경동맥의 치료 초음파 자극이 뇌의 생리적인 작용에 미치는 영향은 여전히 연구가 필요한 상황이다. 이에 본 연구 에서는 치료용 초음파를 이용한 비침습적이고, 정량적인 열자극 기법을 이용하여 경동맥의 자극을 통해 뇌의 생리적 인 기능을 개선하고 건강한 뇌 기능을 유지할 수 있는 방법을 제안하고자 하였다. 건강한 20대 성인 남녀 27명을 대상으로 연구를 진행하였다. 바로 누운 자세에서 진단용 초음파를 이용하여 우측 총경동맥(right common carotid artery, CCA)의 자극 위치를 확인하고, 우측 목빗근(sternocleidomastoid muscle)에 치료 초음파 자극을 적용하였다. 치료 초음파 자극은 치료용 초음파를 이용하여 3MHz의 주파수에서 총 2가지 강도 (5W/cm2 and 10W/cm2)에서 각각 2분 동안 중재하였다. 근적외선분광법(near-infrared spectroscopy, NIRS)을 이용하 여 대뇌산소포화도(regional cerebral oxygen saturation, rSO2) 및 헤모글로빈(hemoglobin, Hb) 농도의 변화를 측정 하여 비교하였으며, 추가적으로 강도에 따른 중재 구간별 변화를 비교 분석하였다. 두피와 대뇌피질 사이(Shallow) 영역에서 rSO2는 강도에 따라서 서로 증감이 다르게 나타났다. 즉, 5W/cm2에서는 중재전과 비교하여 중재중과 중재후에 모두 감소하는 경향을 보였고, 10W/cm2에서는 모두 증가하는 경향을 보였지 만, 모두 통계적으로 유의한 차이를 보이지는 않았다. 반면, 대뇌피질(Deep) 영역에서는 중재중에서 강도와 상관없이 통계적으로 유의한 감소를 보였고(p = .001 for 5W/cm2; p < .001 for 10W/cm2), 중재중과 비교하여 중재후에는 다시 중재전의 상태로 회복되는 경향을 보였다(p = .016 for 5W/cm2; p = .012 for 1 0W/cm2). 옥시헤모글로빈 (oxyhemoglobin, HbO)의 변화는 5W/cm2 자극에서 중재중에서만 유의한 증가를 보였고(p = .036), 디옥시헤모글로 빈(deoxyhemoglobin, HbR)은 감소하는 경향을 나타냈지만, 유의한 차이를 보이지는 않았다. 10W/cm2 자극에서 HbO와 HbR이 모두 감소하는 경향을 보였지만, 유의한 차이를 보이지 않았다. 강도에 따른 변화는 Shallow 영역에 서 rSO2가 유의한 차이를 보였다(중재중, p = .023; 중재후, p = .022). 결론적으로 3MHz의 주파수와 5W/cm2와 10W/cm2 강도로 중재를 수행하게 된다면, 중재중에 Deep 영역의 rSO2 의 감소를 야기한다는 것을 볼 수 있었다. 10W/cm2의 강한 강도의 자극에서는 Shallow 영역에서 rSO2가 증가하고, Deep 영역에서는 감소하는 것을 보여주었다. 이는 강한 강도의 자극에 의해 내경동맥의 혈류 증가로 인한 대뇌피질 에서의 효과적인 자극이 아니라 외경동맥의 혈류 증가로 인하여 Shallow 영역에서의 혈류 공급의 과다로 인한 현상 이 나타난 것으로 보여 진다. 반면, 대뇌에서 HbO는 5W/cm2의 강도에서 증가하였고, 이는 전반적인 산소 공급량이 높아졌음을 의미한다. 이에 적절한 강도의 치료용 초음파 자극을 사용한다면 기존에 알려져 있던 뇌혈류의 개선을 통하여 뇌혈관 건강을 개선하는 기능과 더불어 대뇌의 생리적인 기능을 조절하여 뇌기능의 개선에 기여할 수 있을 것으로 보인다.

A progressive muscle atrophy is strongly associated with aging, resulting in lower quality of life in elderly individuals. This study was conducted to determine relative hemoglobin (Hb) concentrations in the rabbit model of the sciatic nerve transection injury using non-invasive diffuse optical spectroscopy (DOS). From the 2nd week to the end of the experiment after sciatic nerve injury, a total muscle mass in nerve injured-group (NI group) significantly reduced compared with that in the normal group (p<0.001). During the capillary occlusion after nerve injury, the deoxy-hemoglobin (Hb-R) concentration in NI group significantly increased compared to that in the normal group at the 2nd and 3rd week after sciatic nerve injury (p<0.05). During the capillary release after nerve injury, the oxy-hemoglobin (Hb-O2) concentration in NI group significantly decreased at the 1st and 3rd week, and Hb-R significantly increased at 2nd week, compared to those in the normal group (p<0.05). Histological changes in the gastrocnemius muscle of NI group observed that clear fat filled spaces at the periphery of muscle fibers and angular fibers. From the results of this study, non-invasive DOS could be used to measure changes of Hb concentrations in muscles.

Near infrared reflectance spectroscopy (NIRS) is routinely used for the determination of nutrient components of forages. However, little is known about the impact of sample preparation and wavelength on the accuracy of the calibration to predict minerals. This study was conducted to assess the effect of sample preparation and wavelength of near infrared spectrum for the improvement of calibration and prediction accuracy of Calcium (Ca) and Phosphorus (P) in imported hay using NIRS. The samples were scanned in reflectance in a monochromator instrument (680–2,500 nm). Calibration models (n = 126) were developed using partial least squares regression (PLS) based on cross-validation. The optimum calibrations were selected based on the highest coefficients of determination in cross validation (R2) and the lowest standard error of cross-validation (SECV). The highest R2 and the lowest SECV were obtained using oven-dry grinded sample preparation and 1,100-2,500 nm wavelength. The calibration (R2) and SECV were 0.99 (SECV: 468.6) for Ca and 0.91 (SECV: 224.7) for P in mg/kg DM on a dry weight, respectively. Results of this experiment showed the possibility of NIRS method to predict mineral (Ca and P) concentration of imported hay in Korea for routine analysis method to evaluate the feed value.

The aim of this study was to investigate the feasibility of discrimination 12 different cultivar of sorghum × sudangrass hybrid (Sorghum genus) seed through near infrared spectroscopy (NIRS). The amount of samples for develop to the best discriminant equation was 360. Whole samples were applied different three spectra range (visible, NIR and full range) within 680-2500 nm wavelength and the spectrastar 2500 Near near infrared was used to measure spectra. The calibration equation for discriminant analysis was developed partial least square (PLS) regression and discrimination equation (DE) analysis. The PLS discriminant analysis model for three spectra range developed with mathematic pretreatment 1,8,8,1 successfully discriminated 12 different sorghum genus. External validation indicated that all samples were discriminated correctly. The whole discriminant accuracy shown 82 ~ 100 % in NIR full range spectra. The results demonstrated the usefulness of NIRS combined with chemometrics as a rapid method for discrimination of sorghum × sudangrass hybrid cultivar through seed.

In this study, whole crop rice samples were used to develop near-infrared reflectance (NIR) equations to estimate six forage quality parameters: Moisture, crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), Ash and relative feed value (RFV). A population of 564 whole crop rice representing a wide range in chemical parameters was used in this study. Undried finely chopped whole crop rice samples were scanned at 1 nm intervals over the wavelength range 680–2500 nm and the optical data recorded as log 1/Reflectance (log 1/R). NIRS calibrations were developed by means of partial least-squares (PLS) regression. The correlation coefficients of cross-validation (R2 cv) and standard error of cross-validation (SECV) for whole crop rice calibration were 0.98 (SECV 1.81%) for moisture, 0.89 (SECV 0.50%) for CP, 0.86 (SECV 1.79%) for NDF, 0.89 (SECV 0.86%) for ash, and 0.84 (SECV 5.21%) for RFV on a dry matter (%), respectively. The NIRS calibration equations developed in this study will be useful in predicting whole crop rice quality for these six quality parameters.

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid and accurate method of evaluating some chemical compositions in forages. The objective of this study was to evaluate the potential of NIRS, applied to imported forage, to estimate the moisture and chemical parameters for imported hays. A population of 392 imported hay representing a wide range in chemical parameters was used in this study. Samples of forage were scanned at 1 nm intervals over the wavelength range 680-2500nm and the optical data was recorded as log 1/Reflectance(log 1/R), which scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares(PLS) multivariate analysis in conjunction with spectral math treatments to reduced the effect of extraneous noise. The optimum calibrations were selected based on the highest coefficients of determination in cross validation(R2) and the lowest standard error of cross-validation(SECV). The results of this study showed that NIRS predicted the chemical parameters with very high degree of accuracy. The R2 and SECV for imported hay calibration were 0.92(SECV 0.61%) for moisture, 0.98(SECV 0.65%) for acid detergent fiber, 0.97(SECV 0.40%) for neutral detergent fiber, 0.99(SECV 0.06%) for crude protein and 0.97(SECV 3.04%) for relative feed value on a dry matter(%), respectively. Results of this experiment showed the possibility of NIRS method to predict the moisture and chemical composition of imported hay in Korea for routine analysis method to evaluate the feed value.

This study was carried out to explore the accuracy of near infrared spectroscopy(NIRS) for the prediction of moisture content and chemical parameters on winter annual forage crops. A population of 2454 winter annual forages representing a wide range in chemical parameters was used in this study. Samples of forage were scanned at 1nm intervals over the wavelength range 680-2500nm and the optical data was recorded as log 1/Reflectance(log 1/R), which scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares(PLS) multivariate analysis in conjunction with spectral math treatments to reduced the effect of extraneous noise. The optimum calibrations were selected based on the highest coefficients of determination in cross validation(R2) and the lowest standard error of cross-validation(SECV). The results of this study showed that NIRS calibration model to predict the moisture contents and chemical parameters had very high degree of accuracy except for barely. The R2 and SECV for integrated winter annual forages calibration were 0.99(SECV 1.59%) for moisture, 0.89(SECV 1.15%) for acid detergent fiber, 0.86(SECV 1.43%) for neutral detergent fiber, 0.93(SECV 0.61%) for crude protein, 0.90(SECV 0.45%) for crude ash, and 0.82(SECV 3.76%) for relative feed value on a dry matter(%), respectively. Results of this experiment showed the possibility of NIRS method to predict the moisture and chemical composition of winter annual forage for routine analysis method to evaluate the feed value.

Near infrared spectroscopy (NIRS) is a rapid and accurate method for analyzing the quality of cereals, and dried animal forage. However, one limitation of this method is its inability to measure fermentation parameters in dried and ground samples because they are volatile, and therefore, respectively lost during the drying process. In order to overcome this limitation, in this study, fresh coarse haylage was used to test the potential of NIRS to accurately determine chemical composition and fermentation parameters. Fresh coarse Italian ryegrass haylage samples were scanned at 1 nm intervals over a wavelength range of 680 to 2500 nm, and optical data were recorded as log 1/reflectance. Spectral data, together with first- and second-order derivatives, were analyzed using partial least squares (PLS) multivariate regressions; scatter correction procedures (standard normal variate and detrend) were used in order to reduce the effect of extraneous noise. Optimum calibrations were selected based on their low standard error of cross validation (SECV) values. Further, ratio of performance deviation, obtained by dividing the standard deviation of reference values by SECV values, was used to evaluate the reliability of predictive models. Our results showed that the NIRS method can predict chemical constituents accurately (correlation coefficient of cross validation, R2 cv, ranged from 0.76 to 0.97); the exception to this result was crude ash (R2 cv = 0.49 and RPD = 2.09). Comparison of mathematical treatments for raw spectra showed that second-order derivatives yielded better predictions than first-order derivatives. The best mathematical treatment for DM, ADF, and NDF, respectively was 2, 16, 16, whereas the best mathematical treatment for CP and crude ash, respectively was 2, 8, 8. The calibration models for fermentation parameters had low predictive accuracy for acetic, propionic, and butyric acids (RPD < 2.5). However, pH, and lactic and total acids were predicted with considerable accuracy (R2 cv 0.73 to 0.78; RPD values exceeded 2.5), and the best mathematical treatment for them was 1, 8, 8. Our findings show that, when fresh haylage is used, NIRS-based calibrations are reliable for the prediction of haylage characteristics, and therefore useful for the assessment of the forage quality.

This study was conducted to find out an alternative way of rapid and accurate analysis of chemical composition of permanent pastures in hilly grazing area. Near reflectance infrared spectroscopy (NIRS) was used to evaluate the potential for predicting proximate analysis of permanent pastures in a vegetative stage. 386 pasture samples obtained from hilly grazing area in 2015 and 2016 were scanned for their visible–NIR spectra from 400~2,400nm. 163 samples with different spectral characteristics were selected and analysed for moisture, crude protein (CP), crude ash (CA), acid detergent fiber (ADF) and neutral detergent fiber (NDF). Multiple linear regression was used with wet analysis data and spectra for developing the calibration and validation mode1. Wavelength of 400 to 2500nm and near infrared range with different critical T outlier value 2.5 and 1.5 were used for developing the most suitable equation. The important index in this experiment was SEC and SEP. The R2 value for moisture, CP, CA, CF, Ash, ADF, NDF in calibration set was 0.86, 0.94, 0.91, 0.88, 0.48 and 0.93, respectively. The value in validation set was 0.66, 0.86, 0.83, 0.71, 0.35 and 0.88, respectively. The results of this experiment indicate that NIRS is a reliable analytical method to assess forage quality for CP, CF, NDF except ADF and moisture in permanent pastures when proper samples incorporated into the equation development.