bluuebunny/crossref_metadata_2025_split huggingface dataset:
Osteoinductive 3D printed scaffold healed 5 cm segmental bone defects in the ovine metatarsus
Authors: Yang Yunzhi Peter , Labus Kevin M. , Gadomski Benjamin C. , Bruyas Arnaud , Easley Jeremiah , Nelson Brad , Palmer Ross H. , McGilvray Kirk , Regan Daniel , Puttlitz Christian M. , Stahl Alexander , Lui Elaine , Li Jiannan , Moeinzadeh Seyedsina , Kim Sungwoo , Maloney William , Gardner Michael J. 
Published: March 2021

AbstractAutologous bone grafts are considered the gold standard grafting material for the treatment of nonunion, but in very large bone defects, traditional autograft alone is insufficient to induce repair. Recombinant human bone morphogenetic protein 2 (rhBMP-2) can stimulate bone regeneration and enhance the healing efficacy of bone grafts. The delivery of rhBMP-2 may even enable engineered synthetic scaffolds to be used in place of autologous bone grafts for the treatment of critical size defects, eliminating risks associated with autologous tissue harvest. We here demonstrate that an osteoinductive scaffold, fabricated by combining a 3D printed rigid polymer/ceramic composite scaffold with an rhBMP-2-eluting collagen sponge can treat extremely large-scale segmental defects in a pilot feasibility study using a new sheep metatarsus fracture model stabilized with an intramedullary nail. Bone regeneration after 24 weeks was evaluated by micro-computed tomography, mechanical testing, and histological characterization. Load-bearing cortical bridging was achieved in all animals, with increased bone volume observed in sheep that received osteoinductive scaffolds compared to sheep that received an rhBMP-2-eluting collagen sponge alone.
Comparison of trifocal or hybrid multifocal-extended depth of focus intraocular lenses: a systematic review and meta-analysis
Authors: Zhong Yueyang , Wang Kai , Yu Xiaoning , Liu Xin , Yao Ke 
Published: March 2021

AbstractThis meta-analysis aimed to evaluate the clinical outcomes following implantation of trifocal intraocular lenses (IOLs) or a hybrid multifocal-extended depth of focus (EDOF) IOL in cataract or refractive lens exchange surgeries. We examined 13 comparative studies with bilateral implantation of trifocal (898 eyes) or hybrid multifocal-EDOF (624 eyes) IOLs published through 1 March 2020. Better uncorrected and corrected near visual acuity (VA) were observed in the trifocal group (MD: − 0.143, 95% CI: − 0.192 to − 0.010, P < 0.001 and MD: − 0.149, 95% CI: − 0.217 to − 0.082, P < 0.001, respectively), while the hybrid multifocal-EDOF group presented better uncorrected intermediate VA (MD: 0.055, 95% CI: 0.016 to 0.093, P = 0.005). Trifocal IOLs were more likely to achieve spectacle independence at near distance (RR: 1.103, 95% CI: 1.036 to 1.152, P = 0.002). The halo photic effect was generated more frequently by the trifocal IOLs (RR: 1.318, 95% CI: 1.025 to 1.696, P = 0.031). Contrast sensitivity and subjective visual quality yielded comparable results between groups. Trifocal IOLs demonstrated better performance at near distance but apparently led to more photic disturbances. Our findings provided the most up-to-date and comprehensive evidence by comparing the benefits of advanced IOLs in clinical practice.
The size-weight illusion is unimpaired in individuals with a history of congenital visual deprivation
Authors: Pant Rashi , Guerreiro Maria J. S. , Ley Pia , Bottari Davide , Shareef Idris , Kekunnaya Ramesh , Röder Brigitte 
Published: March 2021

AbstractVisual deprivation in childhood can lead to lifelong impairments in multisensory processing. Here, the Size-Weight Illusion (SWI) was used to test whether visuo-haptic integration recovers after early visual deprivation. Normally sighted individuals perceive larger objects to be lighter than smaller objects of the same weight. In Experiment 1, individuals treated for dense bilateral congenital cataracts (who had no patterned visual experience at birth), individuals treated for developmental cataracts (who had patterned visual experience at birth, but were visually impaired), congenitally blind individuals and normally sighted individuals had to rate the weight of manually explored cubes that differed in size (Small, Medium, Large) across two possible weights (350 g, 700 g). In Experiment 2, individuals treated for dense bilateral congenital cataracts were compared to sighted individuals in a similar task using a string set-up, which removed haptic size cues. In both experiments, indistinguishable SWI effects were observed across all groups. These results provide evidence that early aberrant vision does not interfere with the development of the SWI, and suggest a recovery of the integration of size and weight cues provided by the visual and haptic modality.
A new fossil piddock (Bivalvia: Pholadidae) may indicate estuarine to freshwater environments near Cretaceous amber-producing forests in Myanmar
Authors: Bolotov Ivan N. , Aksenova Olga V. , Vikhrev Ilya V. , Konopleva Ekaterina S. , Chapurina Yulia E. , Kondakov Alexander V. 
Published: March 2021

AbstractThe lower Cenomanian Kachin amber from Myanmar contains a species-rich assemblage with numerous plant and animal fossils. Terrestrial and, to a lesser degree, freshwater species predominate in this assemblage, while a few taxa with marine affinities were also discovered, e.g. isopods, ammonites, and piddocks. Here, we describe the Kachin amber piddock †Palaeolignopholas kachinensis gen. & sp. nov. It appears to be an ancestral stem lineage of the recent Lignopholas piddocks, which are estuarine to freshwater bivalves, boring into wood and mudstone rocks. Frequent occurrences and high abundance of †Palaeolignopholas borings and preserved shells in the Kachin amber could indicate that the resin-producing forest was partly situated near a downstream (estuarine to freshwater) section of a river. Multiple records of freshwater invertebrates (caddisflies, mayflies, stoneflies, odonates, and chironomids) in this amber could also manifest in favor of our paleo-environmental reconstruction, although a variety of local freshwater environments is known to occur in coastal settings.
Coagulation factor IX analysis in bioreactor cell culture supernatant predicts quality of the purified product
Authors: Zacchi Lucia F. , Roche-Recinos Dinora , Pegg Cassandra L. , Phung Toan K. , Napoli Mark , Aitken Campbell , Sandford Vanessa , Mahler Stephen M. , Lee Yih Yean , Schulz Benjamin L. , Howard Christopher B. 
Published: March 2021

AbstractCoagulation factor IX (FIX) is a complex post-translationally modified human serum glycoprotein and high-value biopharmaceutical. The quality of recombinant FIX (rFIX), especially complete γ-carboxylation, is critical for rFIX clinical efficacy. Bioreactor operating conditions can impact rFIX production and post-translational modifications (PTMs). With the goal of optimizing rFIX production, we developed a suite of Data Independent Acquisition Mass Spectrometry (DIA-MS) proteomics methods and used these to investigate rFIX yield, γ-carboxylation, other PTMs, and host cell proteins during bioreactor culture and after purification. We detail the dynamics of site-specific PTM occupancy and structure on rFIX during production, which correlated with the efficiency of purification and the quality of the purified product. We identified new PTMs in rFIX near the GLA domain which could impact rFIX GLA-dependent purification and function. Our workflows are applicable to other biologics and expression systems, and should aid in the optimization and quality control of upstream and downstream bioprocesses.
CRISPR activation screen in mice identifies novel membrane proteins enhancing pulmonary metastatic colonisation
Authors: van der Weyden Louise , Harle Victoria , Turner Gemma , Offord Victoria , Iyer Vivek , Droop Alastair , Swiatkowska Agnieszka , Rabbie Roy , Campbell Andrew D. , Sansom Owen J. , Pardo Mercedes , Choudhary Jyoti S. , Ferreira Ingrid , Tullett Mark , Arends Mark J. , Speak Anneliese O. , Adams David J. 
Published: March 2021

AbstractMelanoma represents ~5% of all cutaneous malignancies, yet accounts for the majority of skin cancer deaths due to its propensity to metastasise. To develop new therapies, novel target molecules must to be identified and the accessibility of cell surface proteins makes them attractive targets. Using CRISPR activation technology, we screened a library of guide RNAs targeting membrane protein-encoding genes to identify cell surface molecules whose upregulation enhances the metastatic pulmonary colonisation capabilities of tumour cells in vivo. We show that upregulated expression of the cell surface protein LRRN4CL led to increased pulmonary metastases in mice. Critically, LRRN4CL expression was elevated in melanoma patient samples, with high expression levels correlating with decreased survival. Collectively, our findings uncover an unappreciated role for LRRN4CL in the outcome of melanoma patients and identifies a potential therapeutic target and biomarker.
Interaction of clinical-stage antibodies with heme predicts their physiochemical and binding qualities
Authors: Lecerf Maxime , Kanyavuz Alexia , Rossini Sofia , Dimitrov Jordan D. 
Published: March 2021

AbstractImmunoglobulin repertoires contain a fraction of antibodies that recognize low molecular weight compounds, including some enzymes’ cofactors, such as heme. Here, by using a set of 113 samples with variable region sequences matching clinical-stage antibodies, we demonstrated that a considerable number of these antibodies interact with heme. Antibodies that interact with heme possess specific sequence traits of their antigen-binding regions. Moreover they manifest particular physicochemical and functional qualities i.e. increased hydrophobicity, higher propensity of self-binding, higher intrinsic polyreactivity and reduced expression yields. Thus, interaction with heme is a strong predictor of different molecular and functional qualities of antibodies. Notably, these qualities are of high importance for therapeutic antibodies, as their presence was associated with failure of drug candidates to reach clinic. Our study reveled an important facet of information about relationship sequence-function in antibodies. It also offers a convenient tool for detection of liabilities of therapeutic antibodies.
EB2 promotes hepatocellular carcinoma proliferation and metastasis via MAPK/ERK pathway by modulating microtubule dynamics
Authors: Zhong Fang-Jing , Li Yi-Ming , Xu Cong , Sun Bo , Wang Ji-Long , Yang Lian-Yue 
Published: March 2021

Abstract
Metastasis is the main cause of poor postoperative survival of hepatocellular carcinoma (HCC) patients. Cytoskeleton rearrangement is a key event in cancer metastasis. However, the significance of microtubule (MT), one of the core components of cytoskeleton, in this process is only beginning to be revealed. Here, we find that the MT dynamics regulator end-binding protein 2 (EB2) is highly expressed in HCC and predicts poor prognosis of HCC patients. Functional studies show that EB2 overexpression promotes HCC proliferation, invasion and metastasis in vitro and in vivo, while EB2 knockdown has opposite results. Mechanistically, EB2 mediates MTs destabilization, increases Src (Src proto-oncogene non-receptor tyrosine kinase) activity, and thus facilitates extracellular signal-regulated kinase (ERK) signaling activation, which could in turn promote EB2 expression in HCC, eventually resulting in enhanced HCC proliferation, invasion and metastasis. Furthermore, U0126, a specific ERK inhibitor, could effectively inhibit EB2-mediated HCC proliferation and metastasis in vitro and in vivo. In conclusion, EB2 coordinates MT cytoskeleton and intracellular signal transduction, forming an EB2–MT–ERK positive feedback loop, to facilitate HCC proliferation, invasion and metastasis. EB2 could serve as a promising prognostic biomarker and potential therapeutic target for HCC; HCC patients with high EB2 expression may benefit from treatment with ERK inhibitors.
Continuous improvement and adaptation of predictive models in smart manufacturing and model management
Authors: Bachinger Florian , Kronberger Gabriel , Affenzeller Michael 
Published: March 2021

AbstractPredictive models are increasingly deployed within smart manufacturing for the control of industrial plants. With this arises, the need for long‐term monitoring of model performance and adaptation of models if surrounding conditions change and the desired prediction accuracy is no longer met. The heterogeneous landscape of application scenarios, machine learning frameworks, hardware‐restricted IIoT platforms, and the diversity of enterprise systems require flexible, yet stable and error resilient solutions that allow the automated adaptation of prediction models. Recommendations are provided for the application and management of predictive models in smart manufacturing. Typical causes for concept drift in real‐world smart manufacturing applications are analysed, and essential steps in data and prediction model management are highlighted, to ensure reliability and efficiency in such applications. For this purpose, recommendations and a reference architecture for model management are provided. In addition, experimental results of two model adaptation strategies on an artificial dataset are shown.
Machine learning discovery of distinguishing laboratory features for severity classification of COVID‐19 patients
Authors: Xiao Yang , Yan Li , Zhang Mingyang , Pinkerton Kent E. , Cao Haosen , Xiao Ying , Li Wei , Li Shuai , Wang Yancheng , Li Shusheng , Cao Zhiguo , Wong Gary Wing‐Kin , Xu Hui , Zhang Hai‐Tao 
Published: March 2021

AbstractThe exponential spread of COVID‐19 worldwide is evident, with devastating outbreaks primarily in the United States, Spain, Italy, the United Kingdom, France, Germany, Turkey and Russia. As of 1 May 2020, a total of 3,308,386 confirmed cases have been reported worldwide, with an accumulative mortality of 233,093. Due to the complexity and uncertainty of the pathology of COVID‐19, it is not easy for front‐line doctors to categorise severity levels of clinical COVID‐19 that are general and severe/critical cases, with consistency. The more than 300 laboratory features, coupled with underlying disease, all combine to complicate proper and rapid patient diagnosis. However, such screening is necessary for early triage, diagnosis, assignment of appropriate level of care facility, and institution of timely intervention. A machine learning analysis was carried out with confirmed COVID‐19 patient data from 10 January to 18 February 2020, who were admitted to Tongji Hospital, in Wuhan, China. A softmax neural network‐based machine learning model was established to categorise patient severity levels. According to the analysis of 2662 cases using clinical and laboratory data, the present model can be used to reveal the top 30 of more than 300 laboratory features, yielding 86.30% blind test accuracy, 0.8195 F1‐score, and 100% consistency using a two‐way patient classification of severe/critical to general. For severe/critical cases, F1‐score is 0.9081 (i.e. recall is 0.9050, and precision is 0.9113). This model for classification can be accomplished at a mini‐second‐level computational cost (in contrast to minute‐level manual). Based on available COVID‐19 patient diagnosis and therapy, an artificial intelligence model paradigm can help doctors quickly classify patients with a high degree of accuracy and 100% consistency to significantly improve diagnostic and classification efficiency. The discovered top 30 laboratory features can be used for greater differentiation to serve as an essential supplement to current guidelines, thus creating a more comprehensive assessment of COVID‐19 cases during the early stages of infection. Such early differentiation will help the assignment of the appropriate level of care for individual patients.
Rhythm motion control in bio‐inspired fishtail based on central pattern generator
Authors: Chen Song , Liu Yubiao , Chen Tehuan , Lou Junqiang 
Published: March 2021

AbstractThe elastic oscillation structure based on central pattern generators (CPGs), which can produce rhythmic motions, is discussed. First, Hopf CPG, the typical CPG model, being a signal generator for the elastic oscillation structure, is analysed via the theory of differential equations. Next, the well‐posedness results of a coupling system composed by the CPG and an elastic beam are proved by means of the linear operator semi‐group theory. Then, the numerical results using the finite difference method indicate that the coupled system can obtain a variety of periodic motion behaviours by choosing the internal parameters of the CPG network. Finally, the dynamic simulation of complex system motion is investigated using COMSOL Multiphysics.
Real‐time multi‐window stereo matching algorithm with fuzzy logic
Authors: Vázquez‐Delgado Héctor‐Daniel , Pérez‐Patricio Madaín , Aguilar‐González Abiel , Arias‐Estrada Miguel‐Octavio , Palacios‐Ramos Marco‐Antonio , Camas‐Anzueto Jorge Luis , Pérez‐Cruz Antonio , Velázquez‐Trujillo Sabino 
Published: March 2021

AbstractStereo matching obtains a depth map called a disparity map that indicates or shows the positions of the objects in a scene. To estimate a disparity map, the most popular trend consists of comparing two images (left‐right) from two different points from the same scene. Unfortunately, small window sizes are suitable to preserve the edges, while large window sizes are required in homogeneous areas. To solve this problem, in this article, a novel real‐time stereo matching algorithm embedded in an FPGA is proposed. The approach consists of estimating disparity maps with different window sizes by using the sum of absolute differences (SAD) as a local correlation metric. Once the disparity maps are obtained, the left‐right consistency for each window size is computed. At the end of this stage, the centre pixel deviation is estimated through a 5 × 5 window and the Sobel gradient is extracted from the left image. Finally, both parameters are processed by a Fuzzy Inference System (FIS), which combines the calculated disparities and generates a final disparity map. An architecture embedded in FPGA is established and hardware acceleration strategies are discussed. Experimental results demonstrated that this algorithmic formulation provides promising results compared with the current state of the art.
Point cloud classification by dynamic graph CNN with adaptive feature fusion
Authors: Guo Rui , Zhou Yong , Zhao Jiaqi , Man Yiyun , Liu Minjie , Yao Rui , Liu Bing 
Published: March 2021

AbstractThe deep neural network has made the most advanced breakthrough in almost all 2D image tasks, so we consider the application of deep learning in 3D images. Point cloud data, as the most basic and important form of representation of 3D images, can accurately and intuitively show the real world. The authors propose a new network based on feature fusion to improve the point cloud classification and segmentation tasks. Our network mainly consists of three parts: global feature extractor, local feature extractor and adaptive feature fusion module. A multi‐scale transformation network is devised to guarantee the invariance of the transformation of the global feature, and a residual block is introduced to alleviate the problem of gradient disappearance to enhance the global feature extractor. Based on the edge convolution and multi‐layer perceptron, a local feature extractor is constructed. Finally, an adaptive feature‐fusion module is proposed to complete the fusion of global features and local features. Extensive experiments on point cloud classification and segmentation tasks are carried out to verify the effectiveness of the proposed method. The classification accuracy of the ModelNet40 is 93.6%, which is 4.4% higher than that of the PointNet. Similarly, the segmentation accuracy on the ShapeNet is 85.6%, which is higher than other methods.
An automatic feature selection and classification framework for analyzing ultrasound kidney images using dragonfly algorithm and random forest classifier
Authors: Narasimhulu C Venkata 
Published: March 2021

AbstractIn medical imaging, the automatic diagnosis of kidney carcinoma has become more difficult because it is not easy to detect by physicians. Pre‐processing is the first identification method to enhance image quality, remove noise and unwanted components from the backdrop of the kidneys image. The pre‐processing method is essential and significant for the proposed algorithm. The objective of this analysis is to recognize and classify kidney disturbances with an ultrasound scan by providing a number of substantial content description parameters. The ultrasound pictures are prepared to protect the interest pixels before extracting the feature. A series of quantitative features were synthesized of each images, the principal component analysis was conducted for minimizing the number of features to produce set of wavelet‐based multi‐scale features. Dragonfly algorithm (DFA) was executed in this method. In the proposed work, the design and training of a random decision forest classifier and selected features are implemented. The classification of e‐health information using ideal characteristics is used by the RF classifier. The proposed technique is activated in MATLAB/simulink work site and the experimental results show that the peak accuracy of the proposed technique is 95.6% using GWO‐FFBN techniques compared to other existing techniques.
Optimisation‐based design of transversal signal‐interference microwave bandpass and lowpass filters with extended stopband
Authors: Gómez‐García Roberto , Yang Li , Muñoz‐Ferreras José‐María 
Published: March 2021

AbstractGeneralised bi‐path stepped‐impedance‐line signal‐interference bandpass and lowpass transversal filtering sections (TFSs) with increased stopband bandwidth above the main transmission band are presented. They are realised by partitioning the two in‐parallel transmission‐line paths in their related classic spectrally periodic TFS approach into a plurality of line sub‐segments, whose characteristic‐impedance and electrical‐length values are derived through optimisation. It is demonstrated that the stopband range above the transmission band in these generalised non‐frequency‐periodic TFSs can be remarkably broadened with regard to those intrinsic to their conventional bandpass and lowpass TFS counterparts. Furthermore, as added benefits, this is achieved for shorter transmission‐line paths and a sharper cut‐off slope between the transmission band and the extended stopband. For experimental‐validation purposes, three proof‐of‐concept microstrip prototypes are manufactured and characterised. They correspond to a 1‐GHz bandpass TFS, a 1‐GHz two‐TFS‐in‐series‐cascade‐based bandpass filter (BPF) with augmented selectivity and out‐of‐band power‐rejection levels, and one lowpass TFS with a designed 3‐dB cut‐off frequency of 1.25 GHz.