The interpretation of Myocardial Infarction (MI) via electrocardiogram (ECG) signal is a challenging task. ECG signals' morphological view show significant variation in different patients under different physical conditions. Several learning algorithms have been studied to interpret MI. However, the drawback of machine learning is the use of heuristic features with shallow feature learning architectures. To overcome this problem, a deep learning approach is used for learning features automatically, without conventional handcrafted features. This paper presents sequence modeling based on deep learning with recurrent network for ECG-rhythm signal classification. The recurrent network architecture such as a Recurrent Neural Network (RNN) is proposed to automatically interpret MI via ECG signal. The performance of the proposed method is compared to the other recurrent network classifiers such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The objective is to obtain the best sequence model for ECG signal processing. This paper also aims to study a proper data partitioning ratio for the training and testing sets of imbalanced data. The large imbalanced data are obtained from MI and healthy control of PhysioNet: The PTB Diagnostic ECG Database 15-lead ECG signals. According to the comparison result, the LSTM architecture shows better performance than standard RNN and GRU architecture with identical hyper-parameters. The LSTM architecture also shows better classification compared to standard recurrent networks and GRU with sensitivity, specificity, precision, F1-score, BACC, and MCC is 98.49%, 97.97%, 95.67%, 96.32%, 97.56%, and 95.32%, respectively. Apparently, deep learning with the LSTM technique is a potential method for classifying sequential data that implements time steps in the ECG signal.Algorithms 2019, 12, 118 2 of 12 records electrical signals related to heart activity and producing a voltage-chart cardiac rate and being a cardiological test that has been used in the past 100 years [7]. ECG signals have three different waveforms for each cardiac cycle: P wave, QRS complex, and T wave in normal rate [8]. In other cases, ECG form changes in the T waveform, the ST interval length, and ST elevation. Its morphology causes a cardiac abnormality, i.e., Ischemic Heart Disease (IHD) [9]. The IHD is the single largest cause of the main contributors to the disease burden in developing countries [10]. The two leading manifestations of IHD are angina and Acute Myocardial Infarction (MI) [10]. Angina is the characteristic caused by atherosclerosis leading to stenosis of one or more coronary arteries. Then, MI occurs due to a lack of oxygen demand in the cardiac muscle tissue. If cardiac muscle activity increases, oxygen demand also increases [11]. MI is the most dangerous form of IHD with the highest mortality rate [10].MI is usually diagnosed by changes in the ECG due to the increase of serum enzymes, such as creatine phosphokinase and troponin T or I [10]. ECG is the most reliable tool for interpreting ...

Our effort to find new material for anti cancer from natural resources leads us to focus on stingless bee products such as honey, bee pollen, and propolis. The products were from seven stingless bees named Homotrigona fimbriata, Heterotrigona itama, Heterotrigona bakeri, Tetragonula sarawakensis, Tetragonula testaceitarsis, Tetragonula fuscobalteata, Tetragonula laeviceps. The stingless bee products were evaluated for their cytotoxicity effect on MCF-7, HeLa and Caco-2 cancer cell lines. This is the first time to be reported that the honey, ethanol extracts of bee pollen and propolis of H. fimbriata displayed more potent cytotoxicity than other stingless bee products. By chromatography and biological activity-guided fractionation, ethanol extract of propolis from H. fimbriata was fractionated and isolated its active compound named mangiferonic acid. Mangiferonic acid showed a cytotoxicity effect with IC 50 values 96.76 µM in MCF-7, >110.04 µM in HeLa, and > 110.04 µM in Caco-2, respectively. These results exhibited the potential of ethanol extracts from propolis of H. fimbriata to be further developed for drug and experiments to verify the function are essential.

Selama hamil, sebagian dari kebutuhan nutrient akan meningkat. Hal penting yang harus diperhatikan ibu hamil adalah makanan yang dikonsumsi terdiri dari susunan menu seimbang. Tujuan pengabdian masyarakat ini adalah untuk Meningkatkan pengetahuan ibu hamil  tentang Nutrisi kehamilan. Kegiatan ini dilakukan dengan memberikan penyuluhan dengan leaflet kepada ibu hamil tentang nutrisi kehamilan dengan memberikan pre tes sebelum penyuluhan dan post tes sesudah penyuluhan. Penyuluhan ini dilaksanakan di Klinik Kartika Husada Donomulyo Malamg. yang diikuti oleh 10 ibu hamil dengan menjalankan Protokol Kesehatan Covid 19. Hasil penyuluhan didapatkan Sebelum diberikan penyuluhan sebanyak 3 ibu hamil (30%) dengan pengetahuan Baik dan setelah kegiatan terdapat peningkatan menjadi 8 ibu hamil (80%) dengan penegtahuan Baik. Diharapkan untuk penyuluhan selanjutnya di fokuskan tentang Nutrisi Trimester I,II dan III pada Ibu Hamil.

Due to an increase in the production of green tea, the amount of leaf waste has increased enormously, causing serious environmental problems. With regard to environmental awareness, the possibility of reusing the waste leaves of green tea as a low‐cost and abundantly available source of natural dye for dyeing cotton fibres was investigated. Natural dye powder from the waste leaves of green tea (NDPT) was successfully applied to dye cotton fibres without mordant by batch experiments. NDPT was obtained as a dark brown powder with a yield of 2.7 ± 0.5% w/w from dried waste leaves of green tea. The optimal conditions for dyeing NDPT onto cotton fibres were: pH of dye solution, 3; material to liquor ratio, 100:1; dyeing time, 180 min; concentration of dye solution, 3.0 mg/ml; and dyeing temperature, 100 °C. The colour of cotton fibres dyed with NDPT was observed to be dark brown. The adsorption data of NDPT on cotton fibres was best fitted with a Langmuir adsorption isotherm model with a correlation coefficient (R2) of 0.997. It is clear that there is a strong possibility of reusing the waste leaves of green tea as a low‐cost and abundantly available source of natural dye for dyeing cotton fibres.