駿河台校地-旗本中坊氏屋敷跡-の発掘調査

Articledepartmental bulletin pape

GDF11 expression in the adult rat central nervous system

浜松医科大学博士（医学）doctoral医学系研究科thesi

The Issues over the Publicity of Amish Education: Deliberation through Phenomenological Perspective

本論文は、アーミシュの教育について、日本では大変話題となった、ウィスコンシン州Vsヨーダーの裁判を巡って、教育の公共性と子どもの信教の自由が焦点となっていることを取り上げた。この裁判は、連邦最高裁まで争われ、結局、マイノリティの民族的文化的アイデンティティを保証するためにアーミッシュの８年制の学校を公に認めることとなった。他方、アメリカのエミイ・ガットマンのような教育法学者は、アーミッシュの親が、合衆国憲法に逆らって義務教育年限のハイスクールの一年目から子どもの就学を拒否したことは、子どもの信教の自由そして将来の職業選択の自由を奪っているという、立場にたつ。教育の公共性 において、問題となるのは、多くは信教の自由である。しかし、ガットマンの公共性が問題とするのは、具体的には、職業選択の自由やイスラム教徒の学校へのスカーフ登校のような世俗的問題であって、宗教の中身そのものではない。日本の学者が問題とするのは、宗教的な信念が開かれた相互対話に開かれた熟議民主主義によって「相互尊重」と相互理解を可能にするという仮説である。高校段階における「熟議」や「熟慮」は、「批判的思考力」を育て、教育における「公共性」を可能にするというものである。このような公共性概念は、一つのドクサ（「類推」とか「憶見」）である。アーミッシュの平和主義、非暴力主義、友愛主義は、「その宗教が」ではなく、日常生活世界の生き方がエートスとしてあり、何か公民の教科のカリキュラムがそうさせているわけではない。教育における公共性が、義務教育であれば、自動的にそうさせていると考えるのは、楽観的すぎるといえる。departmental bulletin pape

Use of a new, miniaturized, low-cost spectral sensor to estimate and map the vineyard water status from a mobile platform

Optimizing the use of water and improving irrigation strategies has become increasingly important in most winegrowing countries due to the consequences of climate change, which are leading to more frequent droughts, heat waves, or alteration of precipitation patterns. Optimized irrigation scheduling can only be based on a reliable knowledge of the vineyard water status. In this context, this work aims at the development of a novel methodology, using a contactless, miniaturized, low-cost NIR spectral tool to monitor (on-the-go) the vineyard water status variability. On-the-go spectral measurements were acquired in the vineyard using a NIR micro spectrometer, operating in the 9001900 nm spectral range, from a ground vehicle moving at 3 km/h. Spectral measurements were collected on the northeast side of the canopy across four different dates (July 8th, 14th, 21st and August 12th) during 2021 season in a commercial vineyard (3 ha). Grapevines of Vitis vinifera L. Graciano planted on a VSP trellis were monitored at solar noon using stem water potential (s) as reference indicators of plant water status. In total, 108 measurements of s were taken (27 vines per date). Calibration and prediction models were performed using Partial Least Squares (PLS) regression. The best prediction models for grapevine water status yielded a determination coefficient of cross-validation (r2cv) of 0.67 and a root mean square error of cross-validation (RMSEcv) of 0.131 MPa. This predictive model was employed to map the spatial variability of the vineyard water status and provided useful, practical information towards the implementation of appropriate irrigation strategies. The outcomes presented in this work show the great potential of this low-cost methodology to assess the vineyard stem water potential and its spatial variability in a commercial vineyard

Combination of NIR multispectral information acquired from a ground moving vehicle with AI methods to assess the vine water status in a Tempranillo (Vitis vinifera L.) commercial vineyard

Increasing water scarcity and unpredictable rainfall patterns necessitate efficient water management in grape production. This study proposes a novel approach for monitoring grapevine water status in a commercial vertically-shoot-positioned Vitis vinifera L. Tempranillo vineyard using non-invasive spectroscopy with a battery of different AI methods to assess vineyard water status, that could drive precise irrigation. A contactless, miniature NIR spectrometer (900-1900 nm) mounted on a moving vehicle (3 Km/h) was employed to collect spectral data from the vines northeast side along six dates in season 2021.Grapevines were monitored at solar noon using stem water potential (s) as reference parameter of plant water status. At each date, 36 measurements of s were taken making a total of 396 data in the whole season. AI techniques, including linear regression, gaussian process regression (GPR) support vector machine (SVM), and neural networks, trained with Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG) algorithms were implemented in MATLAB (using the Regression Learner and Natural Net Fitting apps) to analyze the spectral data and predict vine water status. The optimized GPR model achieved the best performance, with a determination coefficient (R2P) above 0.83 and a root mean squared error of prediction (RMSEP) of 0.112 MPa. However, several neural network models trained with the LM algorithm exhibited superior performance, with R2P values over 0.92 and RMSEP values of approximately 0.080 MPa. This study demonstrates the potential of non-invasive spectroscopy combined with AI methods for accurate prediction of grapevine water status, paving the way for precision irrigation in vineyards

Assessing and mapping vineyard water status variability using a miniaturized nir spectrophotometer from a moving vehicle

Context and purpose of the study In the actual scenario of climate change, optimization of water usage is becoming critical in sustainable viticulture. Most of the current approaches to assess grapevine water status and drive irrigation scheduling are either destructive, time and labour consuming and monitor a small, limited number of plants. This work presents a novel methodology using a contactless, miniaturized, low-cost NIR spectrometer to monitor the vineyard water status variability from a moving vehicle, to provide reliable information towards precision irrigation.Material and methods Spectral measurements were acquired using a NIR micro spectrometer, operating in the 9001900 nm range, from a ground vehicle moving at 3 km/h. Spectra acquisition was carried out on the northeast side of the canopy across six dates in 2021 season and five dates in 2022, in two VSP commercial vineyards of Vitis vinifera L. Tempranillo and Graciano in the Rioja Appellation Board (Spain). Grapevines were monitored at solar noon using stem water potential (s) as reference indicator of plant water status. At each date, 36 and 27 measurements of s were taken in the Tempranillo and Graciano vineyards, making a total of 396 and 297 data respectively. Partial least squares (PLS) regression and the Variable Importance in the Projection (VIP) method were used to build calibration and prediction models using the pooled data from the two seasons for each variety. Multiple Linear Regression (MLR) was also applied to build simplified estimation models using 8 and 10 spectral bands with the highest VIP scores (always >1). Determination of coefficient (R2) and root mean square error (RMSE) were computed to assess model performance.Results Remarkable cross-validation models were built using the whole spectrum (117 wavelengths) with R2cv ranging from 0.62 to 0.80, and RMSECV between 0.115-0.138 MPa in Tempranillo and Graciano vineyards, respectively. With the aim of simplifying model building, the 8 and 10 spectral bands showing the highest VIP scores, with values above 1 in all instances, were selected to build MLR cross validation models of stem water potential. In both varieties MLR8 and MLR10 (MLR models built with 8 and 10 wavelenghts only respectively) yielded R2cv ranging from 0.45-0.59 and RMSECV ~ 0.156-0.171 MPa. Although lower performance was achieved with the simplified models they could still be utilized to classify and map the vineyard plots into three different water status zones, susceptible of precise, differentiated irrigation

Hyperspectral imaging application under field conditions: Assessment of the spatio-temporal variability of grape composition within a vineyard

This work illustrates how hyperspectral imaging, carried out from a moving ground vehicle, can be used under field conditions for assessing grape composition and evaluating its spatio-temporal variability within a vineyard. To analyse the distribution of the grape composition parameters, measurements were carried out at four different dates during the ripening season, at which total soluble solids (TSS) and anthocyanin concentrations analyses were also performed. Prediction models were generated for the two parameters, yielding 10-fold cross validation determination coefficients (R2) of 0.92 for TSS and 0.72 for anthocyanin concentrations (RMSEs of 1.444 °Brix and 0.272 mg/g berry, respectively). The results were used for mapping the evolution and distribution of grape composition in time-four dates-and space-the vineyard plot, illustrating the suitability of this technology for the in-field monitoring of the vineyard

On-the-go NIR spectroscopy and thermal imaging for assessing and mapping vineyard water status in precision viticulture.

New proximal sensing technologies are desirable in viticulture to assess and mapvineyard spatial variability. Towards this end, high-spatial resolution information can be obtainedusing novel, non-invasive sensors on-the-go. In order to improve yield, grape quality and watermanagement, the vineyard water status should be determined. The goal of this work was toassess and map vineyard water status using two different proximal sensing technologies on-thego: near infrared (NIR) reflectance spectroscopy and thermal imaging. On-the-go spectral andthermal measurements were acquired at solar noon, on east side of the canopy in a Tempranillo(Vitis vinifera L.) commercial vineyard. A spectrometer (1100-2100 nm) and thermal cameraoperating at 0.30 m and 1.20 m respectively from the canopy were mounted on a ATV whichmoved at 5 km/h. Midday stem water potential (s) was used as reference method. Spectral,thermal and physiological measurements were acquired over several dates from July toSeptember, in seasons 2015 and 2016. Partial least squares (PLS) was used as the algorithm forthe training of the water stress spectral prediction models. In the cross- validation, alldetermination coefficients (R2) were above the 0.89 marks for s. Moreover, canopy temperatureand the crop water stress index (CWSI) were correlated to stem water potential (s), with a R2value of 0.79. Vineyard water status was mapped using both near infrared reflectancespectroscopy and thermal imaging technologies and enabled the identification and delineation ofzones with homogeneous grapevine water status to steer precise and optimized irrigationschedules in the context of precision and sustainable viticulture. These results suggest that bothnear infrared reflectance spectroscopy and thermal imaging can be used to non-destructivelyassess and map the vine water status in commercial vineyards. In conclusions, both new sensingproximal technologies show the potential applicability for assessing and mapping of vineyardwater status in precision viticultur

Implementing VIS-NIR spectroscopy as a rapid and non-intrusive technique for assessing anthocyanin and phenolic concentrations in Vitis vinifera L. Grenache whole grape berries

Anthocyanins and phenolic compounds play a crucial role in winemaking, contributing to the profile, flavor, color, texture, and stability of wine. Grape clusters, specifically Vitis vinifera L. cv. Grenache, were handpicked from a commercial vineyard sited in Tudelilla, La Rioja, Spain (42°18 52.26, Long. -2°7 59.15, Alt. 582 m) on five distinct dates from veraison to harvest during the 2015 season. Non-contact spectral measurements were conducted on intact grape berries using a VIS-NIR spectrometer operating in the 570 1000 nm spectral range under controlled laboratory conditions, positioned at a distance of 25 cm from the berries. The quantification of 16 anthocyanins and phenols in 120 grape clusters was performed using HPLC, established as the reference method for validating the spectral tool. Data exploration and prediction of phenolic concentration in grape berries were conducted through Principal Component Analysis (PCA) and Modified Partial Least Squares (MPLS) regression. The best calibration and cross-validation models were built for total monomeric anthocyanins, nonacylated anthocyanins and cyanidin 3-glucoside with determination coefficients (R2cv values above 0.86, while the standard errors of cross validation (SECV) were 0.058 mg/g, 0.052 mg/g and 0.001 mg/g respectively. Of the other phenolic groups, the model for total flavanol yielded R2cv = 0.66 and SECV = 0.023 mg/g. This technology shows high potential for the selection and classification of berries throughout ripening in the vineyard or upon grape reception at the winery. Its application could help tailoring the oenological fate of grape berries to various wine qualities or styles

Hyperspectral imaging for the appraisal of varietal aroma composition along maturation in intact Vitis vinifera L. Tempranillo Blanco berries

Context and purpose of the study The knowledge of the grape aromatic composition during ripening provides very important information for winegrowers, who may carry out different viticultural practices, or determine the harvest date more accurately. However, there are currently no tools that allow this measurement to be carried out in a non-invasive and rapid way. For this reason, the aim of this work was to design a non-invasive methodology, based on hyperspectral imaging to estimate the aromatic composition and total soluble solids (TSS) of Tempranillo Blanco berries during ripening. Material and methods A total of 236 spectra of intact grape berries were acquired, under laboratory conditions, by hyperspectral imaging (HSI) in the visible + short wave near infrared (VIS+SW-NIR) range (400-1000 nm) to estimate the aromatic composition, and the TSS, of Vitis vinifera L. Tempranillo Blanco berries during ripening. Calibration, cross-validation and prediction models were built by partial least squares (PLS), using as reference the concentration of 20 grape berries volatile compounds (terpenoids, C13norisoprenoids, benzenoids, fatty acids, and C6 compounds), measured by gas chromatography mass spectrometry (GC-MS), and the concentration of total soluble solids (TSS), measured by refractometry. Results Values of determination coefficients of cross-validation (Rcv2) 0.70, were obtained for all terpenoids (-terpineol citral, linalool, and p-cymene), all C13 norisoprenoids (-damascenone and -ionone) and their total, all benzenoids (benzaldehyde, 2-phenylethanol, and benzyl alcohol) and their total, two fatty acids (octanoic acid, and nonanoic acid), four C6 compounds (2-hexenal, hexanal, 2-hexen-1-ol, and (Z)-3-hexen-1-ol) and their total, the sum of all families except C6 compounds (called as total positive compounds), and TSS. Therefore, it can be affirmed that hyperspectral imaging in the VIS+SW-NIR range could be a suitable tool to estimate the aromatic and industrial maturities of Tempranillo Blanco grape berries in a simultaneous, contactless and non-destructive way