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FGF-2 is required to prevent astrogliosis in the facial nucleus after facial nerve injury and mechanical stimulation of denervated vibrissal muscles

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The Journal of Biomedical Research 2016年第2期30卷 142-148页

作者： Arzu Hizay mark seitz Maria Grosheva Nektarios Sinis Yasemin Kaya Habib Bendella Levent Sarikcioglu Sarah A.Dunlop Doychin N.Angelov Department of Anatomy Akdeniz UniversityFaculty of Medicine Department of Anatomy I University of Cologne Department of Oto-Rhino-Laryngology University Hospital Cologne Depeartment of Plastic- Hand-and Reconstructive Microsurgery Department of Neurosurgery Hospital MerheimUniversity of Witten-Herdecke Experimental and Regenerative Neurosciences School of Animal BiologyUniversity of Western Australia(M092) 35 Stirling Highway

Recently,we have shown that manual stimulation of paralyzed vibrissal muscles after facial-facial anastomosis reduced the poly-innervation of neuromuscular junctions and restored vibrissal *** gene knock outs,we found a differential dependence of manual stimulation effects on growth ***,insulin-like growth factor-1 and brain-derived neurotrophic factor are required to underpin manual stimulation-mediated improvements,whereas FGF-2 is *** lack of dependence on FGF-2 in mediating these peripheral effects prompted us to look centrally,*** the facial nucleus where increased astrogliosis after facial-facial anastomosis follows ＂synaptic stripping＂.We measured the intensity of Cy3-fluorescence after immunostaining for glial fibrillary acidic protein（GFAP） as an indirect indicator of synaptic coverage of axotomized neurons in the facial nucleus of mice lacking FGF-2（FGF-2^（-/-） mice）.There was no difference in GFAP-Cy3-fluorescence（pixel number,gray value range17-103） between intact wildtype mice（2.12± 0.37×10~7） and their intact FGF-2^（-/-） counterparts（2.12±0.27×10~7） nor after facial-facial anastomosis ＋handling（wildtype：4.06±0.32×10~7;FGF-2^（-/-）：4.39±0.17×10~7）.However,after facial-facial anastomosis,GFAP-Cy3-fluorescence remained elevated in FGF-2^（-/-）-animals（4.54±0.12×10~7）,whereas manual otimulation reduced the intensity of GFAP-immunofluorescence in wild type mice to values that were not significantly different from intact mice（2.63±0.39×10）.We conclude that FGF-2 is not required to underpin the beneficial effects of manual stimulation at the neuro-muscular junction,but it is required to minimize astrogliosis in the brainstem and,by implication,restore synaptic coverage of recovering facial motoneurons.

关键词： FGF-2 facial nerve axotomy astrogliosis whisking function polyinnervation

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Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning

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npj Computational Materials 2016年第1期2卷 1-10页

作者： Bharat Medasani Anthony Gamst Hong Ding Wei Chen Kristin A Persson mark Asta Andrew Canning Maciej Haranczyk Computational Research Division Lawrence Berkeley National LaboratoryBerkeleyCA 94720USA Physical and Computational Sciences Directorate Pacific National Northwest LaboratoryRichlandWA 99354USA Biostatistics and Bioinformatics University of CaliforniaSan DiegoCA 92093USA Department of Materials Science&Engineering University of CaliforniaBerkeleyCA 94720USA Energy Technologies Area Division Lawrence Berkeley National LaboratoryBerkeleyCA 94720USA Energy Technologies Area Lawrence Berkeley National LaboratoryBerkeleyCA 94720USA Department of Mechanical Materials and Aerospace EngineeringIllinois Institute of TechnologyChicagoIL 60616USA Materials Sciences Division Lawrence Berkeley National LaboratoryBerkeleyCA 94720USA

We present a combination of machine learning and high throughput calculations to predict the points defects behavior in binary intermetallic(A–B)compounds,using as an example systems with the cubic B2 crystal structure(with equiatomic AB stoichiometry).To the best of our knowledge,this work is the first application of machine learning-models for point defect *** throughput first principles density functional calculations have been employed to compute intrinsic point defect energies in 100 B2 intermetallic *** systems are classified into two groups:(i)those for which the intrinsic defects are antisites for both A and B rich compositions,and(ii)those for which vacancies are the dominant defect for either or both composition *** data was analyzed by machine learning-techniques using decision tree,and full and reduced multiple additive regression tree(MART)*** these three schemes,a reduced MART(r-MART)model using six descriptors(formation energy,minimum and difference of electron densities at the Wigner–seitz cell boundary,atomic radius difference,maximal atomic number and maximal electronegativity)presents the highest fit(98%)and predictive(75%)*** model is used to predict the defect behavior of other B2 compounds,and it is found that 45%of the compounds considered feature vacancies as dominant defects for either A or B rich compositions(or both).The ability to predict dominant defect types is important for the modeling of thermodynamic and kinetic properties of intermetallic compounds,and the present results illustrate how this information can be derived using modern tools combining high throughput calculations and data analytics.

关键词： intermetallic intermetallics defect

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