bluuebunny/crossref_metadata_2025_split huggingface dataset:
Emerging challenges and opportunities in innovating food science technology and engineering education
Authors: Saguy I. S. , Silva C. L. M. , Cohen E. 
Published: January 2024

AbstractProgress in science, technology, innovation, and digital capabilities call for reassessing food science, technology, and engineering (FST&E) education and research programs. This survey targeted global professionals and students across food disciplines and nutrition. Its main objectives included assessing the status of FST&E higher education, identifying challenges and opportunities, and furnishing recommendations. Seven topics affecting the future of the FST&E curricula were evaluated by the panel as ‘High’ to ‘Very high’, namely: ‘Critical thinking’, followed by ‘Problem-solving projects’, ‘Teamwork/collaboration’, ‘Innovation/Open innovation’ and ‘Multidisciplinary’. The importance of academic partnership/collaboration with the Food Industry and Nutrition Sciences was demonstrated. Significant positive roles of the food industry in collaboration and partnerships were found. Other essential food industry attributes were related to internships, education, strategy, and vision. Collaboration between FST&E and nutrition sciences indicated the high standing of this direction. The need to integrate or converge nutrition sciences and FST&E is emphasized, especially with the growing consumer awareness of health and wellness. The study provides insights into new education and learning opportunities and new topics for future curricula.
GINv2.0: a comprehensive topological network integrating molecular interactions from multiple knowledge bases
Authors: Chang Xiao , Yan Shen , Zhang Yizheng , Zhang Yingchun , Li Luyang , Gao Zhanyu , Lin Xuefei , Chi Xu 
Published: January 2024

AbstractKnowledge bases have been instrumental in advancing biological research, facilitating pathway analysis and data visualization, which are now widely employed in the scientific community. Despite the establishment of several prominent knowledge bases focusing on signaling, metabolic networks, or both, integrating these networks into a unified topological network has proven to be challenging. The intricacy of molecular interactions and the diverse formats employed to store and display them contribute to the complexity of this task. In a prior study, we addressed this challenge by introducing a “meta-pathway” structure that integrated the advantages of the Simple Interaction Format (SIF) while accommodating reaction information. Nevertheless, the earlier Global Integrative Network (GIN) was limited to reliance on KEGG alone. Here, we present GIN version 2.0, which incorporates human molecular interaction data from ten distinct knowledge bases, including KEGG, Reactome, and HumanCyc, among others. We standardized the data structure, gene IDs, and chemical IDs, and conducted a comprehensive analysis of the consistency among the ten knowledge bases before combining all unified interactions into GINv2.0. Utilizing GINv2.0, we investigated the glycolysis process and its regulatory proteins, revealing coordinated regulations on glycolysis and autophagy, particularly under glucose starvation. The expanded scope and enhanced capabilities of GINv2.0 provide a valuable resource for comprehensive systems-level analyses in the field of biological research. GINv2.0 can be accessed at: https://github.com/BIGchix/GINv2.0.
High-resolution temporal gravity field data products: Monthly mass grids and spherical harmonics from 1994 to 2021
Authors: Uz Metehan , Akyılmaz Orhan , Shum C. K. , Atman Kazım Gökhan , Olgun Sevda , Güneş Özge 
Published: January 2024

AbstractSince April 2002, Gravity Recovery and Climate Experiment (GRACE) and GRACE-FO (FollowOn) satellite gravimetry missions have provided precious data for monitoring mass variations within the hydrosphere, cryosphere, and oceans with unprecedented accuracy and resolution. However, the long-term products of mass variations prior to GRACE-era may allow for a better understanding of spatio-temporal changes in climate-induced geophysical phenomena, e.g., terrestrial water cycle, ice sheet and glacier mass balance, sea level change and ocean bottom pressure (OBP). Here, climate-driven mass anomalies are simulated globally at 1.0° × 1.0° spatial and monthly temporal resolutions from January 1994 to January 2021 using an in-house developed hybrid Deep Learning architecture considering GRACE/-FO mascon and SLR-inferred gravimetry, ECMWF Reanalysis-5 data, and normalized time tag information as training datasets. Internally, we consider mathematical metrics such as RMSE, NSE and comparisons to previous studies, and externally, we compare our simulations to GRACE-independent datasets such as El-Nino and La-Nina indexes, Global Mean Sea Level, Earth Orientation Parameters-derived low-degree spherical harmonic coefficients, and in-situ OBP measurements for validation.
Chromosome-level assembly and gene annotation of Decapterus maruadsi genome using Nanopore and Hi-C technologies
Authors: Chen Longyu , Zhou Zhixiong , Zhou Zhiyin , Yang Junyi , Deng Yacheng , Bai Yulin , Pu Fei , Zhou Tao , Xu Peng 
Published: January 2024

AbstractDecapterus maruadsi is one of the representative offshore fish in the Western Pacific. Since the last century, it has become a commercially valuable marine fishery species in the Western Pacific region. Despite its high economic value, there is still a lack of high-quality reference genome of D. maruadsi in germplasm resource evaluation research. Here we report a chromosome-level reference genome of D. maruadsi based on Nanopore sequencing and Hi-C technologies. The whole genome was assembled through 169 contigs with a total length of 723.69 Mb and a contig N50 length of 24.67 Mb. By chromosome scaffolding, 23 chromosomes with a total length of 713.58 Mb were constructed. In addition, a total of 199.49 Mb repetitive elements, 33,515 protein-coding genes, and 6,431 ncRNAs were annotated in the reference genome. This reference genome of D. maruadsi will provide a solid theoretical basis not only for the subsequent development of genomic resources of D. maruadsi but also for the formulation of policies related to the protection of D. maruadsi.
Varus placement of the tibial component reduces the potential risk of fracture with adequate bony coverage in the Oxford unicompartmental knee arthroplasty
Authors: Kamenaga Tomoyuki , Hiranaka Takafumi , Suda Yoshihito , Fujishiro Takaaki , Okamoto Koji , Kuroda Ryosuke , Matsumoto Tomoyuki 
Published: January 2024

AbstractA short keel-cortex distance (KCD), especially to the posterior cortex, is a potential risk factor for tibial plateau fracture after Oxford mobile-bearing unicompartmental knee arthroplasty (OUKA). This study aimed to evaluate the effect of tibial component alignment in the coronal plane and tibial proximal morphology on the KCD. Included in this study were 51 patients scheduled for primary Oxford medial unicompartmental knee arthroplasty (UKA). The anterior and posterior KCD were preoperatively assessed using 3D simulation software with the component set perpendicular to the tibial mechanical axis (neutral), 3° valgus, 3° varus, and 6° varus, relative to neutral alignment. We evaluated the existence of overhanging medial tibial condyle where the medial eminence line, the line including the medial tibial eminence parallel to the tibial axis, passes outside of the tibial shaft. In all component alignments, patients with a medial overhanging condyle had significantly shorter posterior KCD than those without. In patients with a medial overhanging condyle, the posterior KCD significantly increased when the tibial component was placed in 3° varus (4.6 ± 1.5 mm, P = 0.003 vs neutral, P < 0.001 vs 3° valgus) and 6° varus (5.0 ± 1.4 mm, P < 0.001 vs neutral, P < 0.001 vs 3° valgus) compared with in neutral (3.5 ± 1.9 mm) or 3° valgus (2.8 ± 1.8 mm). In OUKA, varus implantation increased the KCD. This could potentially decrease the risk of fracture, even in knees with the overhanging medial condyle. Conversely, valgus implantation of the tibial component shortened the KCD, and should therefore be avoided.
Predicting memorability of face photographs with deep neural networks
Authors: Younesi Mohammad , Mohsenzadeh Yalda 
Published: January 2024

AbstractWith the advent of social media in our daily life, we are exposed to a plethora of images, particularly face photographs, every day. Recent behavioural studies have shown that some of these photographs stick in the mind better than others. Previous research have shown that memorability is an intrinsic property of an image, hence the memorability of an image can be computed from that image. Moreover, various works found that the memorability of an image is highly consistent across people and also over time. Recently, researchers employed deep neural networks to predict image memorability. Here, we show although those models perform well on scene and object images, they perform poorly on photographs of human faces. We demonstrate and explain why generic memorability models do not result in an acceptable performance on face photographs and propose seven different models to estimate the memorability of face images. In addition, we show that these models outperform the previous classical methods, which were used for predicting face memorability.
Open set classification of sound event
Authors: You Jie , Wu Wenqin , Lee Joonwhoan 
Published: January 2024

AbstractSound is one of the primary forms of sensory information that we use to perceive our surroundings. Usually, a sound event is a sequence of an audio clip obtained from an action. The action can be rhythm patterns, music genre, people speaking for a few seconds, etc. The sound event classification address distinguishes what kind of audio clip it is from the given audio sequence. Nowadays, it is a common issue to solve in the following pipeline: audio pre-processing→perceptual feature extraction→classification algorithm. In this paper, we improve the traditional sound event classification algorithm to identify unknown sound events by using the deep learning method. The compact cluster structure in the feature space for known classes helps recognize unknown classes by allowing large room to locate unknown samples in the embedded feature space. Based on this concept, we applied center loss and supervised contrastive loss to optimize the model. The center loss tries to minimize the intra- class distance by pulling the embedded feature into the cluster center, while the contrastive loss disperses the inter-class features from one another. In addition, we explored the performance of self-supervised learning in detecting unknown sound events. The experimental results demonstrate that our proposed open-set sound event classification algorithm and self-supervised learning approach achieve sustained performance improvements in various datasets.
Patterned immobilization of polyoxometalate-loaded mesoporous silica particles via amine-ene Michael additions on alkene functionalized surfaces
Authors: Yang Bingquan , Picchetti Pierre , Wang Yangxin , Wang Wenjing , Seeger Christoph , Bozov Kliment , Malik Sharali , Mallach Dennis , Schäfer Andreas H. , Ibrahim Masooma , Hirtz Michael , Powell Annie K. 
Published: January 2024

AbstractPolyoxometalates (POM) are anionic oxoclusters of early transition metals that are of great interest for a variety of applications, including the development of sensors and catalysts. A crucial step in the use of POM in functional materials is the production of composites that can be further processed into complex materials, e.g. by printing on different substrates. In this work, we present an immobilization approach for POMs that involves two key processes: first, the stable encapsulation of POMs in the pores of mesoporous silica nanoparticles (MSPs) and, second, the formation of microstructured arrays with these POM-loaded nanoparticles. Specifically, we have developed a strategy that leads to water-stable, POM-loaded mesoporous silica that can be covalently linked to alkene-bearing surfaces by amine-Michael addition and patterned into microarrays by scanning probe lithography (SPL). The immobilization strategy presented facilitates the printing of hybrid POM-loaded nanomaterials onto different surfaces and provides a versatile method for the fabrication of POM-based composites. Importantly, POM-loaded MSPs are useful in applications such as microfluidic systems and sensors that require frequent washing. Overall, this method is a promising way to produce surface-printed POM arrays that can be used for a wide range of applications.
Broadening the capture of natural products mentioned in FAERS using fuzzy string-matching and a Siamese neural network
Authors: Dilán-Pantojas Israel O. , Boonchalermvichien Tanupat , Taneja Sanya B. , Li Xiaotong , Chapin Maryann R. , Karcher Sandra , Boyce Richard D. 
Published: January 2024

AbstractIncreased sales of natural products (NPs) in the US and growing safety concerns highlight the need for NP pharmacovigilance. A challenge for NP pharmacovigilance is ambiguity when referring to NPs in spontaneous reporting systems. We used a combination of fuzzy string-matching and a neural network to reduce this ambiguity. Our aim is to increase the capture of reports involving NPs in the US Food and Drug Administration Adverse Event Reporting System (FAERS). For this, we utilized Gestalt pattern-matching (GPM) and Siamese neural network (SM) to identify potential mentions of NPs of interest in 389,386 FAERS reports with unmapped drug names. A team of health professionals refined the candidates identified in the previous step through manual review and annotation. After candidate adjudication, GPM identified 595 unique NP names and SM 504. There was little overlap between candidates identified by each (Non-overlapping: GPM 347, SM 248). We identified a total of 686 novel NP names from FAERS reports. Including these names in the FAERS collection yielded 3,486 additional reports mentioning NPs.
Graph autoencoder with mirror temporal convolutional networks for traffic anomaly detection
Authors: Ren Zhiyu , Li Xiaojie , Peng Jing , Chen Ken , Tan Qushan , Wu Xi , Shi Canghong 
Published: January 2024

AbstractTraffic time series anomaly detection has been intensively studied for years because of its potential applications in intelligent transportation. However, classical traffic anomaly detection methods often overlook the evolving dynamic associations between road network nodes, which leads to challenges in capturing the long-term temporal correlations, spatial characteristics, and abnormal node behaviors in datasets with high periodicity and trends, such as morning peak travel periods. In this paper, we propose a mirror temporal graph autoencoder (MTGAE) framework to explore anomalies and capture unseen nodes and the spatiotemporal correlation between nodes in the traffic network. Specifically, we propose the mirror temporal convolutional module to enhance feature extraction capabilities and capture hidden node-to-node features in the traffic network. Morever, we propose the graph convolutional gate recurrent unit cell (GCGRU CELL) module. This module uses Gaussian kernel functions to map data into a high-dimensional space, and enables the identification of anomalous information and potential anomalies within the complex interdependencies of the traffic network, based on prior knowledge and input data. We compared our work with several other advanced deep-learning anomaly detection models. Experimental results on the NYC dataset illustrate that our model works best compared to other models for traffic anomaly detection.
Relationship between sleep quality and marital satisfaction of working women during the premenopausal period
Authors: Yadollahi P. , Mavaddatnia S. , Zarshenas M. , Ghaemmaghami P. 
Published: January 2024

AbstractSleep disorders can adversely affect physical, sexual, and marital health, particularly among middle-aged women. This study aimed to determine the relationship between sleep quality and marital satisfaction of working women during the premenopausal period. In this cross-sectional study, we selected 150 women working at Shiraz University of Medical Sciences in Iran was selected using random cluster sampling. A demographic information form, the Pittsburgh Sleep Quality Index (PSQI), and the Evaluation and Nurturing Relationship Issues, Communication, and Happiness (ENRICH) marital satisfaction scale were used for data collection. The Data were analyzed using SPSS.22 software at a significance level of P < 0.05. Multiple linear regression analysis was employed to predict sleep quality based on marital satisfaction. Our results showed that 79 (52.7%) of the participants had undesirable sleep quality, 87 (58%) had high marital satisfaction, and 32 (21.3%) had very high marital satisfaction. Regression analysis revealed that the total marital satisfaction score could not predict the sleep quality score of the participants. However, as dimensions of marital satisfaction, personality issues negatively (β = 0.327, P < 0.05) and ideological orientation positively (β = 0.336, P < 0.01) predicted the sleep quality score. Based on the prediction of the sleep quality score by personality issues and ideological orientations among the dimensions of marital satisfaction, it seems that life skills training, especially in these two dimensions, may improve the quality of sleep and, as a result, the physical and mental health of working women.
Socioeconomic inequalities in early adulthood disrupt the immune transcriptomic landscape via upstream regulators
Authors: Ravi Sudharshan , Shanahan Michael J. , Levitt Brandt , Harris Kathleen Mullan , Cole Steven W. 
Published: January 2024

AbstractDisparities in socio-economic status (SES) predict many immune system-related diseases, and previous research documents relationships between SES and the immune cell transcriptome. Drawing on a bioinformatically-informed network approach, we situate these findings in a broader molecular framework by examining the upstream regulators of SES-associated transcriptional alterations. Data come from the National Longitudinal Study of Adolescent to Adult Health (Add Health), a nationally representative sample of 4543 adults in the United States. Results reveal a network—of differentially expressed genes, transcription factors, and protein neighbors of transcription factors—that shows widespread SES-related dysregulation of the immune system. Mediational models suggest that body mass index (BMI) plays a key role in accounting for many of these associations. Overall, the results reveal the central role of upstream regulators in socioeconomic differences in the molecular basis of immunity, which propagate to increase risk of chronic health conditions in later-life.
New strategies for sterilization and preservation of fresh fish skin grafts
Authors: Ibrahim Ahmed , Fahmy Hossam M. , Mahmoud Ghada Abd-Elmonsef , Soliman Mahmoud , Elshahawy Abdelnaby M. 
Published: January 2024

AbstractThe introduction of fish skin as a biological dressing for treating burns and wounds holds great promise, offering an alternative to existing management strategies. However, the risk of disease transmission is a significant concern. Therefore, this study aimed to examine how established sterilization and preservation procedures affected fish skin grafts' microbiological and histological properties for long-term usage. Lyophilization of the fish skin graft followed by rehydration in normal saline for 15 min did not change the collagen content. Furthermore, gamma irradiation of the lyophilized fish skin graft at different lengths 5, 10, and 25 KGy showed a significant reduction in microbial growth (aerobic bacteria, aerobic yeasts, and fungi) at 15- and 30 days after the irradiation. However, exposure to 10 KGy was found to be the most effective intensity among the different gamma irradiation lengths since it preserved the collagen fiber content and intensity in the lyophilized fish skin grafts at 15- and 30 days after the irradiation. These findings provide efficient preservation and sterilization methods for long-term usage of the fresh Tilapia skin grafts used for biological dressings.
Skin autofluorescence, reflecting accumulation of advanced glycation end products, and the risk of dementia in a population-based cohort
Authors: Mooldijk Sanne S. , Lu Tianqi , Waqas Komal , Chen Jinluan , Vernooij Meike W. , Ikram M. Kamran , Zillikens M. Carola , Ikram M. Arfan 
Published: January 2024

AbstractConditions such as hyperglycemia and oxidative stress lead to the formation of advanced glycation end products (AGEs), which are harmful compounds that have been implicated in dementia. Within the Rotterdam Study, we measured skin AGEs as skin autofluorescence, reflecting long-term accumulation of AGEs, and determined their association with the risk of dementia and with brain magnetic resonance imaging (MRI) measures. Skin autofluorescence was measured between 2013 and 2016 in 2922 participants without dementia. Of these, 1504 also underwent brain MRI, on which measures of brain atrophy and cerebral small vessel disease were assessed. All participants were followed for the incidence of dementia until 2020. Of 2922 participants (mean age 72.6 years, 57% women), 123 developed dementia. Higher skin autofluorescence (per standard deviation) was associated with an increased risk of dementia (hazard ratio 1.21 [95% confidence interval 1.01–1.46]) and Alzheimer’s disease (1.19 [0.97–1.47]), independently of age and other studied potential confounders. Stronger effects were seen in apolipoprotein E (APOE) ε4 carriers (1.34 [0.98–1.82]) and in participants with diabetes (1.35 [0.94–1.94]). Participants with higher skin autofluorescence levels also had smaller total brain volumes and smaller hippocampus volumes on MRI, and they had more often lacunes. These results suggest that AGEs may be involved in dementia pathophysiology.
Design of a novel wide-angle Rotman lens beamformer for 5G mmWave applications
Authors: Azari Abolfazl , Skrivervik Anja , Aliakbarian Hadi 
Published: January 2024

AbstractThe attachment of 5G with millimeter wave (mmWave) frequencies offers massive capacity and low latency to reveal the full 5G experiences. High directive gain and beamforming are considered essential for mmWave 5G systems. The main requirements of the beamforming network for 5G mmWave applications are the scanning coverage of ± 60° and SLL > 10 dB in a wide operational bandwidth over the standard 5G frequency bands. In this paper, a novel PCB-based wide-angle Rotman lens beamformer is designed, simulated, and successfully measured to meet the mentioned requirements for 5G mmWave applications. A comprehensive improved design methodology is provided for all components of the Rotman lens to reach a wide scanning angle, enhanced sidelobe level, and low scan loss. The end-fire Vivaldi antenna is selected as an array element for easy integration to the beamforming network as well as its capability to use in dual-polarization configuration. The proposed Rotman lens is operational in the 24–30 GHz frequency band covering 5Gn257,n258, andn261frequency bands. The results show a nearly constant 8 beams across the whole bandwidth steering from − 53° to 53° in 15° increments to provide ± 60° coverage with the SLL > 10 dB and scan loss < 1.9 dB. The retrieved novelties from this work contain an effective design methodology for an optimized Rotman lens with wide-scan angle and low phase and amplitude error, non-uniform distribution based array ports, and integration with end-fire antenna for possible dual polarization and 2-D beamforming capabilities. The comparison of the proposed beamformer with the most recent works shows several advantages in terms of integrated structure and performances including bandwidth, wide scanning angle, SLL, and scan loss. With such performances, this beamformer can be used for various mmWave and 5G applications such as advanced antenna systems, massive MIMO systems, and hybrid beamforming systems.