BDNF maintains adult taste innervation and is required for taste nerve regeneration after injury.

Brain derived neurotropic factor (BDNF) is required for the gustatory neuron survival,target innervation, and taste bud maintenance during development. However, whether BDNF has any function in the adult gustatory system in normal conditions or after nerve injury is unclear. To address these issues, I inducibly removed BDNF in all cells expressing BDNF in adult mice. In the experimental animals, Bdnf expression decreased to 5% of control mice in the lingual epithelium and geniculate ganglion (p\u3c 0.01) at both two weeks and ten weeks after tamoxifen administration. I found no effect on taste bud morphology at four weeks following Bdnf gene deletion. However, ten weeks following Bdnf gene deletion, P2X3-positive and TUJ1-positive gustatory innervation to individual taste buds was reduced by nearly half (each with p \u3c 0.01) and both taste bud volume and taste cell number decreased 30% (each with p\u3c 0.01). These experiments demonstrate that BDNF is required for maintenance of normal levels of taste innervation and normal taste bud morphology in adulthood. In addition, taste cells expressing PLCß2 (phospholipase C ß2), a marker for taste cells that respond to sweet, bitter and umami, did not decrease after Bdnf gene deletion in the adult. Thus, the missing taste cells are of another type. This indicates that taste cell loss is not uniform across the various taste cell types, even if nearly all taste cell types receive the P2X3 and TUJ1 innervation. Since BDNF is required for initial innervation of the taste system and supports taste bud innervation and size in adulthood, it could also be required for nerve reinnervation after injury. To determine if Bdnf is still expressed following nerve section, the chorda tympani nerve (taste nerve) was sectioned and Bdnf level was detected with Real Time RT-PCR. Bdnf continued to be expressed at normal levels from two days to two months post-surgery in both geniculate ganglion and tongue epithelium. Therefore, BDNF could be involved with chorda tympani regeneration. To determine if this was the case, the Bdnf gene was deleted in adult inducible transgenic mice (under the control of a Ubiquitin promoter) two weeks before chorda tympani nerve section. Taste bud number was reduced by half in all genotypes at two weeks post-surgery (p \u3c 0.01). For the remaining taste buds, gustatory innervation was nearly gone with only a little innervation from the trigeminal nerve remaining in the taste bud (p \u3c 0.01). Taste bud volume (p \u3c 0.01) and taste cell number (p\u3c 0.01) were reduced by half for both control and experimental genotypes. Eight weeks post-surgery, taste bud number recovered in mice without Bdnf gene deletion, but did not recover in mice following Bdnf gene deletion (p \u3c 0.01). Gustatory nerve innervation returned in 70% of the taste buds in control mice (p\u3c 0.01). For those reinnervated taste buds, both taste bud volume and taste cell number increased to normal levels. However, in mice lacking the Bdnf gene, gustatory fibers only reinnervated 7.8% of the taste buds (p \u3c 0.01); for most uninnervated taste buds, both taste bud volume and taste cell number remained small. These experiments demonstrate that BDNF is crucial for promoting regeneration of gustatory nerve fibers in adulthood. Following gustatory nerve section, considerable adult plasticity has been observed on the contralateral side including enlarged taste buds with more cells (Guagliardo and Hill, 2007). To determine if this anatomical change was associated with alter Bdnf expression. I examined Bdnf level in the geniculate ganglion and tongue epithelium on the contralateral side following chorda tympani nerve section. Results showed Bdnf expression increased two fold at two weeks post-surgery in geniculate ganglion on the contralateral side (p \u3c 0.05), indicating BDNF may involve with the observed plastic changes. To determine if the increase in taste bud size was associated with increased innervation and/or regulated by BDNF, the Bdnf gene was then deleted in inducible knockout mice before nerve surgery, and taste bud size and amount of innervation were measured on the contralateral side. The results showed taste bud volume, taste cell number and a marker for nerve fibers all increased on the contralateral side in mice without Bdnf gene deletion at eight weeks post-surgery. This indicates that larger taste buds could be supported by increased TUJ1 positive fibers from trigeminal nerve. In addition, in mice lacking Bdnf, taste bud volume, taste cell number and innervation did not increase on the contralateral side after surgery, which indicates that Bdnf may contribute to larger taste buds on the contralateral side following nerve section by supporting increased innervation to the larger taste buds

Phase field simulation of dendritic microstructure in additively manufactured titanium alloy

Additive manufacturing (AM) processes for metals, such as selective laser sintering and electron beam melting, involve rapid solidification process. The microstructure of the fabricated material and its properties strongly depend on the solidification. Therefore, in order to control and optimize the AM process, it is important to understand the microstructure evolution. In this work, using Ti-6Al-4V as a model system, the phase field method is applied to simulate the microstructure evolution in additively manufactured metals. First, the fundamental governing equations are presented. Then the effects of various processing related parameters, including local temperature gradient, scan speed and cooling rate, on dendrites’ morphology and growth velocity are studied. The simulated results show that the dendritic arms grow along the direction of the heat flow. Higher temperature gradient, scan speed and cooling rate will result in small dendritic arm spacing and higher growth velocity. The simulated dendritic morphology and arm spacings are in good agreement with experimental data and theoretical predictions

Process Design of Laser Powder Bed Fusion of Stainless Steel Using a Gaussian Process-Based Machine Learning Model

In this work, a Gaussian process (GP)-based machine learning model is developed to predict the remelted depth of single tracks, as a function of combined laser power and laser scan speed in a laser powder bed fusion process. The GP model is trained by both simulation and experimental data from the literature. The mean absolute prediction error magnified by the GP model is only 0.6 μm for a powder bed with layer thickness of 30 μm, suggesting the adequacy of the GP model. Then, the process design maps of two metals, 316L and 17-4 PH stainless steels, are developed using the trained model. The normalized enthalpy criterion of identifying keyhole mode is evaluated for both stainless steels. For 316L, the result suggests that the ΔHhs≥30 criterion should be related to the powder layer thickness. For 17-4 PH, the criterion should be revised to ΔHhs≥25

Atomistic modeling of resistivity evolution of copper nanoparticle in intense pulsed light sintering process

In this work, the intense pulsed light (IPL) sintering process of copper nanoparticle ink is simulated using molecular dynamics (MD) method. First, the neck size growth between the two copper nanoparticles during the IPL sintering process is computed. The resultant electrical resistivity is then calculated by substituting the neck size into the Reimann-Weber formula. Overall, a rapid decrease of electric resistivity is observed in the beginning of the sintering, which is caused by quick neck size growth, followed by a gradually decrease of resistivity. In addition, the correlation of the simulated temperature dependent resistivity is similar to that of the experimentally measured resistivity. The MD model is an effective tool for designers to optimize the IPL sintering process

Transplantation of Bone Marrow Mesenchymal Stem Cells Prevents Radiation-Induced Artery Injury by Suppressing Oxidative Stress and Inflammation

The present study aims to explore the protective effect of human bone marrow mesenchymal stem cells (hBMSCs) on radiation-induced aortic injury (RIAI). hBMSCs were isolated and cultured from human bone marrow. Male C57/BL mice were irradiated with a dose of 18-Gy 6MV X-ray and randomly treated with either vehicle or hBMSCs through tail vein injection with a dose of 103 or 104 cells/g of body weight (low or high dose of hBMSCs) within 24 h. Aortic inflammation, oxidative stress, and vascular remodeling were assessed by immunohistochemical staining at 3, 7, 14, 28, and 84 days after irradiation. The results revealed irradiation caused aortic cell apoptosis and fibrotic remodeling indicated by aortic thickening, collagen accumulation, and increased expression of profibrotic cytokines (CTGF and TGF-β). Further investigation showed that irradiation resulted in elevated expression of inflammation-related molecules (TNF-α and ICAM-1) and oxidative stress indicators (4-HNE and 3-NT). Both of the low and high doses of hBMSCs alleviated the above irradiation-induced pathological changes and elevated the antioxidant enzyme expression of HO-1 and catalase in the aorta. The high dose even showed a better protective effect. In conclusion, hBMSCs provide significant protection against RIAI possibly through inhibition of aortic oxidative stress and inflammation. Therefore, hBMSCs can be used as a potential therapy to treat RIAI

Probabilistic Feasibility Design of a Laser Powder Bed Fusion Process Using Integrated First-Order Reliability and Monte Carlo Methods

Quality inconsistency due to uncertainty hinders the extensive applications of a laser powder bed fusion (L-PBF) additive manufacturing process. To address this issue, this study proposes a new and efficient probabilistic method for the reliability analysis and design of the L-PBF process. The method determines a feasible region of the design space for given design requirements at specified reliability levels. If a design point falls into the feasible region, the design requirement will be satisfied with a probability higher or equal to the specified reliability. Since the problem involves the inverse reliability analysis that requires calling the direct reliability analysis repeatedly, directly using Monte Carlo simulation (MCS) is computationally intractable, especially for a high reliability requirement. In this work, a new algorithm is developed to combine MCS and the first-order reliability method (FORM). The algorithm finds the initial feasible region quickly by FORM and then updates it with higher accuracy by MCS. The method is applied to several case studies, where the normalized enthalpy criterion is used as a design requirement. The feasible regions of the normalized enthalpy criterion are obtained as contours with respect to the laser power and laser scan speed at different reliability levels, accounting for uncertainty in seven processing and material parameters. The results show that the proposed method dramatically alleviates the computational cost while maintaining high accuracy. This work provides a guidance for the process design with required reliability

Machine Learning in Additive Manufacturing: A Review

In this review article, the latest applications of machine learning (ML) in the additive manufacturing (AM) field are reviewed. These applications, such as parameter optimization and anomaly detection, are classified into different types of ML tasks, including regression, classification, and clustering. The performance of various ML algorithms in these types of AM tasks are compared and evaluated. Finally, several future research directions are suggested

A Multi-Scale Multi-Physics Modeling Framework of Laser Powder Bed Fusion Additive Manufacturing Process

A longstanding challenge is to optimize additive manufacturing (AM) process in order to reduce AM component failure due to excessive distortion and cracking. To address this challenge, a multi-scale physics-based modeling framework is presented to understand the interrelationship between AM processing parameters and resulting properties. In particular, a multi-scale approach, spanning from atomic, particle, to component levels, is employed. The simulations of sintered material show that sintered particles have lower mechanical strengths than the bulk metal because of their porous structures. Higher heating rate leads to a higher mechanical strength due to accelerated sintering rates. The average temperature in the powder bed increases with higher laser power. The predicted distortion due to residual stress in the AM fabricated component is in good agreement with experimental measurements. In summary, the model framework provides a design tool to optimize the metal powder based additive manufacturing process

Simulation of Spatters Sticking Phenomenon in Laser Powder Bed Fusion Process Using the Smoothed Particle Hydrodynamics Method

In this work, a smoothed particle hydrodynamics (SPH) method is developed to simulate the spattering phenomenon in the laser powder bed fusion (L-PBF) process. First, an experiment using the high-speed synchrotron X-ray full-field imaging is conducted to acquire in-situ images during the L-PBF process. Then, a scenario is selected from the X-ray image as a case study of the SPH model. In the case study, a particle is ejected and melted by the metal vapor, impacts with another particle, solidifies, and sticks to the other particle to form a rigid body. As a result, the trajectories of the two particles match well with the experimental observation. The evolution of velocity and temperature of the particle is extracted from the simulation for analysis. The SPH model can be a useful alternative to computational models of simulating the spattering phenomenon of L-PBF

Association of psychological distress, smoking and genetic risk with the incidence of lung cancer: a large prospective population-based cohort study

BackgroundEmerging evidence suggests a potential link between psychological distress (anxiety and depression) and lung cancer risk, however, it is unclear whether other factors such as tobacco smoking and genetic susceptibility modify the association.MethodsWe included 405,892 UK Biobank participants free of cancer at baseline. Psychological distress was measured using the Patient Health Questionnaire-4 (PHQ-4). A polygenic risk score (PRS) was calculated using 18 lung cancer-associated genetic loci. Multivariable Cox regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs).ResultsDuring a median follow-up of 7.13 years, 1754 lung cancer cases were documented. The higher score of psychological distress was associated with an increased risk of lung cancer (HRper 1-SD= 1.07, 95% CI: 1.02-1.11) after adjustment for smoking and other confounders. Mediation analysis revealed that 16.8% (95% CI: 13.0%-20.6%) of the distress-lung cancer association was mediated by smoking. Compared with never smokers with no distress, participants with heavy smoking and high distress had the highest risk of lung cancer (HR=18.57, 95% CI: 14.51-23.76). Both multiplicative and additive interactions were observed between smoking and psychological distress in lung cancer. Furthermore, the greatest relative increase in risk was observed among those with high genetic risk and high distress (HR=1.87, 95%CI: 1.50-2.33), and there was a significant additive interaction between the PRS and psychological distress.ConclusionOur results indicate that psychological distress was associated with an elevated risk of incident lung cancer, and such relation was modified by tobacco smoking and genetic susceptibility