3 research outputs found
IDENTIFIKASI EKSPRESI GEN MIOGLOBIN PADA SEL PUNCA MESENKIMAL
Get PDFMioglobin (Mb) merupakan protein yang bertindak sebagai
penyimpan oksigen untuk membantu menjaga persediaan oksigen
jaringan, ditemukan dalam jumlah besar pada otot jantung dan otot
rangka. Penelitian terbaru menunjukkan bahwa mioglobin juga ditemukan
ekspresinya dijaringan non muskuler, yaitu di paru tikus dan otak penyu
hijau pada keadaan normoksia. Hal tersebut melatar belakangi penelitian
identifikasi ekspresi mioglobin pada sel punca mesenkimal, yaitu sel yang
nantinya akan terdiferensiasi menjadi jaringan non muskuler. Metode
penelitian ini adalah deskriptif menggunakan teknik two step RT-PCR dan
metode Taqman Probe untuk mengidentifikasi ekspresi gen target
mioglobin dan gen HIF (Hypoxia include factor) sebagai penanda keadaan
oksigen pada sampel dan Housekeeping Gene Betha Actin sebagai
kontrol teknik pekerjaan. Penelitian ini dilakukan pada bulan Maret- Mei
2015 di Laboratorium Biokimia dan Biologi Molekuler Jurusan Biologi,
FMIPA - UNJ dan Laboratorium Biologi Oral FKG - UI. Gen mioglobin,
HIF, dan Betha actin terekspresi pada sel punca mesenkimal. Hasil
penelitian menunjukkan bahwa gen mioglobin terekspresi pada sel punca
mesenkimal hasil preservasi cryogenik deep freezer -80 °C disertai
dengan ekspresi gen HIF 1α yang menunjukkan bahwa sel punca
mesenkimal berada pada keadaan hipoksia.
Myoglobin is a protein that act as the depositary of oxygen to help
maintain the supply of oxygen for the tissue, found in large numbers upon
the cardiac muscle and skeletal muscles. Recent study shows that
myoglobin also found its expression in non-muscular tissue, in pulmonary
of rat and green turtles brains on normoksia circumstances. It triggered the
study to indentify the expression of Myoglobin on the stem cell that will be
differentiated become non-muscular tissue. The research methodology
was descriptive with two step RT-PCR technic and Taqman Probe method
to identify the Myoglobin expression gene target and HIF gene (Hypoxia
include factor) as the oxygen indicatoron the sample and Housekeeping
Gene Betha Actinas controlling for technical of the study. The studywas
heldon March - May 2015 at Biochemistry Laboratory – Biology University
of Jakarta and Laboratory Biology Oral FKG University of Indonesia.
Myoglobin gene, HIF, and Betha actin be expressed on stem cell. The
results showed that the myoglobin gene expressed in mesenchymal stem
cells preservation results cryogenik deep freezer -80 ° C accompanied by
the expression of HIF-1α gene which indicates that the mesenchymal stem
cells that are in a state of hypoxia
Penerapan CNN Untuk Klasifikasi Tingkat Kematangan Berry Dengan Augmentasi Transformasi Dan Colorjitter Menggunakan Tensorflow
Get PDFWith the increasing consumer demand for fresh and high-quality fruits, automated recognition of fruit ripeness becomes crucial to enhance the quality and efficiency in fruit production. This research focuses on the implementation of Convolutional Neural Network (CNN) with Transformation Augmentation and Color Jitter using TensorFlow to classify the ripeness level of berries through images. The dataset consists of images of berries that have undergone pre-processing steps, including resizing images to 224x224 pixels, horizontal flipping, a rotation range of 20 degrees, zoom range of 0.3, shear range of 0.3, height shift range of 0.3, width shift range of0.3, random hue of 0.08, random saturation of 0.6-1.6, random brightness of 0.05, and random contrast of 0.7-1.3. The network architecture comprises 4 convolution layers, 4 max-pooling layers, a global average pooling layer, and a dense layer. The results show that the CNN method with Transformation Augmentation and Color Jitter using TensorFlow can achieve good accuracy in classifying the ripeness of berries. After 500 epochs of training, the model achieved an accuracy of 93.75% on the training data and 90.33% on the testing data. The model also achieved 100% accuracy on 30 new data samples that were different from the training and testing data. Additionally, the use of Transformation Augmentation and Color Jitter in CNN has a positive impact on the accuracy of ripeness classification of berries, with the augmented model showing higher accuracy
