bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deep learning-based postoperative visual acuity prediction in idiopathic epiretinal membrane
Authors: Wen Dejia , Yu Zihao , Yang Zhengwei , Zheng Chuanzhen , Shao Yan , Ren Xinjun , Gu Tianpu , Li Xiaorong 
Published: February 2023

Abstract
Background
To develop a deep learning (DL) model based on preoperative optical coherence tomography (OCT) training to automatically predict the 6-month postoperative visual outcomes in patients with idiopathic epiretinal membrane (iERM).
Methods
In this retrospective cohort study, a total of 442 eyes (5304 images in total) were enrolled for the development of the DL and multimodal deep fusion network (MDFN) models. All eyes were randomized into a training dataset with 265 eyes (60.0%), a validation dataset with 89 eyes (20.1%), and an external testing dataset with the remaining 88 eyes (19.9%). The input variables for prediction included macular OCT images and various clinical data. Inception-Resnet-v2 network was employed to estimate the 6-month postoperative best-corrected visual acuity (BCVA). The clinical data and OCT parameters were used to develop a regression model for predicting postoperative BCVA. The reliability of the models was further evaluated in the testing dataset.
Results
The prediction DL algorithm showed a mean absolute error (MAE) of 0.070 logMAR and root mean square error (RMSE) of 0.11 logMAR in the testing dataset. The DL model showed promising performance with R2 = 0.80, compared to R2 = 0.50 of the regression model. The percentages of BCVA prediction errors within ± 0.20 logMAR were 94.32% in the testing dataset.
Conclusions
The OCT-based DL model demonstrated sensitive and accurate predictive ability of postoperative BCVA in iERM patients. This novel DL model has great potential to be integrated into surgical planning.
Linear-ResNet GAN based anime style transfer of face images
Authors: Chen Mingxi , Dai Hansen , Wei Shijie , Hu Zhenzhen 
Published: February 2023

Abstract
Converting directly real-world images into high-quality anime styles using generative adversarial networks is one of the research hotspots in computer vision. The current popular AnimeGAN and WhiteBox anime generative adversarial networks have distortion of image features problem and loss of details on lines and textures problem, respectively. To address these problems, we introduce a new AnimationGAN based on a linear bottleneck residual network and a hybrid attention mechanism. The proposed AnimationGAN can prevent severe detail loss and distortion of image features in AnimeGAN-transferred images. In addition, we adopt optimized normalizations to improve the accuracy and learning rate of the model. The experimental results show that compared with AnimeGAN and Whitebox, the generated animation image significantly improved line texture details and image feature retention, and the network training speed is also faster.
Influence of Thickness and Surface Conditioning on Fracture Resistance of Occlusal Veneer
Authors: ESSAM NOHA , Soltan Hassan , Attia Ahmed 
Published: February 2023

Abstract
Background: The purpose of the current study was to assess the impact of restoration thickness, surface conditioning and the interaction between them on the fracture resistance of CAD/CAM fabricated lithium disilicate occlusal veneers.
Methods: A total of 42 maxillary molars were prepared to receive CAD/CAM fabricated lithium disilicate occlusal veneer either with 0.5 mm (n=21) or 1 mm (n=21) thickness. Each main group was divided into 3 subgroups (n=7), according to surface treatment, HF acid (HF-1, HF-0.5), acidulated phosphate fluoride (APF-1, APF-0.5) and Monobond etch & prime (MON-1, MON-0.5). Multilinik N (Ivoclar-Vivadent) adhesive resin cement was used for bonding according to the manufacturer instructions. One hour after bonding, specimens were stored in water bath for 75 days followed by cyclic loading fatigue for 240000 cycles to simulate clinical situation. Finally, specimens were fractured under compressive load in (N) using a universal testing machine. Two and one-way ANOVA and Post Hoc Tukey test were used for statistical analysis.
Results: The means±SD (N) fracture load for each group were calculated. MON-1 group showed the highest fracture load (1644.7±155.3) followed by HF-1 group (1514.6±212.5). Meanwhile, APF-0.5 showed the lowest fracture load (962±249.6).
Conclusion: CAD/CAM fabricated lithium disilicate occlusal veneers can be used with a thickness of 0.5 mm instead of conventional crowns. Monobond etch & prime is recommended as a surface treatment for CAD/CAM fabricated lithium disilicate occlusal veneer due to biological hazards of Hydrofluoric acid.
Response to Aerosolized Calfactant in Infants with Respiratory Distress Syndrome; A Post-hoc Analysis of AERO-02 trial
Authors: Kaluarachchi Dinushan , Zapata Henry , Becker Heather , Lasarev Michael , Fort Prem , Guthrie Scott 
Published: February 2023

Abstract
Background
The largest randomized control trial of aerosolized surfactant (AERO-02 trial) demonstrated a reduction in intubation by 50%.
Objective
To determine the response after aerosolized calfactant treatment.
Methods
We conducted a post-hoc analysis of moderate to late preterm neonates in the AERO-02 trial. Trends in hourly fraction of oxygen (FiO2), mean airway pressure (MAP) and respiratory severity score (RSS) were compared between the aerosolized surfactant (AS) and usual care (UC) groups.
Results
Forty-eight percent of subjects in the UC group required intubation compared to 24% in the AS group. FiO2, MAP and RSS were lower in the UC group. FiO2 decrease was seen after the first aerosolized calfactant dose.
Conclusion
FiO2, MAP and RSS were lower in the UC group. This is likely due to early and higher rate of liquid surfactant administration in the UC group. Decrease in FiO2 was noted in the AS group after the first aerosolization.
Activation of beta-adrenergic receptors upregulates the signal-to-noise ratio of auditory input in the medial prefrontal cortex and mediates auditory fear conditioning
Authors: Xie Haiting , Tian Yueqin , Li Zhongli , Wang Kaitao , Li Runtong , Yi Shang , Chen Aimin , Chen Jian , Liu Jun , Wei Xuhong , Gao Xiaoya 
Published: February 2023

Abstract
Background: Norepinephrine (NE) is involved in auditory fear conditioning (AFC) in posttraumatic stress disorder (PTSD). However, it is still unclear how it acts on neurons.
Objective: We aimed to investigate whether the activation of the β-adrenergic receptor (β-AR) improves AFC by sensitization of the prelimbic (PL) cortex at the animal, cellular and molecular levels.
Methods: In vivo single-cell electrophysiological recording was used to characterize the changes in neurons in the PL cortex after AFC. Then, PL neurons were measured under inhibition with the optogenetic method and local administration of the β-AR agonist isoprenaline (ISO) or the GABAaR agonist muscimol. Western blotting and immunohistochemistry were finally used to assess molecular changes.
Results: Noise and low-frequency tones induced similar AFC. The expression of β-ARs was upregulated in the PL cortex after fear conditioning. Microinjection of muscimol into the PL cortex blocked the conformation of AFC, whereas ISO injection facilitated AFC. Moreover, PL neurons can be distinguished into two types, with type I but not type II neurons responding to conditioned sound and being regulated by β-ARs.
Conclusion: Our results showed that β-ARs in the PL cortex regulate conditional fear learning by activating type I PL neurons.
An Fgfr2 Mutation as the Underlying Cause of Heritable Osteoporosis: a Case Report
Authors: Dantsev Ilya , Mariia Parfenenko , Gulnara Radzhabova , Ekaterina Nikolaeva 
Published: February 2023

Abstract
Osteoporosis is a systemic, multifactorial disorder of bone mineralization. Many factors contributing to the development of osteoporosis have been identified so far, including gender, age, nutrition, lifestyle, exercise, drug use, as well as a range of comorbidities. In addition to environmental and lifestyle factors, molecular-genetic factors account for 60–85% of osteoporosis cases. One of the candidate genes, the pathogenic variants in which are involved in the pathogenesis of osteoporosis is FGFR2. Recently, the key role in development of osteoporosis has been given to molecular-genetic factors. The vitamin D receptor (VDR), collagen type I (COL1), estrogen receptor (ER), apolypoprotein Е (ApoE), bone morphogenetic protein (BMP), and Low-density lipoprotein receptor-related protein 5 (LRP5) are all involved in the pathogenesis of osteoporosis. Additionally, FGFs/FGFRs-dependent signaling has been shown to regulate skeletal development and has been linked to a plethora of heritable disorders of the musculoskeletal system. In this study we present the clinical, biochemical and radiological findings, as well as results of molecular-genetic testing of a 13 year old male patient with heritable osteoporosis, arthralgia and multiple fractures and a family history of bone disease. Whole exome sequencing found a previously undescribed variant in the FGFR2 gene (GRCh37.p13 ENSG00000066468.16: g.123298133dup; ENST00000457416.2:c.722dup; ENSP00000351276.5:p.Asn241LysfsTer43). The same variant was found in affected relatives. These data leads us to believe that the variant in FGFR2 found in our patient and his relatives could be related to their phenotype. Therefore, modern methods of molecular genetic testing can allow us to differentiate between osteogenesis imperfecta and heritable forms of osteoporosis.
Optimization of landfill gas generation based on a modified first-order decay model: A case study in Quebec province
Authors: Malmir Tahereh , Lagos Daniel , Eicker Ursula 
Published: February 2023

Abstract
Landfills will likely remain an essential part of integrated solid waste management systems in many developed and developing countries for the foreseeable future. This paper uses a genetic algorithm to fit parameters to a CH4 and H2S generation model independently based on a modified first-order decay model. In the case of CH4 generation modeling, biodegradable organic waste (OW) was segregated into food waste, yard waste, paper, and wood. In addition to optimizing the OW fractions, key modeling parameters of OW, such as CH4 generation potential (\({L}_{0}\)) and CH4 decay rate (\({k}_{C{H}_{4}}\)), were determined independently for different periods in the life of the landfill. Similarly, in the case of H2S generation modeling, the construction and demolition waste (CD) was classified into fines (FCD) and bulky materials (BCD), and H2S generation potential (\({S}_{0}\)) and H2S decay rate (\({k}_{{H}_{2}S}\)) of FCD and BCD were determined. Landfill gas (LFG) collection data from a site in Quebec province (Canada) was used to validate the LFG generation model. A range of scenarios was analyzed using the validated model, including twelve scenarios for CH4 and two for H2S modeling, respectively. The results showed that the differentiation of more waste types improves the modeling accuracy for CH4. Moreover, within the decade-long lifetime of a landfill, the waste management strategies change, requiring different assumptions for the modeling. Also, the work showed the importance of considering how different sectors of a landfill are filled over time. Finally, scenario twelve, which assumed four waste types, constant three periodic waste fractions, and six sectors, had the lowest residual sum of squares (RSS) value. For H2S generation modeling, both scenarios, with or without separate fits of \({S}_{0}\) and \({k}_{{H}_{2}S}\) for FCD and BCD, predicted the generated H2S well and had a very similar RSS value. Further data could improve H2S generation modeling.
Combination of multiple dental anomalies in healthy adult: a rare case report
Authors: Shi Yingying , Zhang Ningning , Wang Xiaoli , Wang Junbin , Liu Jiajie , Si Qingzong 
Published: February 2023

Abstract
Purpose
We report a rare case of multiple retained primary teeth combine with impacted permanent teeth in healthy adult female. The diagnosis was confirmed by general oral examination and a cone-beam computed tomography (CBCT). The clinical significance of CBCT in the diagnosis and treatment of impacted teeth was also discussed.
Methods
Multiple retention of deciduous teeth and impacted permanent teeth were diagnosed by oral general examination combine with CBCT. In conjunction with the literature on impacted teeth, we discussed the diagnosis and the concept of treatment of this rare case.
Results
A rare case of primary teeth 53, 55, 63, 65, 74, 85 were retained; permanent teeth 13, 15, 17, 23, 25, 27, 34, 35, 45, 47 were impacted; permanent teeth 42 was congenital deletion; permanent teeth 24 and 44 were severely rotated, which were diagnosed by oral general examination combine with CBCT.
Conclusion
Multiple permanent teeth were impacted is a rare dental anomaly and often found in association with syndromes such as cleidocranial dysplasia or Gardner’s syndrome. However, we report an adult female with multiple impacted teeth who didn’t possess any systemic conditions or syndromes involving both jaws. Based on the oral general examination, auxiliary CBCT examination and literature on impacted teeth, this paper discusses the differential diagnosis and management of this case.
Similarity Evaluation of Graphic Design Based on Deep Visual Saliency Features
Authors: Liu Zhuohua , An Jingrui , Huang Caijuan , Yang Bin 
Published: February 2023

Abstract

The creativity of an excellent design work generally comes from the inspiration and innovation of its main visual features. The similarity between the main visual elements is the most important indicator for detecting plagiarism of design concepts, which is important to protect cultural heritage and copyright. The purpose of this paper is to develop an efficient similarity evaluation scheme for graphic design. A novel deep visual saliency feature extraction generative adversarial network is proposed to deal with the problem of lack of training examples. It consists of two networks: one predicts visual a saliency feature map from an input image; the other takes the output of the first to distinguish whether a visual saliency feature map is a predicted one or ground truth. Different from traditional saliency generative adversarial networks, a residual refinement module is connected after the encoding and decoding network. Design importance maps generated by professional designers are used to guide the network training. A saliency-based segmentation method is developed to not only locate the optimal layout regions but also notice insignificant regions. Priorities are then assigned to different visual elements. Experimental results show that the proposed model obtains state-of-the-art performance among various similarity measurement methods.
Design and Implementation of an Automatic Deep Stacked Sparsely Connected Auto-encoder (ADSSCA) Neural Network Architecture for Lithological Mapping under thick Vegetation using Remote Sensing
Authors: Otele Charlie Gael Atangana , Onabid Mathias Akong , Assembe Patrick Stephane 
Published: February 2023

Abstract
This article, proposes the design and implementation of a hybrid Deep Artificial Neural Network (DANN) to be used in mapping lithology using non-preprocessed Landsat images collected from tropical heterogeneous environments. In it, a sophisticated stacking of the hidden layers is performed through the introduction of an autoencoder topology where a wise variation of the dropout is defined at the encoder block to face the nonlinearity imposed by the limiting factors of such environments. The decoder however is left over without any dropout in order to ensure the reconstruction of the compressed data collected from the encoder. There is a relationships between the number of neurons per hidden layer of the encoder block, the number of hidden layers of the encoder, the dropout percentage suitable to better model the dataset. The resulting architecture which we call Automatic Deep Stacked Sparsely Connected Autoencoder (ADSSCA) is an optimized hybrid neural network architecture based on well formulated rules providing in advance, the definition of, the network topology, the total number of neurons and the number of hidden layers to be used in an extremely noisy environments. The implementation of the ADSSCA on raw landsat-8 images from an area of southern Cameroon produced an overall accuracy of 92.76%. In addition, five lithological classes where identified with similar individual accuracies.
The Preparation of PLA/PBAT/PPC Ternary Blends and The Effect of Epoxidized Soybean Oil on the Physical and Mechanical properties of PLA/PBAT/PPC blends with the Reactive Compatibilization
Authors: Choo Ji Eun , Park Tae Hyeong , Jeon Seon Mi , Hwang Sung Wook 
Published: February 2023

Abstract
Poly (lactic acid) (PLA)/poly (butylene adipate-co-terephthalate) (PBAT)/poly (propylene carbonate) (PPC) multi-phase blends were prepared by melt processing technique under the presence of compatibilizer with various composition. In our previous study, 70/30 wt% composition ratio of PLA/PBAT was found be optimized ratio to achieve the balanced properties of PLA/PBAT blends. The effect of compatibilizer on microstructure of PLA/PBAT/PPC multi-phase blends with/without epoxidized soybean oil (ESO) was investigated, and the structure-properties relationship were assessed. ​​The functional groups of PPC was found to effective to improve an interaction with carboxyl/hydroxyl group of PLA/PBAT binary blends to enhance the mechanical and physical properties on multi-phase blend system. The presence of PPC in PLA/PBAT blend affected the reduction of voids on the interface phase resulting in enhancing the oxygen barrier properties. With addition of ESO, the compatibility of ternary blend was found to be enhanced since the epoxy group of ESO reacted with the carboxyl/hydroxyl group of PLA, PBAT, and PPC, and under the condition with critical content of 4 phr of ESO, the elongation behavior dramatically increased as compared to that of blends without ESO while affecting reduction of oxygen barrier properties. The effect of ESO as a compatibilizer was clearly observed from the overall performances of ternary blends, and the potential feasibility of the PLA/PBAT/PPC ternary blends as packaging materials was confirmed at this study.
Deep Learning Techniques for Photovoltaic Solar Tracking Systems: A Systematic Literature Review
Authors: Phiri Musa , Mulenga Mwenge , Zimba Aaron , Eke Christopher Ifeanyi 
Published: February 2023

Abstract
Although photovoltaic (PV) panels are extensively used to convert solar energy into electric energy, the continuous change in the sun's angle with reference to the earth's surface limits their capacity to collect sufficient energy. To improve efficiency, solar trackers are used to constantly adjust the PV panels towards the sun to maximize energy capture. There has been an increase in the use of deep learning (DL) in solar tracking systems as it has proven to be one of the most efficient techniques owing to its success in related fields. Although there are several studies on different designs of solar tracking systems, a synthesis of the state-of-art knowledge is lacking in the literature. Therefore, this study carried out a review of the DL methods used in solar tracking systems. Specifically, dataset usage, preprocessing methods, feature engineering methods, DL algorithms and the performance metrics used in the identified studies. The review considered the studies published from 2012 to 2022. In the initial search, 5,724 articles were selected from 7 digital libraries. Only 37 academic papers were included in the review based on the inclusion criteria. The results revealed that deep hybrid learning models were the most popular among researchers. Further, the study identified research challenges and future directions relating to the availability of DL-based PV solar tracking with respect to datasets, image data, data normalization, data decomposition, and feature engineering methods. This work will be resourceful to current and future researchers in addressing the trends and challenges related to the application of DL in PV solar trackers.
Research trends in Coquerel’s sifaka (Propithecus coquereli): A bibliometric analysis
Authors: Suzzi-Simmons Amanda , Nelson Michael France 
Published: February 2023

Abstract
The Coquerel's sifaka (Propithecus coquereli) is one of the most critically endangered and least studied nonhuman primates in the world. Through a bibliometric analysis of the literature over the past 40-year period, this research aims to provide an investigation into the study of P. coquereli in terms of the research topics/issues investigated. Information on a series of variables (author, title, keywords, etc) was extracted after conducting an analysis of 89 articles, published in peer-reviewed journals between 1978–2022. The findings suggest that behavior, evolution, diversity, and conservation are the main topics of publications, which follows the general trends in ecology. In addition, the popularity of research on P. coquereli increased around 2005, coinciding with the release of the American computer-animated comedy, Madagascar, the island nation's classification as a megadiversity country, and the declaration that P. coquereli is not a subspecies of P. verreauxi. Further, a literature gap was identified that requires more exploration in the context of P. coquereli research: the effects of climate change on habitat disturbance, genetic diversity, and mortality. The compounded effects of habitat destruction and climate change exacerbate the devastating impacts on biodiversity loss and consequences to Madagascar’s wildlife, which is why it is especially important to study sifakas in the western dry deciduous forests.