Fabrication of microscale medical devices by two-photon polymerization with multiple foci via a spatial light modulator

Two-photon polymerization is an appealing technique for producing microscale devices due to its flexibility in producing structures with a wide range of geometries as well as its compatibility with materials suitable for biomedical applications. The greatest limiting factor in widespread use of two-photon polymerization is the slow fabrication times associated with line-by-line, high-resolution structuring. In this study, a recently developed technology was used to produce microstructures by two-photon polymerization with multiple foci, which significantly reduces the production time. Computer generated hologram pattern technology was used to generate multiple laser beams in controlled positions from a single laser. These multiple beams were then used to simultaneously produce multiple microstructures by two-photon polymerization. Arrays of micro-Venus structures, tissue engineering scaffolds, and microneedle arrays were produced by multifocus two-photon polymerization. To our knowledge, this work is the first demonstration of multifocus two-photon polymerization technology for production of a functional medical device. Multibeam fabrication has the potential to greatly improve the efficiency of two-photon polymerization production of microscale devices such as tissue engineering scaffolds and microneedle arrays

Evidence for a Self-Bound Liquid State and the Commensurate-Incommensurate Coexistence in 2D 3^3He on Graphite

We made heat-capacity measurements of two dimensional (2D) $^3$He adsorbed on graphite preplated with monolayer $^4$He in a wide temperature range (0.1 $\leq T \leq$ 80 mK) at densities higher than that for the 4/7 phase (= 6.8 nm$^{-2}$). In the density range of 6.8 $\leq \rho \leq$ 8.1 nm$^{-2}$, the 4/7 phase is stable against additional $^3$He atoms up to 20% and they are promoted into the third layer. We found evidence that such promoted atoms form a self-bound 2D Fermi liquid with an approximate density of 1 nm$^{-2}$ from the measured density dependence of the $\gamma$-coefficient of heat capacity. We also show evidence for the first-order transition between the commensurate 4/7 phase and the ferromagnetic incommensurate phase in the second layer in the density range of 8.1 $\leq \rho \leq$ 9.5 nm$^{-2}$.Comment: 6 pages, 4 figure

An overlooked connection: serotonergic mediation of estrogen-related physiology and pathology

BACKGROUND: In humans, serotonin has typically been investigated as a neurotransmitter. However, serotonin also functions as a hormone across animal phyla, including those lacking an organized central nervous system. This hormonal action allows serotonin to have physiological consequences in systems outside the central nervous system. Fluctuations in estrogen levels over the lifespan and during ovarian cycles cause predictable changes in serotonin systems in female mammals. DISCUSSION: We hypothesize that some of the physiological effects attributed to estrogen may be a consequence of estrogen-related changes in serotonin efficacy and receptor distribution. Here, we integrate data from endocrinology, molecular biology, neuroscience, and epidemiology to propose that serotonin may mediate the effects of estrogen. In the central nervous system, estrogen influences pain transmission, headache, dizziness, nausea, and depression, all of which are known to be a consequence of serotonergic signaling. Outside of the central nervous system, estrogen produces changes in bone density, vascular function, and immune cell self-recognition and activation that are consistent with serotonin's effects. For breast cancer risk, our hypothesis predicts heretofore unexplained observations of the opposing effects of obesity pre- and post-menopause and the increase following treatment with hormone replacement therapy using medroxyprogesterone. SUMMARY: Serotonergic mediation of estrogen has important clinical implications and warrants further evaluation

Random Forest Regression Model for Estimation of the Growing Stock Volumes in Georgia, USA, Using Dense Landsat Time Series and FIA Dataset

The forest volumes are essential as they are directly related to the economic and environmental values of the forests. Satellite-based forest volume estimation was first developed in the 1990s, and the accuracy of the estimation has been improved over time. One of the satellite-based forest volume estimation issues is that it tends to overestimate the large volume class and underestimate the small volume class. Free availability of the major satellite imagery and the development of cloud-based computational platforms facilitate an immense amount of satellite imagery in the estimation. In this paper, we set three objectives: (1) to examine whether the long Landsat time series contributes to the improvement of the estimation accuracy, (2) to explore the effectiveness of forest disturbance record and land cover data as ancillary spatial data on the accuracy of the estimation, and (3) to apply the bias correction method to reduce the bias of the estimation. We computed three Tasseled-cap components from the Landsat data for preparation of short (2014–2016) and long (1984–2016) time series. Each data entity was analyzed with harmonic regressions resulting in the coefficients and the fitted values recorded as pixel values in a multilayer raster database. Data included Forest Inventory and Analysis (FIA) unit field inventory measurements provided by the United States Department of Agriculture Forest Service and the National Land Cover Database and disturbance history data added as ancillary information. The totality of the available data was organized into seven distinct Random Forest (RF) models with different variables compared against each other to identify the ones with the most satisfactory performance. A bias correction method was then applied to all the RF models to examine the effectiveness of the method. Among the seven models, the worst one used the coefficients and fitted values of the short Landsat time series only, and the best one used coefficients and fitted values of both short and long Landsat time series. Using the Out-of-bag (OOB) score, the best model was found to be 34.4% better than the worst one. The model that used only the long time series data had almost the same OOB score as the best model. The results indicate that the use of the long Landsat time series improves model performance. Contrary to the previous research employing forest disturbance data as a feature variable had almost no effect on OOB. The bias correction method reduced the relative size of the bias in the estimates of the best model from 3.79% to −1.47%, the bottom 10% bias by 12.5 points, and the top 10% bias by 9.9 points. Depending on the types of forest, important feature variables were differed, reflecting the relationship between the time series remote sensing data we computed for this research and the forests’ phenological characteristics. The availability of Light Detection And Ranging (LiDAR) data and accessibility of the precise locations of the FIA data are likely to improve the model estimates further

Random Forest Regression Model for Estimation of the Growing Stock Volumes in Georgia, USA, Using Dense Landsat Time Series and FIA Dataset

The forest volumes are essential as they are directly related to the economic and environmental values of the forests. Satellite-based forest volume estimation was first developed in the 1990s, and the accuracy of the estimation has been improved over time. One of the satellite-based forest volume estimation issues is that it tends to overestimate the large volume class and underestimate the small volume class. Free availability of the major satellite imagery and the development of cloud-based computational platforms facilitate an immense amount of satellite imagery in the estimation. In this paper, we set three objectives: (1) to examine whether the long Landsat time series contributes to the improvement of the estimation accuracy, (2) to explore the effectiveness of forest disturbance record and land cover data as ancillary spatial data on the accuracy of the estimation, and (3) to apply the bias correction method to reduce the bias of the estimation. We computed three Tasseled-cap components from the Landsat data for preparation of short (2014–2016) and long (1984–2016) time series. Each data entity was analyzed with harmonic regressions resulting in the coefficients and the fitted values recorded as pixel values in a multilayer raster database. Data included Forest Inventory and Analysis (FIA) unit field inventory measurements provided by the United States Department of Agriculture Forest Service and the National Land Cover Database and disturbance history data added as ancillary information. The totality of the available data was organized into seven distinct Random Forest (RF) models with different variables compared against each other to identify the ones with the most satisfactory performance. A bias correction method was then applied to all the RF models to examine the effectiveness of the method. Among the seven models, the worst one used the coefficients and fitted values of the short Landsat time series only, and the best one used coefficients and fitted values of both short and long Landsat time series. Using the Out-of-bag (OOB) score, the best model was found to be 34.4% better than the worst one. The model that used only the long time series data had almost the same OOB score as the best model. The results indicate that the use of the long Landsat time series improves model performance. Contrary to the previous research employing forest disturbance data as a feature variable had almost no effect on OOB. The bias correction method reduced the relative size of the bias in the estimates of the best model from 3.79% to −1.47%, the bottom 10% bias by 12.5 points, and the top 10% bias by 9.9 points. Depending on the types of forest, important feature variables were differed, reflecting the relationship between the time series remote sensing data we computed for this research and the forests’ phenological characteristics. The availability of Light Detection And Ranging (LiDAR) data and accessibility of the precise locations of the FIA data are likely to improve the model estimates further