Phenology of fine roots and shoots using high frequency temporal resolution images in a temperate larch forest

Purpose Understanding tree phenology reveals the underlying mechanisms through plant functional and productive activities and carbon sinks in forest ecosystems. However, previous research on tree phenology has focused on shoot dynamics rather than tree root dynamics. We aimed to explore seasonal temperature patterns of daily-based root and shoot dynamics by capturing high frequency plant images in a larch forest. Methods We monitored continuous images using an automated digital camera for shoot dynamics and a flatbed scanner for the fine root dynamics in the larch. Using the images, we analyzed the relationship between temperature and plant area index as shoot growth status and total root-area proportion of white and brown roots. Results Larch shoot production had a single mountain-shaped peak with a positive correlation between plant area index and air temperature. Fine root production had two peaks in the bimodal root-growth pattern in early summer and late autumn. Soil temperature was positively correlated with white root proportion and negatively correlated with brown root proportion. Conclusion We found differences between shoots and roots regarding temperature relationships. In particular, the automated flatbed scanner method for the root dynamics allowed the collection of detailed bimodal patterns of root production with shift from whitening to browning color, which had been previously overlooked. Such high frequency temporal resolution analysis can provide an in-depth of mechanisms of fine-root and shoot phenology through different stages of plant development in terms of growth and senescence.

Recurrence monitoring for ovarian cancer using a cell phone-integrated paper device to measure the ovarian cancer biomarker HE4/CRE ratio in urine

Ovarian cancer has a poor cure rate and rates of relapse are high. Current recurrence detection is limited by non-specific methods such as blood testing and ultrasound. Based on reports that human epididymis four (HE4) / creatinine (CRE) ratios found in urine are elevated in ovarian cancers, we have developed a paper-based device that combines lateral flow technology and cell phone analysis to quantitatively measure HE4/CRE. Surrogate samples were used to test the performance over clinically expected HE4/CRE ratios. For HE4/CRE ratios of 2 to 47, the percent error was found to be 16.0% on average whether measured by a flatbed scanner or cell phone. There was not a significant difference between the results from the cell phone or scanner. Based on published studies, error in this method was less than the difference required to detect recurrence. This promising new tool, with further development, could be used at home or in low-resource settings to provide timely detection of ovarian cancer recurrence.

The Prediction of Chlorophyll Content in African Leaves (Vernonia amygdalina Del.) Using Flatbed Scanner and Optimised Artificial Neural Network

African leaves (Vernonia amygdalina Del.) is a nutrient-rich plant that has been widely used as a herbal plant. African leaves contain chlorophyll which identify compounds produced by a plant, such as flavonoids and phenols. Chlorophyll testing can be carried out non-destructively by using the SPAD 502 chlorophyll meter. However, it is quite expensive, so that another non-destructive method is developed, namely digital image analysis. Relationships between chlorophyll content and leaf image colour indices in the RGB, HSV, HSL, and Lab* space are examined. The objectives of this study are 1) to analyse the relationship between texture parameters of red, green, blue, grey, hue, saturation(HSL), lightness (HSL), saturation( HSV), value(HSV), L*, a*, and b* against the chlorophyll content in African leaves using a flatbed scanner (HP DeskJet 2130 Series); and 2) built a model to predict chlorophyll content in African leaves using optimised ANN through a feature selection process by using several filter methods. The best ANN topologies are 10-30-40-1 (10 input nodes, 40 nodes in hidden layer 1, 30 nodes in hidden layer 2, and 1 output node) with a trainlm on the learning function, tansig on the hidden layer, and purelin on the output layer. The selected topology produces MSE training of 0.0007 with R training 0.9981 and the lowest validation MSE of 0.012 with R validation of 0.967. With these results, it can be concluded that the ANN model can be potentially used as a model for predicting chlorophyll content in African leaves.

Effect of lacto-fermentation and freeze-drying on the quality of beetroot evaluated using machine vision and sensory analysis

This study was aimed at evaluating the effect of freeze-drying and lacto-fermentation on the texture parameters of images and sensory attributes of beetroots. The samples were imaged using a flatbed scanner, and textures from images converted to color channels L, a, b, R, G, B, X, Y, Z were computed. The discrimination of raw and processed beetroots was performed using models based on textures selected for each color channel. The sensory quality of processed samples was determined using the attributes related to smell, color, texture and taste. The highest discrimination accuracy of 97.25% was obtained for the model built for color channel b. The accuracies for other channels were equal to 96.25% for channel a, 95.25% for channel R, 95% for channel Y, 94.75% for channel B, 94.5% for channel X, 94% for channel L, 92.5% for channel G, 88.25% for channel Z. In the case of some models, the raw and lacto-fermented beetroots were discriminated with 100% correctness. The freeze-dried and freeze-dried lacto-fermented samples were also the most similar in terms of sensory attributes, such as off-odor, attractiveness color, beetroot color, crunchiness, hardness, bitter taste, overall quality. The results indicated that the image parameters and sensory attributes may be related.

A protocol for accurate radiochromic film dosimetry using Radiochromic.com

Background Radiochromic films have many applications in radiology and radiation therapy. Generally, the dosimetry system for radiochromic film dosimetry is composed of radiochromic films, flatbed scanner, and film analysis software. The purpose of this work is to present the effectiveness of a protocol for accurate radiochromic film dosimetry using Radiochromic.com as software for film analysis. Materials and methods Procedures for image acquisition, lot calibration, and dose calculation are explained and analyzed. Radiochromic.com enables state-of-the-art models and corrections for radiochromic film dosimetry, such as the Multigaussian model for multichannel film dosimetry, and lateral, inter-scan, and re-calibration corrections of the response. Results The protocol presented here provides accurate dose results by mitigating the sources of uncertainty that affect radiochromic film dosimetry. Conclusions Appropriate procedures for film and scanner handling in combination with Radiochromic.com as software for film analysis make easy and accurate radiochromic film dosimetry feasible.

A validated quantification of triclosan in toothpaste using high-performance thin-layer chromatography and a 48-bit fatbed scanner

We present a densitometric quantification method for triclosan in toothpaste, separated by high-performance thin-layer chromatography (HPTLC) and using a 48-bit flatbed scanner as the detection system. The sample was band-wise applied to HPTLC plates (10 × 20 cm), with fluorescent dye, Merck, Germany (1.05554). The plates were developed in a vertical developing chamber with 20 min of chamber saturation over 70 mm, using n-heptane–methyl tert-butyl ether–acetic acid (92:8:0.1, V/V) as solvent. The RF value of triclosan is hRF = 22.4, and quantification is based on direct measurements using an inexpensive 48-bit flatbed scanner for color measurements (in red, green, and blue) after plate staining with 2,6-dichloroquinone-4-chloroimide (Gibbs' reagent). Evaluation of the red channel makes the measurements of triclosan very specific. For linearization, an extended Kubelka–Munk expression was used for data transformation. The range of linearity covers more than two orders of magnitude and is between 91 and 1000 ng. The separation method is inexpensive, fast and reliable.

IMPLEMENTATION OF CLOSE RANGE PHOTOGRAMMETRY USING MODERN NON-METRIC DIGITAL CAMERAS FOR ARCHITECTURAL DOCUMENTATION

The development of applied geodetic techniques for mapping and documentation of historical structures, buildings and sites is an important and vital purpose for contribution of any recording of cultural heritage for any country such as Egypt. This is done to preserve and restore any valuable architectural or other cultural monument, as a support to architectural, archaeological and other art-historical research throughout the ages. The purpose of this paper is to use close range photogrammetry technique (CRP) to reconstruct 3D model of architectural and historical mosque facade and comparing the accuracy of using digital commercial non-metric cameras with different resolutions and metric camera with flatbed scanner and photogrammetric scanner for architectural building documentation. El-Nasr Mosque façade in Mansoura city, Egypt was chosen as a case study in this paper. At first, twenty five points were selected at mosque façade at different elevations and distributed at different façade surfaces and observed using total station. Some of these points were selected as control points and the others were selected as check points to validate the results. Effect of control point’s number on image processing and analysis is also studied. Three cameras positions were selected for imaging to get the full details of mosque façade. Close range Digital Workstation (CDW) program was used for processing and analysis of multiple images. The results are indicated that close range photogrammetry using metric camera with photogrammetry scanner instead of flatbed scanner in technique is accurate enough to be beneficial in 3D architectural building documentation. Digital cameras with CRP technique give up different accuracy that depends mainly on the resolution of cameras and camera specifications.

Identifikasi Citra Beras Menggunakan Algoritma Multi-SVM Dan Neural Network Pada Segmentasi K-Means

Indonesia is a country with high rice needs because it is a staple food for more than 90% of populations. High demand requires high stock so imports are carried out in accordance with Permendagri Number 19/M-DAG/PER/3/2014 which explains rice import standards. There are many types of rice imported into Indonesia with various quality, color and import requirements such as for health or price stabilization. In terms of colors, imported white rice is the most consumed rice by Indonesians. One example is jasmine rice from Thailand. Meanwhile, in terms of imports, both for health and stabilizing the price of japonica rice (Japan) and Basmati (Pakistan) are the most imported to Indonesia. But there are still many who are not familiar with those three rices. In this research, the three types of rice were identified by comparing the Multi-SVM algorithm and Neural Network algorithm. Image acquisition is done using a flatbed scanner which produces 90 images divided into 63 training images and 27 testing images. K-Means becomes an image segmentation method and image binary converts. Feature extraction using morphological features with the regionprop method combined with the Gray Level Co-Occence Matrix (GLCM) produces 9 features that can produce 96.296% accuracy for Multi-SVM and 88.89% Neural Network