bluuebunny/crossref_metadata_2025_split huggingface dataset:
Study on resonance characteristic of continuous co‐phase traction power supply system
Authors: Zhong Fan , Xie Shaofeng 
Published: December 2022

AbstractIn order to solve the power quality problems and eliminate the neutral sections in the railway, the implementation of the continuous co‐phase traction power supply system (CCTPSS) is the development trend of the future. However, various parameters of the compensator in CCTPSS have a huge impact on the resonance, which will possibly change the original resonance characteristic of the railway. Therefore, the parameters design of the compensator has become a difficult problem to be solved. An optimization mode method is proposed in this paper to obtain the influence of the compensator on the resonance characteristic of CCTPSS and the optimal solution of each parameter is obtained with the goal of suppressing resonance and minimizing the cost. Firstly, the impedance analysis and transfer function analysis are used to establish models for CCTPSS. It is found that the equivalent harmonic impedance of the traction substation decreases after adding the compensator, which will inevitably cause the resonance frequency to increase. By comparing the resonance points of the original traction power supply system (TPSS) and CCTPSS, the results show that the parameters of the compensator have a greater influence on the resonance point in CCTPSS. At last, taking the CCTPSS based on LCL‐type reactive power comprehensive compensator (RPCC) as an example to carry out the optimization mode method, the result proves that this method is not only beneficial to the suppression of harmonic resonance, but also helpful for reducing the cost of the RPCC.
A 2D numerical study on collision coefficient of single conductor under direct current electric field
Authors: Chao Zhou , Jianfeng Sun 
Published: December 2022

AbstractIn early spring or winter, supercooled raindrops impact on transmission lines and accumulate ice on its surface frequently, which may induce galloping vibration occurs. Studies on collision coefficient or icing mostly focus on key parameters of wind velocities, raindrop sizes and conductor diameters, whereas few involve electric field. In order to clarify the influence of the direct current electric field on the collision coefficient of transmission lines, a calculation formula of the mass flow rate of raindrops was proposed, and the process of raindrops hitting the conductor surface is numerically calculated by the Euler–Lagrange method. The effects of electric field intensity, conductor diameter, cross‐sectional shape, wind velocity, raindrop diameter and charge density on the collision coefficient of transmission lines are analyzed. The results show that the collision coefficient decreases with the increase of electric intensity. As the electric intensity increases from 0 to 66.7 kV/cm, the collision coefficient decreases about 33%. Because of the repulsion between the conductor and raindrops, more raindrops move away from the conductor surface and collision coefficient decreases. The collision coefficient of the actual conductor under no electric field is slightly smaller than that of the simplified circular cross‐section. Under DC electric field, the collision coefficient of the conductor decreases with wind velocity increase up to 8 m/s. Beyond that threshold, the collision coefficient is almost constant.
A review of modelling techniques of power transformers for digital real‐time simulation
Authors: Islam Md Maidul , Musil Matthias , Shohan Md Jamal Ahmed , Faruque Md Omar , Lauss Georg , Dehkordi Ali , Forsyth Paul , Kotsampopoulos Panos , Strunz Kai , Li Zhihui , Zhang Yi , Liu Peng 
Published: December 2022

AbstractThis task‐force paper documents and summarizes the models of power transformers that have been proposed, used, and validated in the exercise of digital real‐time simulation. Power transformer is one of the most important equipment in power systems, and its modelling for electromagnetic transient simulation has evolved over time, especially in the area of real‐time simulation. The focus of the paper is to document and archive the models that have been well accepted and used for transient analysis in digital real‐time simulation so that readers can use it as a master document for transformer modelling in real‐time simulation studies. It includes both conventional and specialized models of power transformers that have been broadly acknowledged by the power engineering community. The models provided here come with detailed mathematical representation and their implementation techniques. A comparative study is also performed to illustrate the differences in their performances. In the end, an application guideline has been provided to guide the readers to select the appropriate model for their study.
Die Schnellschnittdiagnostik von Basalzellkarzinomen – eine Besonderheit der Karlsruher Hautklinik
Authors: Frenzel Ann-Kathrin , Klemke Claus-Detlev , Kemter Jürgen 
Published: December 2022

ZusammenfassungBei der Operation von Basalzellkarzinomen gibt es an der Karlsruher Hautklinik zwei
unterschiedliche Vorgehensweisen, die sich primär in der Art der mikrografisch kontrollierten
histologischen Aufarbeitung der Tumoren unterscheiden. Die Schnellschnittdiagnostik, die
hautklinikintern durchgeführt wird, kommt bei Basalzellkarzinomen kleiner als 5 cm² zur
Anwendung, ermöglicht ein gewebeschonendes Operieren und erspart Patienten lange Wartezeiten
bis zum Erhalt des histologischen Ergebnisses. Das Verfahren der Schnelleinbettung kommt bei
Basalzellkarzinomen größer als 5 cm² und Tumoren, die zuvor nicht histologisch gesichert
wurden, zur Anwendung. In Zusammenarbeit mit dem Pathologischen Institut werden die
exzidierten Tumoren bis zum Folgetag histologisch aufgearbeitet, sodass am Folgetag eine
gezielte Nachexzision oder ein Wundverschluss erfolgen kann.
Rapid Prototyping and Implementation of Electronic Order Sets for Critically Ill Adults with COVID-19 Admitted to a Children's Hospital
Authors: McKinnon Nicole K. , Silver Shawna , Pong Sandra , Seto Winnie , Chan Phoebe , Gilfoyle Elaine , Jessa Karim , Gray Seth B. 
Published: December 2022

Abstract
Objectives An eight-bed adult coronavirus (COVID-19) critical care (CC) unit was established within our pediatric CC unit (PCCU) when SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) variants increased the CC bed demand. Our objective was to rapidly implement electronic order sets (OSs) to facilitate computerized provider order entry (CPOE) for adult patients admitted within a children's hospital.
Methods OS development began from the assessment of OSs from seven adult CC units. Using a pre-existing PCCU admission template, we created two OSs: adult COVID-19 admission and ongoing care. We tested the prototypes in a multidisciplinary onsite–virtual hybrid tabletop simulation to evaluate usability within established workflows. Participants utilized role-specific profiles within the electronic health record (EHR) training environment which paralleled their computer interface, permitting charting and documentation. EHR analysts were present to gather change requests. Following implementation, we performed twice-daily huddles with end users to identify issues.
Results A total of 13 multidisciplinary bedside providers participated in simulation testing of the prototypes. Two safety issues were addressed before implementation. The electronic OSs were developed, tested, and implemented within 8 days. The postimplementation huddles identified one medication addition, and no deletions were necessary.
Conclusion Caring for adult COVID-19 patients within a freestanding children's hospital presents challenges and has the potential to introduce latent safety threats. Rapid development and implementation of electronic OSs within 8 days to facilitate CPOE and reduce health care provider cognitive burden relied on leveraging functionality within the EMR system, performing iterative testing with a tabletop simulation, integration into previously established workflows, and gathering post-implementation feedback for continuous improvement.
Laparoscopic Intracorporeal Anastomosis
Authors: Goldstone Robert N. , Popowich Daniel A. 
Published: December 2022

AbstractGiven the progression of laparoscopic surgery, questions continue to arise as to the ideal technique for a laparoscopic colectomy. The most debated of these questions is whether it is best to complete an intracorporeal (ICA) or extracorporeal (ECA) intestinal anastomosis. Here, we review the literature to date and report the equivalent safety and efficacy of ICA and ECA for laparoscopic right colectomy. However, these studies also indicate that when completed, ICA may prove beneficial with respect to earlier return of bowel function, less postoperative pain, shorter incision length, and reduced risk of wound infections. For this, we present the tips and tricks for completing all forms of laparoscopic ICAs during laparoscopic colectomy.
Lateral Thoracic Expansion for Jeune's Syndrome, Surgical Approach, and Technical Details
Authors: Lena Federica , Piro Liliana , Forlini Valentina , Guerriero Vittorio , Salvati Pietro , Stagnaro Nicola , Sacco Oliviero , Torre Michele , Mattioli Girolamo 
Published: December 2022

Abstract
Introduction Jeune's syndrome, or asphyxiating thoracic dystrophy (ATD), is a rare autosomal recessive disorder characterized by skeletal dysplasia. Ribs are typically short and horizontal resulting—in lethal variant—in severe lung hypoplasia, progressive respiratory failure, and death. Lateral thoracic expansion (LTE) consists in staggered bilateral ribs osteotomy leading to chest expansion and lung development. Studies on LTE in ATD patients report encouraging data, but the rarity of ATD implies the lack of a standardized surgical path. The aim of this report is to present our experience with LTE, the technical modification we adopted, and patients' clinical outcome.
Materials and Methods We retrospectively reviewed data of 11 LTE performed in 7 ATD patients with lethal variant. Information regarding pre- and postoperative clinical conditions and surgical details was collected. We adopted a single-stage or a two-stage approach based on patient clinical condition. Computed tomography (CT) scan was performed before and after surgery and lung volume was calculated.
Results Five patients are alive, while two died in intensive care unit for other than respiratory cause (sepsis). Most patients experienced clinical improvement in terms of decreased respiratory infections rate, need for ventilation, and improved exercise tolerance. Postoperative CT scan demonstrated a median lung volume increase of 88%.
Conclusion Mortality in ADT patients is high. However, LTE is a feasible and safe surgical approach, which could improve clinical conditions and survival rate. Survived patients showed postoperatively less oxygen requirement and improved clinical conditions.
Characterization of the surugamide biosynthetic gene cluster of TUA-NKU25, aStreptomyces diastaticusstrain isolated from Kusaya, and its effects on salt-dependent growth
Authors: Takeuchi Aoi , Hirata Asahi , Teshima Aiko , Ueki Miu , Satoh Takumi , Matsuda Kenichi , Wakimoto Toshiyuki , Arakawa Kenji , Ishikawa Morio , Suzuki Toshihiro 
Published: December 2022

ABSTRACTKusaya, a traditional Japanese fermented fish product, is known for its high preservability, as it contains natural antibiotics derived from microorganisms, and therefore molds and yeasts do not colonize it easily. In this study, the Streptomyces diastaticus strain TUA-NKU25 was isolated from Kusaya, and its growth as well as the production of antibiotics were investigated. Strain TUA-NKU25 showed advantageous growth characteristics in the presence, but not in the absence, of sodium chloride (NaCl). Antimicrobial assay, high-performance liquid chromatography, and electrospray ionization-mass spectrometry analysis showed that this strain produced surugamide A and uncharacterized antimicrobial compound(s) during growth in the presence of NaCl, suggesting that the biosynthesis of these compounds was upregulated by NaCl. Draft genomic analysis revealed that strain TUA-NKU25 possesses a surugamide biosynthetic gene cluster (sur BGC), although it is incomplete, lacking surB/surC. Phylogenetic analysis of strain TUA-NKU25 and surugamide-producing Streptomyces showed that sur BGC formed a clade distinct from other known groups.
Large-scale screening of yeast strains that can utilize proline
Authors: Tanahashi Ryoya , Nishimura Akira , Nguyen Minh , Sitepu Irnayuli , Fox Glen , Boundy-Mills Kyria , Takagi Hiroshi 
Published: December 2022

ABSTRACTProline contributes to the taste and flavor of foods. The yeast Saccharomyces cerevisiae poorly assimilates proline during fermentation processes, resulting in the accumulation of proline in fermentative products. We performed here a screening of in total 1138 yeasts to obtain strains that better utilize proline. Our results suggest that proline utilization occurs in the genera of Zygoascus, Galactomyces, and Magnusiomyces.
Inpactor2: a software based on deep learning to identify and classify LTR-retrotransposons in plant genomes
Authors: Orozco-Arias Simon , Humberto Lopez-Murillo Luis , Candamil-Cortés Mariana S , Arias Maradey , Jaimes Paula A , Rossi Paschoal Alexandre , Tabares-Soto Reinel , Isaza Gustavo , Guyot Romain 
Published: December 2022

Abstract
LTR-retrotransposons are the most abundant repeat sequences in plant genomes and play an important role in evolution and biodiversity. Their characterization is of great importance to understand their dynamics. However, the identification and classification of these elements remains a challenge today. Moreover, current software can be relatively slow (from hours to days), sometimes involve a lot of manual work and do not reach satisfactory levels in terms of precision and sensitivity. Here we present Inpactor2, an accurate and fast application that creates LTR-retrotransposon reference libraries in a very short time. Inpactor2 takes an assembled genome as input and follows a hybrid approach (deep learning and structure-based) to detect elements, filter partial sequences and finally classify intact sequences into superfamilies and, as very few tools do, into lineages. This tool takes advantage of multi-core and GPU architectures to decrease execution times. Using the rice genome, Inpactor2 showed a run time of 5 minutes (faster than other tools) and has the best accuracy and F1-Score of the tools tested here, also having the second best accuracy and specificity only surpassed by EDTA, but achieving 28% higher sensitivity. For large genomes, Inpactor2 is up to seven times faster than other available bioinformatics tools.
A review of methods for predicting DNA N6-methyladenine sites
Authors: Han Ke , Wang Jianchun , Wang Yu , Zhang Lei , Yu Mengyao , Xie Fang , Zheng Dequan , Xu Yaoqun , Ding Yijie , Wan Jie 
Published: December 2022

AbstractDeoxyribonucleic acid(DNA) N6-methyladenine plays a vital role in various biological processes, and the accurate identification of its site can provide a more comprehensive understanding of its biological effects. There are several methods for 6mA site prediction. With the continuous development of technology, traditional techniques with the high costs and low efficiencies are gradually being replaced by computer methods. Computer methods that are widely used can be divided into two categories: traditional machine learning and deep learning methods. We first list some existing experimental methods for predicting the 6mA site, then analyze the general process from sequence input to results in computer methods and review existing model architectures. Finally, the results were summarized and compared to facilitate subsequent researchers in choosing the most suitable method for their work.
Multimodal data fusion based on IGERNNC algorithm for detecting pathogenic brain regions and genes in Alzheimer’s disease
Authors: Wang Shuaiqun , Zheng Kai , Kong Wei , Huang Ruiwen , Liu Lulu , Wen Gen , Yu Yaling 
Published: December 2022

Abstract
At present, the study on the pathogenesis of Alzheimer’s disease (AD) by multimodal data fusion analysis has been attracted wide attention. It often has the problems of small sample size and high dimension with the multimodal medical data. In view of the characteristics of multimodal medical data, the existing genetic evolution random neural network cluster (GERNNC) model combine genetic evolution algorithm and neural network for the classification of AD patients and the extraction of pathogenic factors. However, the model does not take into account the non-linear relationship between brain regions and genes and the problem that the genetic evolution algorithm can fall into local optimal solutions, which leads to the overall performance of the model is not satisfactory. In order to solve the above two problems, this paper made some improvements on the construction of fusion features and genetic evolution algorithm in GERNNC model, and proposed an improved genetic evolution random neural network cluster (IGERNNC) model. The IGERNNC model uses mutual information correlation analysis method to combine resting-state functional magnetic resonance imaging data with single nucleotide polymorphism data for the construction of fusion features. Based on the traditional genetic evolution algorithm, elite retention strategy and large variation genetic algorithm are added to avoid the model falling into the local optimal solution. Through multiple independent experimental comparisons, the IGERNNC model can more effectively identify AD patients and extract relevant pathogenic factors, which is expected to become an effective tool in the field of AD research.
A fully differentiable ligand pose optimization framework guided by deep learning and a traditional scoring function
Authors: Wang Zechen , Zheng Liangzhen , Wang Sheng , Lin Mingzhi , Wang Zhihao , Kong Adams Wai-Kin , Mu Yuguang , Wei Yanjie , Li Weifeng 
Published: December 2022

AbstractThe recently reported machine learning- or deep learning-based scoring functions (SFs) have shown exciting performance in predicting protein–ligand binding affinities with fruitful application prospects. However, the differentiation between highly similar ligand conformations, including the native binding pose (the global energy minimum state), remains challenging that could greatly enhance the docking. In this work, we propose a fully differentiable, end-to-end framework for ligand pose optimization based on a hybrid SF called DeepRMSD+Vina combined with a multi-layer perceptron (DeepRMSD) and the traditional AutoDock Vina SF. The DeepRMSD+Vina, which combines (1) the root mean square deviation (RMSD) of the docking pose with respect to the native pose and (2) the AutoDock Vina score, is fully differentiable; thus is capable of optimizing the ligand binding pose to the energy-lowest conformation. Evaluated by the CASF-2016 docking power dataset, the DeepRMSD+Vina reaches a success rate of 94.4%, which outperforms most reported SFs to date. We evaluated the ligand conformation optimization framework in practical molecular docking scenarios (redocking and cross-docking tasks), revealing the high potentialities of this framework in drug design and discovery. Structural analysis shows that this framework has the ability to identify key physical interactions in protein–ligand binding, such as hydrogen-bonding. Our work provides a paradigm for optimizing ligand conformations based on deep learning algorithms. The DeepRMSD+Vina model and the optimization framework are available at GitHub repository https://github.com/zchwang/DeepRMSD-Vina_Optimization.
MARPPI: boosting prediction of protein–protein interactions with multi-scale architecture residual network
Authors: Li Xue , Han Peifu , Chen Wenqi , Gao Changnan , Wang Shuang , Song Tao , Niu Muyuan , Rodriguez-Patón Alfonso 
Published: December 2022

Abstract
Protein–protein interactions (PPIs) are a major component of the cellular biochemical reaction network. Rich sequence information and machine learning techniques reduce the dependence of exploring PPIs on wet experiments, which are costly and time-consuming. This paper proposes a PPI prediction model, multi-scale architecture residual network for PPIs (MARPPI), based on dual-channel and multi-feature. Multi-feature leverages Res2vec to obtain the association information between residues, and utilizes pseudo amino acid composition, autocorrelation descriptors and multivariate mutual information to achieve the amino acid composition and order information, physicochemical properties and information entropy, respectively. Dual channel utilizes multi-scale architecture improved ResNet network which extracts protein sequence features to reduce protein feature loss. Compared with other advanced methods, MARPPI achieves 96.03%, 99.01% and 91.80% accuracy in the intraspecific datasets of Saccharomyces cerevisiae, Human and Helicobacter pylori, respectively. The accuracy on the two interspecific datasets of Human-Bacillus anthracis and Human-Yersinia pestis is 97.29%, and 95.30%, respectively. In addition, results on specific datasets of disease (neurodegenerative and metabolic disorders) demonstrate the ability to detect hidden interactions. To better illustrate the performance of MARPPI, evaluations on independent datasets and PPIs network suggest that MARPPI can be used to predict cross-species interactions. The above shows that MARPPI can be regarded as a concise, efficient and accurate tool for PPI datasets.