Penggunaan Media Gambar untuk Meningkatkan Aktivitas Murid pada Pembelajaran Ilmu Pengetahuan Sosial di SD

This Research can be useful to give the ways to increase Social Studies learning activities. Research method used is descriptive method, the classroom research activities and is a collaborative effort. This research will be done in 2 cycles. Data research results by ability assessment teachers in planning a lesson for the cycle I average 3.80 and in the cycle II average 4. Assessment at the ability of teachers in order to carry out a lesson for the average cycle I 3.89 and in the cycle II average 3.98. The students learning activities percentage at cycle I for physical activity 86 percent, such mental 64 percent, such emotional 78.67 percent and in the cycle II for physical activity 94%, such mental 77 percent, and such emotional 88 percent. Students' achievement assessment obtained in the cycle I average 72.24 and in the cycle II average 81.2. The conclusions from this research is using the images media may increase the activity in Social Studies of the students grade V at Primary School 12 East Pontianak sub-district

Peningkatan Kemampuan Menulis Puisi Anak Menggunakan Model Pembelajaran Concept Sentence Berbantuan Media Gambar Berseri di Sekolah Dasar

: The purpose of this research is to describe the increase of student's ability to write children poetry in Indonesian subject using concept sentence learning model assisted image series media on class VC SD Muhammadiyah 2 Pontianak. The method used in this research is descriptive method with the type of research is classroom action research and the characteristic is collaborative research. The Subjects of this research are 34 students of VC class SD Muhammadiyah 2 Pontianak. The results obtained in this research are the ability of teachers to design learning on the first cycle with an average score of 3.07 increased to 3.48 on the second cycle . The ability of teachers to implement the learning on the first cycle with an average score of 2.98 increased to 3.56 on the second cycle . Assessment result of student's writing poetry on the first cycle with an average score is 17 and it increased on the second cycle to 29. Because of that, the concept sentence learning model assisted image series media can improve students' ability to write children poetry

Potent cytotoxicity of an antihuman transferrin receptor-ricin A-chain immunotoxin on human glioma cells in vitro

The cytotoxic effects of an antihuman transferrin receptor monoclonal antibody-ricin A-chain conjugate (anti-TfR-A) immunotoxin on glioma cells were assessed in vitro. Five human glioma cell lines were studied; three were derived from surgical explants (MG-1, MG-2, MG-3) and two were well characterized established glioma cells (U-87 MG, U-373 MG). The C6 rat glioma line served as a nonhuman control. One of six lines (U-373) expressed glial fibrillary acidic protein, as assessed by immunohistochemistry. All five human lines expressed human transferrin receptor, as assessed by flow cytometry; no human transferrin receptor was demonstrable on rat C6 cells. Potent inhibition of protein synthesis was found after an 18-h incubation with anti-TfR-A. Fifty % inhibitory concentration (IC50) values for human glioma cells ranged from 1.9 X 10(-9) to 1.8 X 10(-8) M. In contrast, no significant inhibition of leucine incorporation was observed when anti-TfR-A was tested on rat cells (IC50 greater than 10(-7) M) or when a control immunotoxin directed against carcinoembryonic antigen was substituted for anti-TfR-A on human glioma cells (IC50 greater than 10(-7) M). Coincubation with the carboxylic ionophore monensin (10(-7) M) decreased the IC50 of anti-TfR-A against human glioma lines from 16- to 842-fold (range, 7.0 X 10(-12) to 1.5 X 10(-10) M). In contrast, an IC50 of greater than 10(-7) M was obtained when C6 cells were incubated with anti-TfR-A and monensin. Anti-TfR-A immunotoxins potentiated by monensin are extremely potent in vitro cytotoxins for human glioma cells

The effect of large-decoherence on mixing-time in Continuous-time quantum walks on long-range interacting cycles

In this paper, we consider decoherence in continuous-time quantum walks on long-range interacting cycles (LRICs), which are the extensions of the cycle graphs. For this purpose, we use Gurvitz's model and assume that every node is monitored by the corresponding point contact induced the decoherence process. Then, we focus on large rates of decoherence and calculate the probability distribution analytically and obtain the lower and upper bounds of the mixing time. Our results prove that the mixing time is proportional to the rate of decoherence and the inverse of the distance parameter (\emph{m}) squared. This shows that the mixing time decreases with increasing the range of interaction. Also, what we obtain for \emph{m}=0 is in agreement with Fedichkin, Solenov and Tamon's results \cite{FST} for cycle, and see that the mixing time of CTQWs on cycle improves with adding interacting edges.Comment: 16 Pages, 2 Figure

Systolic blood pressure, chronic obstructive pulmonary disease and cardiovascular risk

\ua9 2023 Author(s) (or their employer(s)). Objective: In individuals with complex underlying health problems, the association between systolic blood pressure (SBP) and cardiovascular disease is less well recognised. The association between SBP and risk of cardiovascular events in patients with chronic obstructive pulmonary disease (COPD) was investigated. Methods: and analysis In this cohort study, 39 602 individuals with a diagnosis of COPD aged 55-90 years between 1990 and 2009 were identified from validated electronic health records (EHR) in the UK. The association between SBP and risk of cardiovascular end points (composite of ischaemic heart disease, heart failure, stroke and cardiovascular death) was analysed using a deep learning approach. Results: In the selected cohort (46.5% women, median age 69 years), 10 987 cardiovascular events were observed over a median follow-up period of 3.9 years. The association between SBP and risk of cardiovascular end points was found to be monotonic; the lowest SBP exposure group of <120 mm Hg presented nadir of risk. With respect to reference SBP (between 120 and 129 mm Hg), adjusted risk ratios for the primary outcome were 0.99 (95% CI 0.93 to 1.05) for SBP of <120 mm Hg, 1.02 (0.97 to 1.07) for SBP between 130 and 139 mm Hg, 1.07 (1.01 to 1.12) for SBP between 140 and 149 mm Hg, 1.11 (1.05 to 1.17) for SBP between 150 and 159 mm Hg and 1.16 (1.10 to 1.22) for SBP ≥160 mm Hg. Conclusion: Using deep learning for modelling EHR, we identified a monotonic association between SBP and risk of cardiovascular events in patients with COPD

Loss of cell viability is tracked by decreased cytoplasmic conductivity

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PENGGUNAAN MEDIA GAMBAR UNTUK MENINGKATKAN AKTIVITAS MURID PADA PEMBELAJARAN ILMU PENGETAHUAN SOSIAL DI SD

Penelitian ini bermanfaat untuk memberikan kontribusi cara meningkatkan aktivitas pembelajaran Ilmu Pengetahuan Sosial. Metode penelitian yang digunakan adalah metode deskriptif, bentuk penelitian tindakan kelas dan bersifat kolaboratif. Penelitian ini dilakukan sebanyak 3 siklus. Data hasil penelitian diperoleh penilaian kemampuan guru dalam merencanakan pembelajaran pada siklus I Rata-rata 3,80,  pada siklus II Rata-rata 38,9 dan pada siklus III Rata-rata 4,0. Penilaian kemampuan guru dalam melaksanakan pembelajaran pada siklus I Rata-rata 3,89, pada siklus II Rata-rata 3,98 dan pada siklus III Rata-rata 4,0. Persentase aktivitas pembelajaran murid pada siklus I untuk aktivitas fisik 86%, aktivitas mental 64%, aktivitas emosional 78,67%, pada siklus II untuk aktivitas fisik 86%, aktivitas mental 72% dan aktivitas emosional 81% dan pada siklus III untuk aktivitas fisik 94%, aktivitas mental 77%, dan aktivitas emosional 88%. Penilaian hasil belajar murid diperoleh pada siklus I Rata-rata 72,24, pada siklus II Rata-rata 81,2 dan pada siklus III Rata-rata 82,32%. Simpulan dari penelitian ini adalah penggunaan media gambar dapat meningkatkan aktivitas pembelajaran Ilmu Pengetahuan Sosial pada murid kelas V Sekolah Dasar Negeri 02 Sebujit Bengkayang.Kata kunci : Aktivitas pembelajaran, pembelajaran Ilmu Pengetahuan Sosial,   media gambarAbstract: This Research can be useful to give the ways to increase Social Studies learning activities. Research method used is descriptive method, the classroom research activities and is a collaborative effort. This research will be done in 2 cycles. Data research results by ability assessment teachers in planning a lesson for the cycle I average 3.80 and in the cycle II average 4. Assessment at the ability of teachers in order to carry out a lesson for the average cycle I 3.89 and in the cycle II average 3.98. The students learning activities percentage at cycle I for physical activity 86 percent, such mental 64 percent, such emotional 78.67 percent and in the cycle II for physical activity 94%, such mental 77 percent, and such emotional 88 percent. Students' achievement assessment obtained in the cycle I average 72.24 and in the cycle II average 81.2. The conclusions from this research is using the images media may increase the activity in Social Studies of the students grade V at Primary School 12 East Pontianak sub-district.Keywords: Learning Activities, Social Studies, Images Medi

Hi-BEHRT: Hierarchical Transformer-Based Model for Accurate Prediction of Clinical Events Using Multimodal Longitudinal Electronic Health Records

\ua9 2022 IEEE. Electronic health records (EHR) represent a holistic overview of patients\u27 trajectories. Their increasing availability has fueled new hopes to leverage them and develop accurate risk prediction models for a wide range of diseases. Given the complex interrelationships of medical records and patient outcomes, deep learning models have shown clear merits in achieving this goal. However, a key limitation of current study remains their capacity in processing long sequences, and long sequence modelling and its application in the context of healthcare and EHR remains unexplored. Capturing the whole history of medical encounters is expected to lead to more accurate predictions, but the inclusion of records collected for decades and from multiple resources can inevitably exceed the receptive field of the most existing deep learning architectures. This can result in missing crucial, long-term dependencies. To address this gap, we present Hi-BEHRT, a hierarchical Transformer-based model that can significantly expand the receptive field of Transformers and extract associations from much longer sequences. Using a multimodal large-scale linked longitudinal EHR, the Hi-BEHRT exceeds the state-of-the-art deep learning models 1% to 5% for area under the receiver operating characteristic (AUROC) curve and 1% to 8% for area under the precision recall (AUPRC) curve on average, and 2% to 8% (AUROC) and 2% to 11% (AUPRC) for patients with long medical history for 5-year heart failure, diabetes, chronic kidney disease, and stroke risk prediction. Additionally, because pretraining for hierarchical Transformer is not well-established, we provide an effective end-to-end contrastive pre-training strategy for Hi-BEHRT using EHR, improving its transferability on predicting clinical events with relatively small training dataset

Validation of risk prediction models applied to longitudinal electronic health record data for the prediction of major cardiovascular events in the presence of data shifts

\ua9 2022 The Author(s). Published by Oxford University Press on behalf of the European Society of Cardiology. Aims: Deep learning has dominated predictive modelling across different fields, but in medicine it has been met with mixed reception. In clinical practice, simple, statistical models and risk scores continue to inform cardiovascular disease risk predictions. This is due in part to the knowledge gap about how deep learning models perform in practice when they are subject to dynamic data shifts; a key criterion that common internal validation procedures do not address. We evaluated the performance of a novel deep learning model, BEHRT, under data shifts and compared it with several ML-based and established risk models. Methods and results: Using linked electronic health records of 1.1 million patients across England aged at least 35 years between 1985 and 2015, we replicated three established statistical models for predicting 5-year risk of incident heart failure, stroke, and coronary heart disease. The results were compared with a widely accepted machine learning model (random forests), and a novel deep learning model (BEHRT). In addition to internal validation, we investigated how data shifts affect model discrimination and calibration. To this end, we tested the models on cohorts from (i) distinct geographical regions; (ii) different periods. Using internal validation, the deep learning models substantially outperformed the best statistical models by 6%, 8%, and 11% in heart failure, stroke, and coronary heart disease, respectively, in terms of the area under the receiver operating characteristic curve. Conclusion: The performance of all models declined as a result of data shifts; despite this, the deep learning models maintained the best performance in all risk prediction tasks. Updating the model with the latest information can improve discrimination but if the prior distribution changes, the model may remain miscalibrated