bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abnormalities in EEG microstates among individuals with major depression and obsessive- compulsive disorder
Authors: Proshina Ekaterina , Portnova Galina , Khayrullina Guzal , Bainbridge Emily , Martynova Olga 
Published: November 2024

Abstract

Electroencephalogram microstate analysis is a valuable tool for studying the rapid dynamics of large-scale brain networks. Research has shown that microstate changes are associated with mental disorders, particularly major depressive disorder (MDD) and obsessive-compulsive disorder (OCD). Despite their frequent comorbidity and symptomatic overlap, MDD and OCD often require different treatment approaches. By analyzing the patterns of microstates in individuals with OCD, MDD and a control group, we aim to improve our understanding of the neural pathways involved in these disorders. Resting state EEG data were collected from 32 individuals diagnosed with MDD, 26 individuals with OCD and 40 healthy controls. The results showed that the duration, occurrence and coverage of microstate C were significantly higher in the OCD group than in the MDD group. Conversely, both the duration and coverage of microstate D were lower in the OCD group than in MDD and controls. A negative correlation was found between Beck Depression Inventory scores and the duration and coverage of microstate A specifically within the OCD group. Our study revealed that the microstate patterns observed in MDD and OCD were markedly distinct, with microstates A, C, and D emerging as the most indicative in the context of OCD.
FedECA: A Federated External Control Arm Method for Causal Inference with Time-To-Event Data in Distributed Settings
Authors: du Terrail Jean Ogier , Klopfenstein Quentin , Li Honghao , Mayer Imke , Loiseau Nicolas , Hallal Mohammad , Debouver Michael , Camalon Thibault , Fouqueray Thibault , Castro Jorge Arellano , Yanes Zahia , Dahan Laëtitia , Taieb Julien , LAURENT-PUIG Pierre , Bachet Jean-Baptiste , Zhao Shulin , Nicolle Rémy , Cros Jérôme , Gonzalez Daniel , Carreras-Torres Robert , Velasco Adelaida Garcia , Abdilleh Kawther , Doss Sudheer , Balazard Félix , Andreux Mathieu 
Published: November 2024

Abstract

External control arms (ECA) can inform the early clinical development of experimental drugs and provide efficacy evidence for regulatory approval. However, the main challenge in implementing ECA lies in accessing real-world or historical clinical trials data. Indeed, regulations protecting patients' rights by strictly controlling data processing make pooling data from multiple sources in a central server often difficult. To address these limitations, we develop a new method, 'FedECA' that leverages federated learning (FL) to enable inverse probability of treatment weighting (IPTW) for time-to-event outcomes on separate cohorts without needing to pool data. To showcase the potential of FedECA, we apply it in different settings of increasing complexity culminating with a real-world use-case in which FedECA provides evidence for a differential effect between two drugs that would have otherwise gone unnoticed. By sharing our code, we hope FedECA will foster the creation of federated research networks and thus accelerate drug development.
Entity Matching with Quantum Neural Networks
Authors: Bischof Lukas , Teodoropol Stefan , Füchslin Rudolf M. , Stockinger Kurt 
Published: November 2024

Abstract

Modern technology and scientific experiments increasingly generate larger and larger amounts of data. This data is sometimesredundant, incomplete or inaccurate and needs to be cleaned and merged with other data before becoming useful for scientificexploration. Hence, entity matching, i.e. the process of linking data about a given entity gathered from multiple data sets, is amajor problem in artificial intelligence with applications in science and industry. Typical methods for entity matching either usespecialized algorithms or supervised machine learning. While the problem is well studied on classical computers, it is unclearhow quantum approaches would tackle these challenges. In this paper, we evaluate quantum machine learning algorithmsfor entity matching on a handcrafted data set and compare them to similar classical algorithms. We do this by implementinga neural network with a classical embedding layer and extending it with quantum layers. Our experimental results suggestthat our hybrid quantum neural network can improve the performance of some classical approaches while requiring fewerparameters than its classical counterpart. Furthermore, we also show that a model trained on a quantum simulator is portableand thus transferable to a real quantum computer.
T-DNAreader: Fast and precise identification of T-DNA insertion sites in plant genomes using RNA-sequencing data
Authors: Lee Hongwoo , Seo Pil Joon 
Published: November 2024

Abstract

Agrobacterium-mediated plant transformation, which enables the delivery of DNA using transfer DNA (T-DNA) binary vectors, is an essential technique in plant research. T-DNAs randomly integrate into the host genome, and multiple T-DNAs can integrate during a single transformation, necessitating the development of tools to elucidate the T-DNA insertion sites (TISs). Here, we developed T-DNAreader to identify TISs from RNA-sequencing data with high precision, sensitivity, and speed, outperforming existing tools. Notably, T-DNAreader detected previously unknown TISs in characterized mutant plants. Overall, T-DNAreader enables the efficient and reliable identification of TISs within transcribed regions and standardizes the characterization of T-DNA-containing transgenic plants.
Multiple effects of modified biochar in saline-alkali soil: reducing salinity, providing nutrition, and promoting root colonization by beneficial bacteria
Authors: Wang Jiyuan , Riaz Muhammad , Babar Saba , El-Desouki Zeinab , Li Yuxuan , Wang Xiangling , Xia Xiaoyang , Jiang Cuncang 
Published: November 2024

Abstract

Aims
Soil salinization is a serious problem worldwide that restricts agricultural production and sustainability. Biochar modification is a novel method to enhance its amelioration effect of on saline-alkali soil.
Methods
We carried out a pot experiment to compare the effects of four different biochar, including pristine biochar (PBC), acid-modified biochar (HBC), iron-modified biochar (FBC), and acid-iron-modified biochar (HFBC), on the plant growth, chemical properties of saline-alkali soil, and the communities of soil bacteria and endophytic bacteria.
Results
The results showed that FBC had the best effect on promoting cotton growth and reducing soil salinity and alkalinity among four biochar materials. Compared to the CK (non-biochar) treatment, the application of FBC improved the biomass of cotton seedlings by 2.9 folds. Meanwhile, the lowest Na+ content in the 0–15 cm depth of soil was discovered in FBC treatment followed with HBC, PBC, and HFBC treatments, which were decreased by 36.5%, 33.5%, 27.1%, and 15.1%, respectively, compared to the CK. Importantly, we found that the FBC amendment significantly increased the soil available Fe content and Fe content in cotton shoot. Additionally, the application of FBC enhanced the soil bacterial diversity and increased the abundance of Proteobacteria and Bacteroidota. Moreover, FBC addition facilitated the colonization of beneficial endophytes (e.g., Alcaligenaceae) in roots.
Conclusions
Our results indicate that FBC is a promising and novel material for improving saline-alkali soil due to its multiple effects, including reducing salinity, providing nutrition, and promoting root colonization by beneficial bacteria.
The “ins and outs” of electropolymerized PEDOT organic electrochemical transistors
Authors: Cicoira Fabio , Wang Mejing , Fan Jiaxin , Calame Michel Bilodeau , Kim Chihyeong , Chiang Cheng-Ling , Segura Ademar Fabricio Chaverri , Vurro Vito , Bargigia Ilaria , Mauzeroll Janine 
Published: November 2024

Abstract

Organic electrochemical transistors (OECTs) using poly(3,4-ethylenedioxythiophene) (PEDOT) film as the channel material have been widely studied. However, OECTs based on electropolymerized PEDOT, doped with either large or small ions, have received comparatively limited attention, leaving the ionic dynamics during doping and dedoping insufficiently understood. In this work, we report stable OECTs using electropolymerized PEDOT that exhibit minimal drain current degradation after 1,000 cycles in NaCl aqueous solution. Compared to inkjet-printed OECTs, electropolymerized devices show significantly enhanced cycling stability, with the addition of the small anion dopant (ClO4⁻) further increasing PEDOT stability and cycling performance. Electrochemical Quartz Crystal Microbalance measurements reveal unique ionic transport behavior in PEDOT:ClO4, where dopant ion movement plays a more prominent role compared to PEDOT:PSS. This study highlights the advantages of electropolymerization and small anion incorporation, providing valuable insights into the functional mechanisms of OECTs across different PEDOT systems.
Enhanced Chinese Mural Face Generation via FreqSplitAttention and Dual Mask Discriminator
Authors: Huang Qiangying , Cheng Liangfei , Li Xiayong , Zhong Weikang , Wu Hao , Yuan Guowu 
Published: November 2024

Abstract

As an important part of traditional art, Chinese murals embody rich historical and cultural connotations, especially the facial images, which reflect the political, cultural, and religious aspects of ancient society. However, the erosion caused by natural environments and human factors poses significant threats to the protection and inheritance of this cultural heritage. Therefore, the application of digital technology to protect mural faces is particularly urgent. Currently, the research on mural face digital generation faces numerous challenges, including the lack of standardized datasets, inadequate detail retention in generated images, inconsistent styles, and a tendency to overfit. These issues directly impact the effectiveness of mural face digitization. To address these challenges, we propose an improved StyleGAN2 generation architecture based on FreqSplitAttention. This method leverages Fourier transforms to separate high-frequency and low-frequency components of images, achieving simultaneous attention and optimization for both the global structure and local details of mural faces, thereby significantly enhancing the quality and structural consistency of the generated images. Additionally, we introduce a dynamic masking training strategy, employing a Dual Mask Discriminator (DMD) in both spatial and spectral domains to suppress the discriminator's shortcut learning, effectively alleviating overfitting issues during GAN training on limited datasets. Furthermore, we construct a high-quality digital Dunhuang mural face dataset named DMF containing 9552 images with a resolution of 256×256. Experimental results demonstrate that our method excels in generating high-quality and diverse Chinese mural face images. On the DMF dataset, our model achieved the lowest FID score of 27.74, the lowest KID score of 10.22, and the highest IS score of 2.19, all outperforming existing state-of-the-art models. Our method not only preserves the cultural essence of traditional murals but also infuses them with new vitality, providing important theoretical and practical references for the digital preservation and cultural inheritance of murals. Additionally, the DMF dataset we constructed offers a valuable resource for the study of mural art.
Rapid Aerogel Fabrication Facilitates a Range of Applications
Authors: Anderson Ann M. , Carroll Mary K. 
Published: November 2024

Abstract

Rapid supercritical extraction (RSCE) methods for the preparation of aerogel materials have advantages over other methods of aerogel fabrication, including significantly shorter timescales from mixing chemicals to obtaining aerogels and lower chemical waste; however, they are not employed widely. In this retrospective article, three RSCE methods that have been used for preparation of monolithic silica aerogel are described. Each involves placing a gel precursor mixture into a contained mold and bringing the temperature and pressure inside the mold above the critical point of the solvent in the pores of the wet gel. The RSCE method that uses a contained mold and a hydraulic hot press has also been employed for preparation of a variety of types of aerogel. Its use in development of aerogel-based windows, in applications in which dopants are readily incorporated into aerogels, and in preparation of aerogels with curvilinear shapes are emphasized. Advantages as well as limitations of RSCE methods are discussed.
The future is fully defined: recombinant fragment E8 of laminin-511 is a viable xenofree alternative to Matrigel for hiPSC culture and differentiation into neurovascular cell types
Authors: Ferreira Laís A. , Nascimento Denise Fabiano do , Tandon Ishita , Cordes Lance , Balachandran Kartik 
Published: November 2024

Abstract

Matrigel remains the gold standard substrate for culture of induced pluripotent stem cells (iPSCs). However, its highly variable composition, animal origin and unpredictable effects on biological activity have been discussed for more than 3 decades. In this study, we comparatively explored the use of fragment E8 of recombinant laminin 511 (iMatrix) only as an alternative for iPSC maintenance and differentiation, aiming to demonstrate whether other known and unknown components of Matrigel played a critical role in relevant cell behavior and responses. iPSCs were cultured on either iMatrix or Matrigel and assessed for cell growth and viability, pluripotency, genetic stability, and ability to differentiate into isogenic brain microvascular endothelial cells (iBMECs) and brain pericytes. It was observed that iMatrix facilitated iPSC growth and viability comparable to Matrigel while maintaining a higher number of more consistently sized colonies. Additionally, like Matrigel, iMatrix maintained the expression of pluripotency markers SSEA-4 and OCT-3/4 over 15 passages without inducing DNA damage. iMatrix also supported the differentiation of these iPSCs into isogenic iBMECs and pericytes, which were successfully co-culture for generation of a simplified blood-brain barrier model. Overall, we showed that iMatrix, which is a cost effective, fully defined, and xenofree alternative can be used as a substitute for Matrigel for maintenance and differentiation of iPSCs yielding equivalent results.
WITHDRAWN: Housing price prediction using numeric and textual data in Karachi
Authors: KHAN MUHAMMAD KASHIF , KIRCHBERGER MARTINA 
Published: November 2024

Abstract

The study explores the critical issue of housing price estimation in Karachi, Pakistan which is a rapidly growing megacity with significant housing demand. The study highlights the challenges faced by potential homebuyers and sellers, emphasizing the importance of accurate price predictions for effective real estate transactions and policy making by organizations like the House Building Finance Corporation (HBFC). Karachi, with a population of approximately 24 million, experiences a housing deficit, requiring around 100,000 new units annually while only 60,000 are supplied. This gap has led to the proliferation of illegal settlements, exacerbating socio-economic issues, particularly among the 75 percent of the population classified as poor. The study underscores the societal perception of homeownership as a status symbol, driving individuals to prioritize savings to achieve this goal despite rising property taxes and inflation that affects affordability. This research also highlights the limitations of traditionally used numeric feature based models that often overlook the significance of textual data. Moreover, this study aims to enhance the capability of housing price prediction models by integrating both numeric features (e.g., location, size, number of bedrooms) and textual descriptions of properties. The study employs three word embedding techniques that are BERT, TF-IDF, and Word2Vec to analyze the textual data. Additionally, four regression machine learning algorithms namely, gradient boosting, random forest, support vector machines, and deep neural networks are utilized to predict property values based on the collected dataset of approximately 18,000 properties from a prominent Pakistani real estate website.The methodology includes three experimental setups: one focusing solely on numeric data, another on textual data, and a third combining both to identify the most effective model. The study's practical contribution is the development of a housing price prediction code, accessible via GitHub, which provides users with estimated property prices based on comprehensive data analysis. Although the model based on descriptive data alone gives better results than traditional numeric data based models, the concatenation of both numeric and descriptive data provides even better outcomes based on gradient boosting algorithm. Thus, the document concludes with emphasizing the integration of qualitative and quantitative data to improve prediction accuracy. Overall, this research addresses a vital need for robust housing price prediction tools in Karachi, aiming to facilitate informed decision making for buyers, sellers, and policymakers alike.
Reproductive performance of mangrove crab broodstock, Scylla serrata in a recirculating aquaculture system with polychaete-assisted sand biofilter
Authors: Calunod Charles Van , Maceren-Pates Mercedes , Pates Gaudioso , Nemeño Derly , Quinitio Emilia , Estepa Fe Dolores , Arcilla Mark , Añasco Leny 
Published: November 2024

Abstract

The increasing global demand for mangrove crabs (genus Scylla) and threats to the wild populations highlight the urgency of fully rearing them in captivity. The Southeast Asian Fisheries Development Center/Aquaculture Department (SEAFDEC/AQD) has developed the basic larval rearing and nursery protocols for mangrove crab. However, the supply of hatchery seedstock is still inadequate to meet the requirements for farming. One of the major causes is the inconsistent larval quality in the hatchery which appears to be due to poor broodstock performance. The present study determined the efficiency of two broodstock holding systems, the Recirculating Aquaculture System (RAS) with polychaete-assisted biofilter and the conventional system with regular water replacement or non-RAS (NRAS, control) based on survival rate, spawning, and hatching of Scylla serrata captive broodstock. The percentage survival of mangrove crab broodstock was similar in the RAS (54.00 ± 10.00) and NRAS (50.84 ± 3.89) systems. However, significantly higher mean percentage of spawning (28.61 ± 6.65) and hatching (28.61 ± 6.65) was observed in the RAS compared to the NRAS (15.18 ± 7.41 and 15.18 ± 7.41 respectively). Temperature levels were recorded in the RAS, ranging from 27.7–29.3°C compared to 26.7–28.5°C in the NRAS. Salinity in the RAS ranged from 33.7–35.2 ppt, while in the NRAS it ranged from 33.5–34.7 ppt. Dissolved oxygen (DO) concentrations ranged from 5.8–7.3 mg/L in RAS and 5.5–7.3 in NRAS. Additionally, pH values in the RAS ranged from 7.8–8.6, compared to 7.8–8.5 in the non-RAS. Moreover, ammonia levels were significantly lower in the RAS, ranging from 0.11–0.25 mg/L, compared to 0.31–1.18 mg/L in the NRAS. Similarly, nitrite concentrations were significantly lower in the RAS (0.001–0.007 mg/L) than in the NRAS (0.013–0.026 mg/L). The present study suggests that RAS with polychaete-assisted biofilter offers a less stressful environment thereby enhancing the reproductive performance of Scylla serrata broodstock in captivity.
Leisure Satisfaction as a Predictor of Job Satisfaction and Performance Among Male Police Officers
Authors: Akçakese Andaç , Tükel Yalçın , Demirel Mehmet , Çalış Murat 
Published: November 2024

Abstract

This study investigates the correlation between leisure satisfaction and job performance among male police officers in Turkey. Utilizing a cross-sectional survey design, it targets 352 male police officers with a minimum of seven years of professional experience. It employs the Leisure Satisfaction Scale (LSS), Job Satisfaction Scale, and Job Performance Scale to measure physical and psychological dimensions of leisure satisfaction, job satisfaction, and job performance. The findings reveal that both physiological and psychological leisure satisfaction significantly influence job satisfaction and performance. Specifically, physiological leisure satisfaction is a strong predictor of job satisfaction, while both types of leisure satisfaction contribute positively to job performance. These results underscore the importance of promoting diverse leisure activities within law enforcement agencies to enhance officers' well-being and job effectiveness. This study recommends implementing policies that encourage participation in varied leisure activities to improve job satisfaction and performance, ultimately leading to better policing outcomes and enhanced community service.
Female UK leaders and mentoring and the underrepresentation of women in leadership roles in the UK financial sector.
Authors: Babafemi Elizabeth 
Published: November 2024

Abstract

Background: Despite the efforts of governments and organizations, there remains a severe problem with the underrepresentation of women in UK finance. There are several structural barriers to female career progression in the sector. Mentoring is among the strategies that may help females secure more senior roles. This study examines if the current practice of mentoring is beneficial to women. It is a qualitative study based on the views of current female finance leaders who have been both mentors and mentees.
Methodology: This qualitative study used semi-structured interviews to collect primary data from a sample of 14 women. The primary data was analyzed using thematic analysis.
Findings. The findings included that any type of mentoring was beneficial and that both males and females could successfully support women who aspired to financial leadership roles. However, some women could not secure mentors. There was a perceived shortage of mentors in some sectors and a need for access to this support. No preference was found for male or female mentors.
Discussions: The study found that women were in favour of mentoring. However, mentoring was limited and inflexible. Many women needed the support they needed, and the provision of mentoring needed to be uniform in UK finance. Many female mentors focused on performances that unintentionally reinforced barriers to women. Moreover, mentors expected females to accept male working styles and practices that many would find unacceptable.
Conclusion: Mentoring was found to be important and helped women advance their careers. However, the provision of this support and the way it was provided were not helpful enough for women and perpetuated their representation in UK Finance. The study recommends further research and changes to workplace practices.