bluuebunny/crossref_metadata_2025_split huggingface dataset:
Narcissism predicts noise perception but not signal decoding in emotion
Authors: Czarna Anna Z. , Mauersberger Heidi , Kastendieck Till , Zdunek Roksana R. , Sedikides Constantine , Hess Ursula 
Published: September 2023

AbstractGrandiose narcissists claim that they have better-than-average emotion recognition abilities, but many objective tests do not support this claim. We sought to clarify the relation between grandiose (both agentic and communal) narcissism and emotion recognition by taking a closer look at the components of emotion recognition. In two studies (N1 = 147, N2 = 520), using culturally distinct samples and different stimulus materials, we investigated the relation between grandiose narcissism and signal decoding (accurate view of the intended emotion displayed in an expression) as well as noise perception (inaccurate deciphering of secondary emotions that are not part of the emotional message). Narcissism was inconsistently related to signal decoding, but consistently and positively related to noise perception. High grandiose (agentic and communal) narcissists are not necessarily better at signal decoding, but are more susceptible to noise perception. We discuss implications for narcissists’ social interactions and interpersonal relationships.
Deploying efficient net batch normalizations (BNs) for grading diabetic retinopathy severity levels from fundus images
Authors: Batool Summiya , Gilani Syed Omer , Waris Asim , Iqbal Khawaja Fahad , Khan Niaz B. , Khan M. Ijaz , Eldin Sayed M. , Awwad Fuad A. 
Published: September 2023

AbstractDiabetic retinopathy (DR) is one of the main causes of blindness in people around the world. Early diagnosis and treatment of DR can be accomplished by organizing large regular screening programs. Still, it is difficult to spot diabetic retinopathy timely because the situation might not indicate signs in the primary stages of the disease. Due to a drastic increase in diabetic patients, there is an urgent need for efficient diabetic retinopathy detecting systems. Auto-encoders, sparse coding, and limited Boltzmann machines were used as a few past deep learning (DL) techniques and features for the classification of DR. Convolutional Neural Networks (CNN) have been identified as a promising solution for detecting and classifying DR. We employ the deep learning capabilities of efficient net batch normalization (BNs) pre-trained models to automatically acquire discriminative features from fundus images. However, we successfully achieved F1 scores above 80% on all efficient net BNs in the EYE-PACS dataset (calculated F1 score for DeepDRiD another dataset) and the results are better than previous studies. In this paper, we improved the accuracy and F1 score of the efficient net BNs pre-trained models on the EYE-PACS dataset by applying a Gaussian Smooth filter and data augmentation transforms. Using our proposed technique, we have achieved F1 scores of 84% and 87% for EYE-PACS and DeepDRiD.
Development and validation of nomograms to predict survival of primary adrenal lymphoma: a population-based retrospective study
Authors: Sun Shiwei , Wang Yue , Yao Wei , Yue Peng , Guo Fuyu , Deng Xiaoqian , Zhang Jiandong , Zhang Yangang 
Published: September 2023

AbstractWhile it is known that accurate evaluation of overall survival (OS) and disease-specific survival (DSS) for patients with primary adrenal lymphoma (PAL) can affect their prognosis, no stable and effective prediction model exists. This study aimed to develop prediction models to evaluate survival. This study enrolled 5448 patients with adrenal masses from the SEER Program. The influencing factors were selected using the least absolute shrinkage and selection operator regression model (LASSO) and Fine and Gray model (FGM). In addition, nomograms were constructed. Receiver operating characteristic curves and bootstrap self-sampling methods were used to verify the discrimination and consistency of the nomograms. The independent influencing factors for PAL survival were selected by LASSO and FGM, and three models were built: the OS, DSS, and FGS (DSS analysis by FGM) model. The areas under the curve and decision curve analyses indicated that the models were valid. This study developed survival prediction models to predict OS and DSS of patients with PAL. The FGS model was more accurate than the DSS model in the short term. Above all, these models should offer benefits to patients with PAL in terms of the treatment modality choice and survival evaluation.
Crowding alters F-actin secondary structure and hydration
Authors: Chen Xiaobing , Roeters Steven J. , Cavanna Francis , Alvarado José , Baiz Carlos R. 
Published: September 2023

AbstractActin, an important component of eukaryotic cell cytoskeleton, regulates cell shape and transport. The morphology and biochemical properties of actin filaments are determined by their structure and protein-protein contacts. Crowded environments can organize filaments into bundles, but less is known about how they affect F-actin structure. This study used 2D IR spectroscopy and spectral calculations to examine how crowding and bundling impact the secondary structure and local environments in filaments and weakly or strongly bundled networks. The results reveal that bundling induces changes in actin’s secondary structure, leading to a decrease in β-sheet and an increase in loop conformations. Strongly bundled networks exhibit a decrease in backbone solvent exposure, with less perturbed α-helices and nearly “locked” β-sheets. Similarly, the loops become less hydrated but maintain a dynamic environment. These findings highlight the role of loop structure in actin network morphology and stability under morphology control by PEG.
TimeTalk uses single-cell RNA-seq datasets to decipher cell-cell communication during early embryo development
Authors: Wang Longteng , Zheng Yang , Sun Yu , Mao Shulin , Li Hao , Bo Xiaochen , Li Cheng , Chen Hebing 
Published: September 2023

AbstractEarly embryonic development is a dynamic process that relies on proper cell-cell communication to form a correctly patterned embryo. Early embryo development-related ligand-receptor pairs (eLRs) have been shown to guide cell fate decisions and morphogenesis. However, the scope of eLRs and their influence on early embryo development remain elusive. Here, we developed a computational framework named TimeTalk from integrated public time-course mouse scRNA-seq datasets to decipher the secret of eLRs. Extensive validations and analyses were performed to ensure the involvement of identified eLRs in early embryo development. Process analysis identified that eLRs could be divided into six temporal windows corresponding to sequential events in the early embryo development process. With the interpolation strategy, TimeTalk is powerful in revealing paracrine settings and studying cell-cell communication during early embryo development. Furthermore, by using TimeTalk in the blastocyst and blastoid models, we found that the blastoid models share the core communication pathways with the epiblast and primitive endoderm lineages in the blastocysts. This result suggests that TimeTalk has transferability to other bio-dynamic processes. We also curated eLRs recognized by TimeTalk, which may provide valuable clues for understanding early embryo development and relevant disorders.
Genetic-tunneling driven energy optimizer for spin systems
Authors: Xu Qichen , Shen Zhuanglin , Pereiro Manuel , Sjöqvist Erik , Herman Pawel , Eriksson Olle , Delin Anna 
Published: September 2023

AbstractFinding the ground state of complex many-body systems, such as magnetic materials containing topological textures, like skyrmions, is a fundamental and long-standing problem. We present here a genetic-tunneling-driven variance-controlled optimization method, that efficiently identifies the ground state of two-dimensional skyrmionic systems. The approach combines a local energy-minimizer backend and a metaheuristic global search frontend. The method is shown to perform significantly better than simulated annealing. Specifically, we demonstrate that for the Pd/Fe/Ir(111) system, our method correctly and efficiently identifies the experimentally observed spin spiral geometry, skyrmion lattice and ferromagnetic ground states as a function of the external magnetic field. To our knowledge, no other optimization method has until now succeeded in doing this. We envision that our findings will pave the way for evolutionary computing in mapping out phase diagrams for spin systems in general.
Ptychographic X-ray computed tomography of porous membranes with nanoscale resolution
Authors: Górecki Radosław , Polo Carla Cristina , Kalile Tiago Araujo , Miqueles Eduardo X. S. , Tonin Yuri R. , Upadhyaya Lakshmeesha , Meneau Florian , Nunes Suzana P. 
Published: September 2023

AbstractNew visualization methods can be utilized to resolve structures at resolutions that were previously unachievable. 3D images of porous materials with high resolution have been so far obtained using transmission electron tomography or focused ion beam coupled with scanning electron microscopy. For these methods, ultra-vacuum is required, and only a small volume of the sample is visualized. Here, we demonstrate the application of ptychographic X-ray computed tomography for the visualization of soft matter with a resolution of 26 nm over large fields of view. Thanks to the high-penetration depth of the X-ray beam, we visualize the 3D complex porous structure of polyetherimide hollow fibers in a non-destructive manner and obtain quantitative information about pore size distribution and pore network interconnectivity across the whole membrane wall. The non-destructive nature of this method, coupled with its ability to image samples without requiring modification or a high vacuum environment, makes it valuable in the fields of porous- and nano-material sciences enabling imaging under different environmental conditions.
Top-down structuring of freshwater bacterial communities by mixotrophic flagellates
Authors: Ivanković Marina , Ptacnik Robert , Bengtsson Mia Maria 
Published: September 2023

Abstract
Mixotrophic and heterotrophic protists hold a key position in aquatic microbial food webs. Whereas they can account for the bulk of bacterivory in pelagic systems, the potential structuring effect of these consumers on bacterial communities is far from clear. We conducted short-term grazing experiments to test for the overall impact on bacterial community structure and possible prey preferences of phagotrophic protists. The protist taxa selected for this study include three mixotrophic flagellates, comprising two obligate- and one facultative mixotroph, and one phagoheterotrophic flagellate lacking phototrophic capacity. Bacterioplankton from seven different lakes were enriched and used to represent semi-natural prey communities. Our study demonstrated protist strain specific impacts on bacterial community composition linked to grazing. The three mixotrophs had variable impacts on bacterial communities where the two obligate mixotrophs exhibited lower grazing rates, while showing a tendency to promote higher bacterial diversity. The phagoheterotroph displayed the highest grazing rates and structured the bacterial communities via apparent selective grazing. Consistent selectivity trends were observed throughout the experiments, such as the apparent avoidance of all flagellates of Actinobacteria, and high grazing on dominant Burkholderiales taxa. However, there was no consistent “fingerprint” of mixotrophic grazing on prey communities, but the structuring impact rather seemed to depend on the trophic mode of the individual protist taxa, i.e. their dependence on phototrophy vs. phagotrophy. Our findings highlight the differential structuring impact of protist taxa on bacterial communities which may have important ecological implications, for example during periodic dominance of obligate mixotrophic bacterivores in changing lake ecosystems.
Top abundant deep ocean heterotrophic bacteria can be retrieved by cultivation
Authors: Sanz-Sáez Isabel , Sánchez Pablo , Salazar Guillem , Sunagawa Shinichi , de Vargas Colomban , Bowler Chris , Sullivan Matthew B , Wincker Patrick , Karsenti Eric , Pedrós-Alió Carlos , Agustí Susana , Gojobori Takashi , Duarte Carlos M , Gasol Josep M , Sánchez Olga , Acinas Silvia G 
Published: September 2023

Abstract
Traditional culture techniques usually retrieve a small fraction of the marine microbial diversity, which mainly belong to the so-called rare biosphere. However, this paradigm has not been fully tested at a broad scale, especially in the deep ocean. Here, we examined the fraction of heterotrophic bacterial communities in photic and deep ocean layers that could be recovered by culture-dependent techniques at a large scale. We compared 16S rRNA gene sequences from a collection of 2003 cultured heterotrophic marine bacteria with global 16S rRNA metabarcoding datasets (16S TAGs) covering surface, mesopelagic and bathypelagic ocean samples that included 16 of the 23 samples used for isolation. These global datasets represent 60 322 unique 16S amplicon sequence variants (ASVs). Our results reveal a significantly higher proportion of isolates identical to ASVs in deeper ocean layers reaching up to 28% of the 16S TAGs of the bathypelagic microbial communities, which included the isolation of 3 of the top 10 most abundant 16S ASVs in the global bathypelagic ocean, related to the genera Sulfitobacter, Halomonas and Erythrobacter. These isolates contributed differently to the prokaryotic communities across different plankton size fractions, recruiting between 38% in the free-living fraction (0.2–0.8 µm) and up to 45% in the largest particles (20–200 µm) in the bathypelagic ocean. Our findings support the hypothesis that sinking particles in the bathypelagic act as resource-rich habitats, suitable for the growth of heterotrophic bacteria with a copiotroph lifestyle that can be cultured, and that these cultivable bacteria can also thrive as free-living bacteria.
Phagotrophic protists preserve antibiotic-resistant opportunistic human pathogens in the vegetable phyllosphere
Authors: Lin Chenshuo , Li Li-Juan , Ren Kexin , Zhou Shu-Yi-Dan , Isabwe Alain , Yang Le-Yang , Neilson Roy , Yang Xiao-Ru , Cytryn Eddie , Zhu Yong-Guan 
Published: September 2023

Abstract
Food safety of leafy greens is an emerging public health issue as they can harbor opportunistic human pathogens (OHPs) and expose OHPs to consumers. Protists are an integral part of phyllosphere microbial ecosystems. However, our understanding of protist-pathogen associations in the phyllosphere and their consequences on public health remains poor. Here, we examined phyllosphere protists, human pathogen marker genes (HPMGs), and protist endosymbionts from four species of leafy greens from major supermarkets in Xiamen, China. Our results showed that Staphylococcus aureus and Klebsiella pneumoniae were the dominant human pathogens in the vegetable phyllosphere. The distribution of HPMGs and protistan communities differed between vegetable species, of which Chinese chive possessed the most diverse protists and highest abundance of HPMGs. HPMGs abundance positively correlated with the diversity and relative abundance of phagotrophic protists. Whole genome sequencing further uncovered that most isolated phyllosphere protists harbored multiple OHPs which carried antibiotic resistance genes, virulence factors, and metal resistance genes and had the potential to HGT. Colpoda were identified as key phagotrophic protists which positively linked to OHPs and carried diverse resistance and virulence potential endosymbiont OHPs including Pseudomonas nitroreducens, Achromobacter xylosoxidans, and Stenotrophomonas maltophilia. We highlight that phyllosphere protists contribute to the transmission of resistant OHPs through internalization and thus pose risks to the food safety of leafy greens and human health. Our study provides insights into the protist-OHP interactions in the phyllosphere, which will help in food safety surveillance and human health.
Infected wound repair with an ultrasound-enhanced nanozyme hydrogel scaffold
Authors: Zhang Fan , Kang Yong , Feng Liwen , Xi Guan , Chen Wei , Kong Na , Tao Wei , Luan Tiangang , Koo Seyoung , Ji Xiaoyuan 
Published: September 2023

An all-round therapeutic strategy based on high-density platinum nanoparticle assemblies nanozymes was developed to simultaneously scavenge ROS, generate O2 and regulate the immune system for accelerating diabetic wound healing.
Morphology and gene expression of sex‐determining region Y‐box 9 and runt‐related transcription factor 2 in centrifugally compressed cell collagen‐combined constructs (C6)
Authors: Ye Xu , Yamazaki Masashi , Ando Wataru , Nakamura Norimasa , Fujie Hiromichi 
Published: September 2023

AbstractThe authors previously developed a scaffold‐free tissue‐engineered construct (TEC) from mesenchymal stem cells (MSCs). Although the TEC exhibited even cell distribution and was successfully applied for cartilage repair in animal models, it is unsuitable for relatively large‐scale cartilage defects due to its small size. To solve the problem, the authors recently developed a novel biomaterial, a centrifugally compressed cell‐collagen combined construct (C6) from a mixture of MSCs and atelocollagen, both of which are subjected to centrifugation. The results of the previous study indicated that C6 exhibited high cell viability (70 %) and sufficient cell distribution similar to that of the TEC. In the present study, the morphology and gene expression of C6 were investigated. Histological examination indicated that C6 is six times thicker (approximately 1 mm) than the TEC after a 7‐day culture. The C6 remained unchanged in scale with increased cell density after a 21‐day culture. Scanning electron microscopic observation indicated that C6 exhibited interconnected and porous microstructures, while the TEC had close‐knit microstructures. Reverse transcriptase‐polymerase chain reaction analysis indicated that the expression of sex‐determining region Y‐box 9 and runt‐related transcription factor 2 was significantly higher in C6 than that in TEC.
Unleashing the power of generative adversarial networks: A novel machine learning approach for vehicle detection and localisation in the dark
Authors: Hassan Onim Md Saif , Nyeem Hussain , Khan Arnob Md. Wahiduzzaman , Pooja Arunima Dey 
Published: September 2023

AbstractMachine vision in low‐light conditions is a critical requirement for object detection in road transportation, particularly for assisted and autonomous driving scenarios. Existing vision‐based techniques are limited to daylight traffic scenarios due to their reliance on adequate lighting and high frame rates. This paper presents a novel approach to tackle this problem by investigating Vehicle Detection and Localisation (VDL) in extremely low‐light conditions by using a new machine learning model. Specifically, the proposed model employs two customised generative adversarial networks, based on Pix2PixGAN and CycleGAN, to enhance dark images for input into a YOLOv4‐based VDL algorithm. The model's performance is thoroughly analysed and compared against the prominent models. Our findings validate that the proposed model detects and localises vehicles accurately in extremely dark images, with an additional run‐time of approximately 11 ms and an accuracy improvement of 10%–50% compared to the other models. Moreover, our model demonstrates a 4%–8% increase in Intersection over Union (IoU) at a mean frame rate of 9 fps, which underscores its potential for broader applications in ubiquitous road‐object detection. The results demonstrate the significance of the proposed model as an early step to overcoming the challenges of low‐light vision in road‐object detection and autonomous driving, paving the way for safer and more efficient transportation systems.
Deep Q‐learning recommender algorithm with update policy for a real steam turbine system
Authors: Modirrousta Mohammad Hossein , Aliyari Shoorehdeli Mahdi , Yari Mostafa , Ghahremani Arash 
Published: September 2023

AbstractIn modern industrial systems, diagnosing faults in time and using the best methods becomes increasingly crucial. It is possible to fail a system or to waste resources if faults are not detected or are detected late. Machine learning and deep learning (DL) have proposed various methods for data‐based fault diagnosis, and the authors are looking for the most reliable and practical ones. A framework based on DL and reinforcement learning (RL) is developed for fault detection. The authors have utilised two algorithms in their work: Q‐Learning and Soft Q‐Learning. Reinforcement learning frameworks frequently include efficient algorithms for policy updates, including Q‐learning. These algorithms optimise the policy based on the predictions and rewards, resulting in more efficient updates and quicker convergence. The authors can increase accuracy, overcome data imbalance, and better predict future defects by updating the RL policy when new data is received. By applying their method, an increase of 3%–4% in all evaluation metrics by updating policy, an improvement in prediction speed, and an increase of 3%–6% in all evaluation metrics compared to a typical backpropagation multi‐layer neural network prediction with comparable parameters is observed. In addition, the Soft Q‐learning algorithm yields better outcomes compared to Q‐learning.
Electric vehicle dispatching strategy considering time cost and risk of operating distribution network
Authors: Wang He , Leng Xianda , Liang Zhifeng , Huo Xuesong , Yu Ruoying , Bian Jing 
Published: September 2023

AbstractElectric vehicles (EVs) are increasingly being valued by countries, but the disorderly charging behaviour of too many EVs poses a huge challenge to the operation of the power grid. First, for EVs, a methodical charging and discharging technique was designed, taking into account the temporal and spatiotemporal characteristics of different EV models, time convenience of owners, and safe operation of power grid. Second, the EV characteristics and safe operation of the grid after EV integration into the grid are presented for vehicle owners to achieve minimal charging station and optimal charging stations selection as well as charging and discharging schemes. Third, simulation calculations and analyses of ordered charging and discharging modes as well as disordered charging modes under various scenarios were performed. This study fully considers the characteristics of different vehicle models and the willingness of users to respond, making the model more realistic. The findings demonstrate that the optimised charging and discharging strategy in this study lowers the cost of charging for vehicle owners, boosts revenue from the charging station and the rate of use of the charging pile, lowers the risk of the safe operation of distribution networks, and effectively relieves pressure on the power grid. While solving the scheduling difficulties of a large number of EVs, it increases the economy between users and the power grid, and improves the safety of power grid operation.