide (NADH) to NAD+. With the assistance of NAD+-dependent dehydrogenase, NADH could be restored to 95% of the initial level. This assay system can detect glucose levels scientifically and accurately in the range of 0.02–10 mM and remains viable after 20 cycles.
iGRLDTI: an improved graph representation learning method for predicting drug–target interactions over heterogeneous biological information network
Authors: Zhao Bo-Wei , Su Xiao-Rui , Hu Peng-Wei , Huang Yu-An , You Zhu-Hong , Hu Lun 
Published: July 2023

Abstract

Motivation
The task of predicting drug–target interactions (DTIs) plays a significant role in facilitating the development of novel drug discovery. Compared with laboratory-based approaches, computational methods proposed for DTI prediction are preferred due to their high-efficiency and low-cost advantages. Recently, much attention has been attracted to apply different graph neural network (GNN) models to discover underlying DTIs from heterogeneous biological information network (HBIN). Although GNN-based prediction methods achieve better performance, they are prone to encounter the over-smoothing simulation when learning the latent representations of drugs and targets with their rich neighborhood information in HBIN, and thereby reduce the discriminative ability in DTI prediction.

Results
In this work, an improved graph representation learning method, namely iGRLDTI, is proposed to address the above issue by better capturing more discriminative representations of drugs and targets in a latent feature space. Specifically, iGRLDTI first constructs an HBIN by integrating the biological knowledge of drugs and targets with their interactions. After that, it adopts a node-dependent local smoothing strategy to adaptively decide the propagation depth of each biomolecule in HBIN, thus significantly alleviating over-smoothing by enhancing the discriminative ability of feature representations of drugs and targets. Finally, a Gradient Boosting Decision Tree classifier is used by iGRLDTI to predict novel DTIs. Experimental results demonstrate that iGRLDTI yields better performance that several state-of-the-art computational methods on the benchmark dataset. Besides, our case study indicates that iGRLDTI can successfully identify novel DTIs with more distinguishable features of drugs and targets.

Availability and implementation
Python codes and dataset are available at https://github.com/stevejobws/iGRLDTI/.
Single-cell Hi-C data enhancement with deep residual and generative adversarial networks
Authors: Wang Yanli , Guo Zhiye , Cheng Jianlin 
Published: July 2023

Abstract

Motivation
The spatial genome organization of a eukaryotic cell is important for its function. The development of single-cell technologies for probing the 3D genome conformation, especially single-cell chromosome conformation capture techniques, has enabled us to understand genome function better than before. However, due to extreme sparsity and high noise associated with single-cell Hi-C data, it is still difficult to study genome structure and function using the HiC-data of one single cell.

Results
In this work, we developed a deep learning method ScHiCEDRN based on deep residual networks and generative adversarial networks for the imputation and enhancement of Hi-C data of a single cell. In terms of both image evaluation and Hi-C reproducibility metrics, ScHiCEDRN outperforms the four deep learning methods (DeepHiC, HiCPlus, HiCSR, and Loopenhance) on enhancing the raw single-cell Hi-C data of human and Drosophila. The experiments also show that it can generate single-cell Hi-C data more suitable for identifying topologically associating domain boundaries and reconstructing 3D chromosome structures than the existing methods. Moreover, ScHiCEDRN’s performance generalizes well across different single cells and cell types, and it can be applied to improving population Hi-C data.

Availability and implementation
The source code of ScHiCEDRN is available at the GitHub repository: https://github.com/BioinfoMachineLearning/ScHiCEDRN.