Constructing a new nigrostriatal pathway in the Parkinsonian model with bridged neural transplantation in substantia nigra

The physical repair and restoration of a completely damaged pathway in the brain has not been achieved previously. In a previous study, using excitatory amino acid bridging and fetal neural transplantation, we demonstrated that a bridged mesencephalic transplant in the substantia nigra generated an artificial nerve pathway that reinnervated the striatum of 6-hydroxydopamine (6-OHDA)-lesioned rats. In the current study, we report that a bridged mesencephalic transplant can anatomically, neurochemically, and functionally reinstate the 6-OHDA-eradicated nigro-striatal pathway. An excitatory amino acid, kainic acid, laid down in a track during the transplant generated a trophic environment that effectively guided the robust growth of transplanted neuronal fibers in a bundle to innervate the distal striatum. Growth occurred at the remarkable speed of approximately 200 microm/d. Two separate and distinct types of dopamine (DA) innervation from the transplant have been achieved for the first time: (1) DA innervation of the striatum, and (2) DA innervation of the pars reticularis of the substantia nigra. In addition, neuronal tracing revealed that reciprocal connections were achieved. The grafted DA neurons in the SNr innervated the host's striatum, whereas the host's striatal neurons, in turn, innervated the graft within 3-8 weeks. Electrochemical volt- ammetry recording revealed the restoration of DA release and clearance in a broad striatal area associated with the DA reinnervation. Furthermore, the amphetamine-induced rotation was attenuated, which indicates that the artificial pathways were motor functional. This study provides additional evidences that our bridged transplantation technique is a potential means for the repair of a completely damaged neuronal pathway

Gender Determination using Fingerprint Features

Several previous studies have investigated the gender difference of the fingerprint features. However, regarding to the statistical significance of such differences, inconsistent results have been obtained. To resolve this problem and to develop a method for gender determination, this work proposes and tests three fingertip features for gender determination. Fingerprints were obtained from 115 normal healthy adults comprised of 57 male and 58 female volunteers. All persons were born in Taiwan and were of Han nationality. The age range was18-35 years. The features of this study are ridge count, ridge density, and finger size, all three of which can easily be determined by counting and calculation. Experimental results show that the tested ridge density features alone are not very effective for gender determination. However, the proposed ridge count and finger size features of left little fingers are useful, achieving a classification accuracy of 75% (P-valu

Fuzzy Linguistic Optimization on Multi-Attribute Machining

Most existing multi-attribute optimization researches for the modern CNC (computer numerical control) turning industry were either accomplished within certain manufacturing circumstances, or achieved through numerous equipment operations. Therefore, a general deduction optimization scheme proposed is deemed to be necessary for the industry. In this paper, four parameters (cutting depth, feed rate, speed, tool nose runoff) with three levels (low, medium, high) are considered to optimize the multi-attribute (surface roughness, tool wear, and material removal rate) finish turning. Through FAHP (Fuzzy Analytic Hierarchy Process) with eighty intervals for each attribute, the weight of each attribute is evaluated from the paired comparison matrix constructed by the expert judgment. Additionally, twenty-seven fuzzy control rules using trapezoid membership function with respective to seventeen linguistic grades for each attribute are constructed. Considering thirty input and eighty output intervals, the defuzzifierion using center of gravity is thus completed. The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is moreover utilized to integrate and evaluate the multiple machining attributes for the Taguchi experiment, and thus the optimum general deduction parameters can then be received. The confirmation experiment for optimum general deduction parameters is furthermore performed on an ECOCA-3807 CNC lathe. It is shown that the attributes from the fuzzy linguistic optimization parameters are all significantly advanced comparing to those from benchmark. This paper not only proposes a general deduction optimization scheme using orthogonal array, but also contributes the satisfactory fuzzy linguistic approach for multiple CNC turning attributes with profound insight

Impact of Cloud Ice Particle Size Uncertainty in a Climate Model and Implications for Future Satellite Missions

Ice particle size is pivotal to determining ice cloud radiative effect and precipitating rate. However, there is a lack of accurate ice particle effective radius (R_(ei)) observation on the global scale to constrain its representation in climate models. In support of future mission design, here we present a modeling assessment of the sensitivity of climate simulations to R_(ei) and quantify the impact of the proposed mission concept on reducing the uncertainty in climate sensitivity. We perturb the parameters pertaining to ice fall speed parameter and R_(ei) in radiation scheme, respectively, in National Center for Atmospheric Research CESM1 model with a slab ocean configuration. The model sensitivity experiments show that a settling velocity increase due to a larger R_(ei) results in a longwave cooling dominating over a shortwave warming, a global mean surface temperature decrease, and precipitation suppression. A similar competition between longwave and shortwave cloud forcing changes also exists when perturbing R_(ei) in the radiation scheme. Linearity generally holds for the climate response for R_(ei) related parameters. When perturbing falling snow particle size (R_(es)) in a similar way, we find much less sensitivity of climate responses. Our quadrupling CO₂ experiments with different parameter settings reveal that R_(ei) and R_(es) can account for changes in climate sensitivity significantly from +12.3% to −6.2%. By reducing the uncertainty ranges of R_(ei) and R_(es) from a factor of 2 to ±25%, a future satellite mission under design is expected to improve the climate state simulations and reduce the climate sensitivity uncertainty pertaining to ice particle size by approximately 60%. Our results highlight the importance of better observational constraints on R_(ei) by satellite missions

Impact of Cloud Ice Particle Size Uncertainty in a Climate Model and Implications for Future Satellite Missions

Ice particle size is pivotal to determining ice cloud radiative effect and precipitating rate. However, there is a lack of accurate ice particle effective radius (R_(ei)) observation on the global scale to constrain its representation in climate models. In support of future mission design, here we present a modeling assessment of the sensitivity of climate simulations to R_(ei) and quantify the impact of the proposed mission concept on reducing the uncertainty in climate sensitivity. We perturb the parameters pertaining to ice fall speed parameter and R_(ei) in radiation scheme, respectively, in National Center for Atmospheric Research CESM1 model with a slab ocean configuration. The model sensitivity experiments show that a settling velocity increase due to a larger R_(ei) results in a longwave cooling dominating over a shortwave warming, a global mean surface temperature decrease, and precipitation suppression. A similar competition between longwave and shortwave cloud forcing changes also exists when perturbing R_(ei) in the radiation scheme. Linearity generally holds for the climate response for R_(ei) related parameters. When perturbing falling snow particle size (R_(es)) in a similar way, we find much less sensitivity of climate responses. Our quadrupling CO₂ experiments with different parameter settings reveal that R_(ei) and R_(es) can account for changes in climate sensitivity significantly from +12.3% to −6.2%. By reducing the uncertainty ranges of R_(ei) and R_(es) from a factor of 2 to ±25%, a future satellite mission under design is expected to improve the climate state simulations and reduce the climate sensitivity uncertainty pertaining to ice particle size by approximately 60%. Our results highlight the importance of better observational constraints on R_(ei) by satellite missions