Original Structure Research Articles

Proper motion study of the 6.7 GHz methanol maser rings.

Methanol masers at 6.7 GHz are well-known signposts of high-mass star-forming regions. Due to their high brightness, they enable us to derive the three-dimensional gas kinematics near protostars and young stars. We aim to understand the origin of the ring-like structures outlined by methanol maser emission in a number of sources. This emission could be, a priori, spatially associated with an outflow and/or disc around a high-mass protostar. In cases of expansion or rotation, maser proper motions should be, for instance, diverging from the ring centre or perpendicular to the ring radius. Using sensitive, three-epoch observations spanning over eight years with the European VLBI Network, we have started the most direct investigations of maser rings using very accurate proper motion measurements with uncertainties below sim 1\,km We present full results for the five targets of our sample, G23.207$-$00.377, G23.389$+$00.185, G28.817$+$00.365, G31.047$+$00.356, and G31.581$+$00.077, where proper motions show similar characteristics; maser cloudlets do not move inwards towards the centre of the rings but rather outwards. We also include the most circular source, G23.657$-$00.127, in the discussion as a reference. The magnitude of maser proper motions ranges from a maximum of about 13\,km $ to 0.5 km s$^ $. In two of the five sources with a high number of maser spots ($>$\,100), namely G23.207$-$00.377 and G23.389$+$00.185, we show that the size of the best elliptical model, fitted to the distribution of persistent masers, increases in time in a manner similar to the case of G23.657$-$00.127. Moreover, we checked the separations between the pairs of spots from distinct regions, and we were able to assess that G28.817$+$00.365 and G31.047$+$00.356 can be interpreted as showing expanding motions. We analysed the profiles of single maser cloudlets and studied their variability. Contrary to single-dish studies, the interferometric data indicate variability of the emission of single-masing cloudlets. In five of the six targets, namely, G23.207$-$00.377, G23.389$+$00.185, G23.657$-$00.127, G28.817$+$00.365, and G31.047$+$00.356, expansion motions prevail. Only in the case of G31.581$+$00.077 can a scenario of disc-like rotation not be excluded. Complementary observations of thermal tracers as well as searching for ultra-compact H II regions in the same sources are needed. Although the overall morphology of the maser emission has remained stable, the intensities of individual maser cloudlets varied from epoch to epoch, suggesting internal instabilities.

Comparison of Different Methods for Ancient Ship Calm Water Resistance Estimation

The remains of ancient ships from various time periods are commonly found on land and under the sea in conditions that make it difficult to reconstruct their original form and structure. For this reason, the reconstruction should be supported by other data, such as data on similar ships, but also by certain assumptions. The results of the reconstruction are significant not only in a historical sense but are of exceptional importance when building floating replicas. Two ships, Nin 1 and Nin 2, today for promotional purposes known as Condurae Croaticae, were found in Nin (Croatia) at the end of the 1960s. They are about 8 to 10 m long, and tentatively dated to the 11th century AD, although there are indications that they could be dated two centuries later. Based on archaeological finds exhibited in the Museum of Nin Antiquities, hull line drawings were created, according to which two floating replicas were made at the end of the 1990s. Considering the problem of hogging that appeared in both ships, a new proposal for the reconstruction of the original hull lines was performed based on the available documentation. The aim of this paper is a systematic analysis of its calm water resistance. Based on the established credibility of experimental testing, a scale model (1:4 ratio) of the Nin 1 vessel is constructed and evaluated through towing tank experiments. The second approach, the CFD method, is a reliable numerical method for calm resistance estimation, but it is rarely used in the analysis of ancient ships. Finally, the widely used empirical Holtrop method is also applied, but it was developed for ships of larger dimensions and with large parts of flat bottoms and, therefore, the more appropriate Delft Hull Yacht Series method is also tested. The results obtained by applying the four mentioned methods are compared and discussed.

Comparative Analysis of Deep Generative Model for Industrial Enzyme Design

Abstract: Although enzymes have the advantage of efficient catalysis, natural enzymes lack stability in industrial environments and do not even meet the required catalytic reactions. This prompted us to urgently de novo design new enzymes. Computational design is a powerful tool, allowing rapid and efficient exploration of sequence space and facilitating the design of novel enzymes tailored to specific conditions and requirements. It is beneficial to de novo design industrial enzymes using computational methods. Currently, only one tool explicitly designed for the enzyme-only generation performs unsatisfactorily. We have selected several general protein sequence design tools and systematically evaluated their effectiveness when applied to specific industrial enzymes. We investigated the literature related to protein generation. We summarized the computational methods used for sequence generation into three categories: structure-conditional sequence generation, sequence generation without structural constraints, and co-generation of sequence and structure. To effectively evaluate the ability of six computational tools to generate enzyme sequences, we first constructed a luciferase dataset named Luc_64. Then we assessed the quality of enzyme sequences generated by these methods on this dataset, including amino acid distribution, EC number validation, etc. We also assessed sequences generated by structure-based methods on existing public datasets using sequence recovery rates and root-mean-square deviation (RMSD) from a sequence and structure perspective. In the functionality dataset, Luc_64, ABACUS-R, and ProteinMPNN stood out for producing sequences with amino acid distributions and functionalities closely matching those of naturally occurring luciferase enzymes, suggesting their effectiveness in preserving essential enzymatic characteristics. Across both benchmark datasets, ABACUS-R and ProteinMPNN, have also exhibited the highest sequence recovery rates, indicating their superior ability to generate sequences closely resembling the original enzyme structures. Our study provides a crucial reference for researchers selecting appropriate enzyme sequence design tools, highlighting the strengths and limitations of each tool in generating accurate and functional enzyme sequences. ProteinMPNN and ABACUS-R emerged as the most effective tools in our evaluation, offering high accuracy in sequence recovery and RMSD and maintaining the functional integrity of enzymes through accurate amino acid distribution. Meanwhile, the performance of protein general tools for migration to specific industrial enzymes was fairly evaluated on our specific industrial enzyme benchmark.

Contrastive Language-knowledge Graph Pre-training

Recent years have witnessed a surge of academic interest in knowledge-enhanced pre-trained language models (PLMs) that incorporate factual knowledge to enhance knowledge-driven applications. Nevertheless, existing studies primarily focus on shallow, static, and separately pre-trained entity embeddings, with few delving into the potential of deep contextualized knowledge representation for knowledge incorporation. Consequently, the performance gains of such models remain limited. In this article, we introduce a simple yet effective knowledge-enhanced model, College ( Co ntrastive L anguage-Know le dge G raph Pr e -training), which leverages contrastive learning to incorporate factual knowledge into PLMs. This approach maintains the knowledge in its original graph structure to provide the most available information and circumvents the issue of heterogeneous embedding fusion. Experimental results demonstrate that our approach achieves more effective results on several knowledge-intensive tasks compared to previous state-of-the-art methods. Our code and trained models are available at https://github.com/Stacy027/COLLEGE .

GSGSL: Gravity-driven self-supervised graph structure learning

The existing graph structure learning methods heavily rely on the original graph structure and often fail to capture potential high-level abstract features and global correlations within the graph. To address these issues, this paper proposes a Universal Gravitational-driven Self-supervised Graph Structure Learning method (GSGSL), overcoming the limitations of current graph structure learning methods in advanced feature extraction. GSGSL models the graph structure using the universal force of gravity in a dynamic, globally adaptive manner. Additionally, it employs a multi-perspective contrastive learning approach to eliminate the need for external labels, jointly optimizing graph structure learning with downstream tasks. Extensive experimental results on public datasets demonstrate that, in comparative experiments without considering the original adjacency matrix, the GSGSL method outperforms baseline models by 0.5% to 17.3%. In comparative experiments optimizing the original adjacency matrix, the GSGSL method exhibits improvements ranging from 0.9% to 12%, validating that the GSGSL approach to simulating gravitational fields has better dynamic characteristics. It effectively captures advanced abstract features and global characteristics of graph data, surpassing the limitations of baseline methods in graph structure learning.

Atomically Deciphering the Phase Segregation in Mixed Halide Perovskite

AbstractMixed‐halide perovskites show promising applications in tandem solar cells owing to their adjustable bandgap. One major obstacle to their commercialization is halide phase segregation, which results in large open‐circuit voltage deficiency and J‐V hysteresis. However, the ambiguous interplay between structural origin and phase segregation often results in aimless and unspecific optimization strategies for the device's performance and stability. An atomic scale is directly figured out the abundant Ruddlesden‐Popper anti‐phase boundaries (RP‐APBs) within a CsPbIBr2 polycrystalline film and revealed that phase segregation predominantly occurs at RP‐APB‐enriched interfaces due to the defect‐mediated lattice strain. By compensating their structural lead halide, such RP‐APBs are eliminated, and the decreasing of strain can be observed, resulting in the suppression of halide phase segregation. The present work provides the deciphering to precisely regulate the perovskite atomic structure for achieving photo‐stable mixed halide wide‐bandgap perovskites of high‐efficiency tandem solar cell commercial applications.

Ultrahigh breakdown strength of NaNbO3‐based dielectric ceramics for high‐voltage capacitor application

AbstractDielectric ceramics have attracted significant attention in power electronic systems, owing to their exceptional charging and discharging speeds, as well as their high power density. However, simultaneously achieving a high recoverable energy density (Wrec) and high efficiency (η) in high‐voltage dielectric ceramics remains a challenge for applications where a high breakdown electric field (Eb) is required. In this study, high‐quality (1 – x)NaNbO3–xSr0.7Bi0.2(Mg1/3Nb2/3)O3 [(1 – x)NN–xSBMN] ceramics were prepared based on an optimization strategy combining phase structure with microstructural regulation. A quasilinear P–E loop with negligible hysteresis was realized in the ceramic with x = 0.4, excellent Wrec of 5.61 J/cm3, and high η of 85.1% obtained at a largely improved Eb of 710 kV/cm. To the best of our knowledge, the Eb of 710 kV/cm is one of the highest values achieved in dielectric ceramics to date. The noticeable reduction in grain size (∼0.95 µm) and increased bandgap improve the Eb to an ultra‐high level, which is a crucial factor in high energy storage density. The coexistence of a few antiferroelectric phases and the dominant paraelectric phase is the structural origin of the comprehensive energy‐storage performance improvement. Therefore, our research develops a unique approach to unleash the potential in NaNbO3‐based ceramics, holding great promise for application in high‐voltage dielectric capacitors.

The Brief Version of the Mentalization Scale (MentS-12): Evidence-Based Assessment of Mentalizing Capacity.

Recognizing the need for a concise self-report measure of mentalizing capacity, we developed a 12-item iteration of the well-established Mentalization Scale (MentS). Using college student and community samples of Serbian adults (N = 566), we performed a precise selection of items and then examined the psychometric attributes of the shortened scale (MentS-12). The new scale maintains the original three-dimensional structure: self-related mentalization, other-related mentalization, and motivation to mentalize. MentS-12 proves to be both reliable and structurally consistent. To improve its utility in therapeutic contexts, we determined clinical change thresholds for both the complete and abbreviated forms. We hope that its feasibility stimulates the integration of the MentS-12 in longitudinal research projects and real-world clinical settings.

Cellulose nanocrystals intercalated clay biocomposite for rapid Cr(VI) removal.

The application of bentonite (Bt) as an adsorbent for heavy metals has been limited due to its hydrophobicity and insufficient surface area. Herein, we present cellulose nanocrystal (CNC) modified Bt composite (CNC@Bt) with enhanced efficiency for Cr(VI) removal. CNC@Bt exhibited an increased specific surface area and a porous structure, while maintaining the original crystal structure of Bt. This was achieved through a synergistic function of ion exchange, hydrogen bonding, electrostatic interactions, and steric hindrance. The adsorption of Cr(VI) by CNC@Bt followed the pseudo-second-order kinetic and Langmuir isotherm adsorption model. Moreover, the process was endothermic and spontaneous. At an initial Cr(VI) concentration of 20mg/L and pH = 4.0, 10g/L CNC@Bt achieved a removal rate of 92.7%, and the adsorption capacity was 1.85mg/g, significantly higher than bare Bt (37.9% and 0.76mg/g). The removal efficiency remained consistently above 80% over a wide pH range, indicating the potential practical applicability of CNC@Bt. With its fast adsorption rate, pH adaptability, and stable performance, CNC@Bt presents promising prospects for the rapid treatment of Cr-contaminated wastewater.

Engineered and Mimicked Extracellular Nanovesicles for Therapeutic Delivery.

Exosomes are spherical extracellular nanovesicles with an endosomal origin and unilamellar lipid-bilayer structure with sizes ranging from 30 to 100 nm. They contain a large range of proteins, lipids, and nucleic acid species, depending on the state and origin of the extracellular vesicle (EV)-secreting cell. EVs' function is to encapsulate part of the EV-producing cell content, to transport it through biological fluids to a targeted recipient, and to deliver their cargos specifically within the aimed recipient cells. Therefore, exosomes are considered to be potential biological drug-delivery systems that can stably deliver their cargo into targeted cells. Various cell-derived exosomes are produced for medical issues, but their use for therapeutic purposes still faces several problems. Some of these difficulties can be avoided by resorting to hemisynthetic approaches. We highlight here the uses of alternative exosome-mimes involving cell-membrane coatings on artificial nanocarriers or the hybridization between exosomes and liposomes. We also detail the drug-loading strategies deployed to make them drug-carrier systems and summarize the ongoing clinical trials involving exosomes or exosome-like structures. Finally, we summarize the open questions before considering exosome-like disposals for confident therapeutic delivery.

The Turkish version of nursing practice readiness scale: Cross-cultural adaptation and psychometric evaluation.

This study aims to adapt the "Nursing Practice Readiness Scale" to Turkish culture, and evaluate its psychometric properties. New graduates' nursing practice readiness can impact their work adaptation and performance. The research employed a methodological design. Data were collected between May and July 2022. The sample consisted of 436 newly graduated nurses. Content validity, construct validity and criterion validity were evaluated. Reliability was examined with adjusted item-total correlation, Cronbach's a coefficient, composite-reliability and split-half reliability. The Turkish version of Nursing Practice Readiness Scale was found to have good content and criterion validity. As a result of confirmatory factor analysis, the original five-factor structure of the scale was also confirmed for the Turkish version. The scale's overall Cronbach's α coefficient was determined to be 0.96, with subscale coefficients ranging from 0.73 to 0.94. The composite reliability values of the subscales were found between 0.75 and 0.94. In split-half reliability, the correlation coefficient between half was 0.952, with a Spearman-Brown Coefficient (Unequal Length) of 0.976. The Turkish version of Nursing Practice Readiness Scale is a valid and reliable measurement tool for evaluating the nursing practice readiness of newly graduated nurses.

Emotional intelligence in young emerging adults: A focus on Wong and Law's scale in the digital work sphere

Despite the growing research on the usefulness and validity of the four-factor Wong and Law Emotional Intelligence Scale (WLEIS), empirical evidence for its relevance to Black African young emerging adults seems non-existent. The study's objective was to assess the relevance of the original WLEIS factor structure for a sample (N = 365) of South African Technical and Vocational Education and Training (TVET) final year students (mean age = 24.3 years; SD = 2.38). Exploratory and confirmatory factor analyses, and convergent and discriminant validity tests revealed a three-factor first-order structure with uniquely descriptive items characteristic of the sample's emotional intelligence. The findings contribute to the WLEIS measurement of emotional intelligence in young emerging adults who are preparing to enter the digital-age work world.

Experimental and theoretical investigation on the high activity La0.8-xSr0.2KxMn1-yByO3-δ perovskite catalyst with combined A and B site modifications

In this work, a series of LaMnO3 (LM) perovskite catalysts modified at A and B sites were prepared to accelerate soot combustion. The La0.68Sr0.2K0.12Mn0.9Cu0.1O3-δ (LSK0.12MCu0.1) modified by Sr, K and Cu has good catalytic activity and cycle stability, the presence of NO can obviously promote the oxidation of soot. A variety of characterization results show that doping the A and B sites of perovskite will not destroy the original structure of perovskite, and the doped ions (Sr2+, K+, and Cu2+) can enter the perovskite catalyst well, which may be conducive to the formation of more oxygen defects and active oxygen species. According to the DFT calculation, adding Sr and K to the A sites can alter how atoms interact in a perovskite catalyst, increase the amount of unsaturated coordination bonds, and encourage the development of high-valence active ions (Mn4+) and oxygen defects. The doping of transition metal to the B sites results in the formation of the Cu-O-Mn link, which forms the Mn(Cu)O6 octahedron and stimulates the development of oxygen vacancies and the release of active oxygen. This study will provide an important reference for developing non-precious metal-based perovskite materials with high activity and low cost for catalytic combustion.

Beliefs About Sexual Functioning Scale: Cross-Cultural Adaptation and Validation of Psychometric Properties Within Turkish Population.

Background and Purpose: Beliefs about sexual function play a very significant role in determining sexual dysfunctions. There are very few short and reliable scales that can be administered to both men and women to measure their beliefs about sexual function. In this study, the researchers aimed to define the cross-culturally adapted Turkish version of the Beliefs about Sexual Functioning Scale and to evaluate its psychometric properties. Methods: In this methodological study which included 192 people, data were collected through face-to-face interviews using the personal information form and the Beliefs about Sexual Functioning Scale. Results: The content validity index and Cronbach's alpha value of the scale were .96 and .726, respectively. Findings indicate that the four-factor structure of the scale, whose items were reduced from 15 to 11, showed a good fit. According to the results of exploratory factor analysis, the scale preserved its original structure, but one subdimension (anal sex beliefs) was excluded from the scale because it was not suitable for the Turkish culture. The remaining four subdimensions accounted for 61.71% of the total variance. Conclusions: The Beliefs about Sexual Functioning Scale can be used to determine the beliefs about sexual functions of people living in Turkey.

Collaborative graph contrastive learning for recommendation

Recently, Graph Neural Networks (GNNs) using aggregating neighborhood collaborative information have shown effectiveness in recommendation. However, GNNs-based models suffer from over-smoothing and data sparsity problems. Due to its self-supervised nature, contrastive learning has gained considerable attention in the field of recommendation, aiming at alleviating highly sparse data. Graph contrastive learning models are widely used to learn the consistency of representations by constructing different graph augmentation views. Most current graph augmentation with random perturbation destroy the original graph structure information, which mislead embeddings learning. In this paper, an effective graph contrastive learning paradigm CollaGCL is proposed, which constructs graph augmentation by using singular value decomposition to preserve crucial structure information. CollaGCL enables perturbed views to effectively capture global collaborative information, mitigating the negative impact of graph structural perturbations. To optimize the contrastive learning task, the extracted meta-knowledge was propagate throughout the original graph to learn reliable embedding representations. The self-information learning between views enhances the semantic information of nodes, thus alleviating the problem of over-smoothing. Experimental results on three real-world datasets demonstrate the significant improvement of CollaGCL over state-of-the-art methods.

Validation of the Revised Impact of Miscarriage Scale in the Republic of Burundi: A cross-sectional multicenter study.

The objective of this study was to translate and validate the Revised Impact of Miscarriage Scale (RIMS) into Kirundi for use among women and men in Burundi. Additionally, the study aimed to compare the experience and personal meaning of miscarriage between women and men. This is a cross-sectional multicentered study. The RIMS was translated into Kirundi. Cronbach coefficient alpha and its internal consistency were measured for both genders. An exploratory factor analysis (EFA) was used to determine the underlying factors and the shared variance. Both women and men completed the RIMS questionnaire, while women completed sociodemographic, reproductive and mental health questions. In all, 79 couples completed the RIMS. The original factor structure was retained after the EFA, with 68% of the shared variance explained in the three-factor solution with 16 questions. Isolation/guilt, Loss of baby, and Devastating event.The internal consistency for women and men combined was α = 0.928. Although women scored higher on the factors of Isolation/guilt and Loss of baby, there were no significant differences in the Devastating event factorbetween women and men. Couples scores were positively correlated. Women who had experienced a previous miscarriage were more significantly impacted by all three factors compared to women experiencing their first miscarriage. The Kirundi translation of the RIMS retained the original factor structure and demonstrated excellent internal consistency α = 0.928 in women and men combined. The RIMS could be a tool for caregivers to identify individuals who require additional support after a miscarriage.

Phylogeography, origin and population structure of the self-fertile emerging plant pathogen Phytophthora pseudosyringae.

Phytophthora pseudosyringae is a self-fertile pathogen of woody plants, particularly associated with tree species from the genera Fagus, Notholithocarpus, Nothofagus and Quercus, which is found across Europe and in parts of North America and Chile. It can behave as a soil pathogen infecting roots and the stem collar region, as well as an aerial pathogen infecting leaves, twigs and stem barks, causing particular damage in the United Kingdom and western North America. The population structure, migration and potential outcrossing of a worldwide collection of isolates were investigated using genotyping-by-sequencing. Coalescent-based migration analysis revealed that the North American population originated from Europe. Historical gene flow has occurred between the continents in both directions to some extent, yet contemporary migration is overwhelmingly from Europe to North America. Two broad population clusters dominate the global population of the pathogen, with a subgroup derived from one of the main clusters found only in western North America. Index of association and network analyses indicate an influential level of outcrossing has occurred in this preferentially inbreeding, homothallic oomycete. Outcrossing between the two main population clusters has created distinct subgroups of admixed individuals that are, however, less common than the main population clusters. Differences in life history traits between the two main population clusters should be further investigated together with virulence and host range tests to evaluate the risk each population poses to natural environments worldwide.

Elitism-crossover barnacle mating optimization and its application to PID controller design for a buck converter

This paper presents an elitism-crossover barnacle mating optimization (ECBMO). It is an improvement of barnacle mating optimization (BMO). The original BMO suffers from local optima problem leading to a low accurate solution. A new method of offspring generation is adopted into the original BMO structure. Some features of the best-so-far solution are incorporated into the generated offspring. The accuracy performance of the proposed algorithm is tested on several IEEE functions. A statistical analysis is conducted to compare its performance over the original BMO. It is also applied to optimize proportional integral derivative (PID) parameters for controlling output voltage of a buck converter. Result of benchmark functions test shows the proposed algorithm has attained higher accuracy for all functions compared to BMO algorithm. Application on the real problem shows both algorithms control the converter voltage satisfactorily. However, the ECBMO has achieved more optimal PID parameters and leading to a better output voltage response.

Fc-COOH modified MIL-101(Cr) as enhanced photocatalyst to efficiently degrade tetracycline under visible light

MIL-101(Cr) is an ideal material for wastewater treatment due to its large specific surface area, excellent water stability, and chemical stability. However, its limited light absorption capacity hinders its application. We loaded ferrocene carboxylic acid (Fc-COOH) onto MIL-101(Cr) by a post-synthetic modification method, which resulted in the MOF possessing excellent optical properties while maintaining its original skeleton structure. The MIL-101(Cr)/Fc-COOH (1:2), (abbreviated as CFCC-2) exhibited the best performance, achieving a removal rate of 96% for tetracycline (TC) in just 30 min. This removal rate is significantly higher compared to MIL-101(Cr) and Fc-COOH, which achieved removal rates of only 15% and 21%, respectively. We propose a possible Z-type heterojunction mechanism between MIL-101(Cr) and Fc-COOH to explain the excellent photocatalytic performance of CFCC-2. The intermediates in TC were identified using LC-MS, and the toxicity of these intermediates was predicted and evaluated through the Toxicity Estimation Software Tool (T.E.S.T.). This study provides new insights into the design and fabrication of MIL-101(Cr)-based catalysts as photodegrading organic pollutants.

ProNet DB: a proteome-wise database for protein surface property representations and RNA-binding profiles.

The rapid growth in the number of experimental and predicted protein structures and more complicated protein structures poses a significant challenge for computational biology in leveraging structural information and accurate representation of protein surface properties. Recently, AlphaFold2 released the comprehensive proteomes of various species, and protein surface property representation plays a crucial role in protein-molecule interaction predictions, including those involving proteins, nucleic acids and compounds. Here, we proposed the first extensive database, namely ProNet DB, that integrates multiple protein surface representations and RNA-binding landscape for 326 175 protein structures. This collection encompasses the 16 model organism proteomes from the AlphaFold Protein Structure Database and experimentally validated structures from the Protein Data Bank. For each protein, ProNet DB provides access to the original protein structures along with the detailed surface property representations encompassing hydrophobicity, charge distribution and hydrogen bonding potential as well as interactive features such as the interacting face and RNA-binding sites and preferences. To facilitate an intuitive interpretation of these properties and the RNA-binding landscape, ProNet DB incorporates visualization tools like Mol* and an Online 3D Viewer, allowing for the direct observation and analysis of these representations on protein surfaces. The availability of pre-computed features enables instantaneous access for users, significantly advancing computational biology research in areas such as molecular mechanism elucidation, geometry-based drug discovery and the development of novel therapeutic approaches. Database URL: https://proj.cse.cuhk.edu.hk/aihlab/pronet/.