Penerapan Metode Eksperimen untuk Meningkatkan Konsep Dasar Sains pada Anak Didik Kelompok A Tk Pkk Suruhwadang Kecamatan Kademangan Kabupaten Blitar

Tujuan penelitian ini adalah untuk memperoleh tentang kemampuan kognitif anak dalamhal konsep dasar sains dengan menggunakan metode eksperimen pada anak didik kelompokA TK PKK Suruhwadang sebelum dan sesudah dilakukan tindakan. Melakukan tindakanberupa penerapan metode eksperimen untuk meningkatkan kemampuan kognitif dalamkonsep dasar sains pada anak didik kelompok A TK PKK Suruhwadang. Mengetahui adatidaknya perbedaan kemampuan konsep dasar sains dengan menggunakan metodeeksperimen pada anak didik kelompok A TK PKK Suruhwadang antara sebelum dan setelahdilakukan tindakan. Rumusan masalah pada penitilian ini adalah apakah metode eksperimendapat meningkatkan kemampuan pemahaman konsep dasar sains pada anak didik kelompokA TK PKK Suruhwadang Kecamatan Kademangan Kabupaten Blitar. Untuk menjawabrumusan masalah digunakan jenis penelitian tindakan kelas (PTK) dengan model Kemmisdan Taggart melalui empat tahapan yaitu tahap perencanaan , pelaksanaan, observasi danrefleksiyang dilalui dengan dua siklus. Teknik pengumpulan data menggunakan teknikobservasi dan dokumentasi. Adapun instrumen yang digunakan adalah lembar observasikegiatan anak dan lembar observasi pembelajaran oleh guru.Hasil penelitian menunjukanbahwa kemampuan kognitif anak kelompok A pada konsep dasar sain pada pra penelitianmenunjukkan prosentase 56.25%. Setelah pelaksanaan siklus I tentang bidang kemampuankognitif pada konsep dasar sains menunjukkan 59% mengalami peningkatan .Setelahpelaksanaan siklus ke II naik menjadi 83%. Hal ini menunjukkan pelaksanaan siklus ke IItelah mencapai kriteria ketuntasan dan membuktikan bahwa dengan metode eksperimendapat meningkatkan kemampuan kognitif dalam konsep dasar sains

Consensus network creation.

<p>Using the functional gene groups from the JAK-STAT signaling pathway, PubMed searches were used to calculate the number of occurrences for each gene and its aliases within publication abstracts. The top five most commonly observed members from each functional gene group were selected for use as members of the data set. The node numbers match each gene group to a column number within its prior knowledge matrix (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0186004#pone.0186004.s001" target="_blank">S1 File</a>).</p

Inferring gene and protein interactions using PubMed citations and consensus Bayesian networks

<div><p>The PubMed database offers an extensive set of publication data that can be useful, yet inherently complex to use without automated computational techniques. Data repositories such as the Genomic Data Commons (GDC) and the Gene Expression Omnibus (GEO) offer experimental data storage and retrieval as well as curated gene expression profiles. Genetic interaction databases, including Reactome and Ingenuity Pathway Analysis, offer pathway and experiment data analysis using data curated from these publications and data repositories. We have created a method to generate and analyze consensus networks, inferring potential gene interactions, using large numbers of Bayesian networks generated by data mining publications in the PubMed database. Through the concept of network resolution, these consensus networks can be tailored to represent possible genetic interactions. We designed a set of experiments to confirm that our method is stable across variation in both sample and topological input sizes. Using gene product interactions from the KEGG pathway database and data mining PubMed publication abstracts, we verify that regardless of the network resolution or the inferred consensus network, our method is capable of inferring meaningful gene interactions through consensus Bayesian network generation with multiple, randomized topological orderings. Our method can not only confirm the existence of currently accepted interactions, but has the potential to hypothesize new ones as well. We show our method confirms the existence of known gene interactions such as <i>JAK-STAT-PI3K-AKT-mTOR</i>, infers novel gene interactions such as <i>RAS- Bcl-2</i> and <i>RAS-AKT</i>, and found significant pathway-pathway interactions between the JAK-STAT signaling and Cardiac Muscle Contraction KEGG pathways.</p></div

Kappa coefficients across network resolutions for differing sample sizes.

<p>The minimum, maximum, and mean kappa coefficients for each pair of networks created with varying sample sizes. It can be seen that variations in the sample size inputs for Bayesian network construction results in similar consensus networks.</p

The JAK-STAT signaling KEGG pathway.

<p>The JAK-STAT signaling KEGG pathway shows the known interactions within the JAK-STAT signaling cascade.</p

Kappa coefficients across network resolutions for differing sample sizes.

<p>When comparing consensus networks of differing sample sizes, for each of the 10 tests cases, Cohen’s kappa coefficient was calculated for 10 network resolutions ranging from 0.1 to 1.0.</p

The Cardiac Muscle Contraction KEGG pathway.

<p>The Cardiac Muscle Contraction KEGG pathway shows how a ca2+ influx induces cardiac muscle contraction.</p

Removing bi-directionality from consensus networks.

<p>Bi-directionality is removed from directed consensus networks by adding the edge weights from both directions for each set of nodes in (A) and using an undirected edge with the new total in (B).</p

A consensus network at different resolutions.

<p>(A) The adjacency matrix for a consensus network created from 10 separate Bayesian networks. (B) The graph of the consensus network with a resolution of 0.1. (C) The graph of the consensus network with a resolution of 0.8. The resolution determines which edges to include by dividing the edge weight by the largest weight of any single edge in the network and removing those which do not exceed the cut-off.</p

Consensus network output.

<p>Mining genetic information from abstracts cited within PubMed, our method generated a consensus network using large numbers of Bayesian networks representing potential genetic interactions within the JAK-STAT signaling (blue nodes) and Cardiac Muscle Contraction (orange nodes) KEGG pathways. Gray nodes are randomly selected negative controls. This graph shows the consensus network at a resolution of 0.9. Edges between genes indicate an inferred relationship. Green lines indicate a conserved interaction between the consensus network and the KEGG pathway, red lines indicate a novel interaction within the pathway, and brown lines indicate pathway-pathway interactions.</p