Penggunaan Media Model dalam Pembelajaran IPA

This study aims to describe the increase in interest and student learning outcomes in science teaching in primary schools. Classroom action research was conducted in two cycles, each cycle consisted of two meetings with the various forms of energy and material use. Research subjects elementary school students grade IV No 20 Gunung Pangilun Padang Utara. The instrument of this study is the observation sheet student\u27s interests, learning activities observation sheets, and achievement test students\u27 interest in learning the instrument. The results showed that students\u27 interest in learning science in one cycle is 64.4 percent, and in the second cycle of 82.2 percent. Student learning outcomes in a single cycle on average 63.5 and 83.5 in the two cycle becomes. Besides that, it also revealed that an increasing mastery learning students from one cycle is 45.8 percent and in the second cycle of 91.6 percent. Analysis of teachers in implementing learning activities in a cycle that is 79.1 per cent and 91.6 per cent of the second cycle. The use of models in the media can increase interest in science learning, learning outcomes and teacher activities . Therefore, the model can be used medium primary school teachers as one of the media in learning science . Besides, teachers also need to make a good plan in accordance with the science curriculum in elementary schools

Overview of the Procedure

<div><p>(Top Panel) The four major steps used to define the PHN-Families are shown. The blue-shaded boxes (left and lower right) indicate the automated steps of the algorithm that, starting from a set of protein sequences, lead to the PHN-Families definition: i) generation of the network, ii) partitioning of the network for various cutoff values, iii) selection of the optimal cutoff. Values specific to the system analyzed in this study are shown on a gray background within the three boxes. The tan box (upper right) summarizes the investigation of the network topology. Since the PHN structure does not change upon addition of new sequences, this step does not need to be repeated when the sequence dataset is updated.</p><p>(A,B) A graphical visualization ([<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0020173#pcbi-0020173-b031" target="_blank">31</a>], see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0020173#pcbi-0020173-sd001" target="_blank">Protocol S1</a>) of the PHN in proximity to the VirB1 proteins is shown. (A) shows the results before partitioning, and (B) shows the results after partitioning. Different colors indicate different PHN-Families.</p><p>doi:10.1371/journal.pcbi. 0020173.g001</p></div

PHN-Family Based Profiling of T3SS and T4SS

<div><p>Multiprotein complexes can be classified by considering the presence or absence of part of their components, or their specific variants. Rows represent different T3SS (A) and T4SS (B); columns represent protein functional classes. Different colors identify different PHN-Families. Empty squares indicate absent proteins, while conserved proteins are shown in gray. Two external reference systems (E. coli flagellar apparatus and a Tra/Trb conjugative system) are marked in bold. The dendrograms represent a hierarchical clustering of the data (for further details and a list of the proteins, see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0020173#pcbi-0020173-sd001" target="_blank">Protocol S1</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0020173#pcbi-0020173-st003" target="_blank">Table S3</a>, and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0020173#pcbi-0020173-st004" target="_blank">Table S4</a>) that highlights the presence of four major groups (roman numbers) both in T3SS and T4SS.</p><p>doi:10.1371/journal.pcbi. 0020173.g008</p></div

Overlap and PHN-Families

<div><p>By partitioning the network with the overlap procedure for increasing value of θ<i>,</i> we separated the PHN into regions of increasing compactness. The maximum value of the modularity measure <i>Q</i> (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0020173#s4" target="_blank">Materials and Methods</a>) allowed us to identify the optimal cutoff value to partition the PHN into families of homologous proteins.</p><p>(Main Graph) <i>Q</i> is shown as a function of θ<i> </i>. The maximum value of <i>Q</i> = 0.723 is found for θ<i> </i> = 0.5.</p><p>(Inset Graph) The dark circles represent the compactness index <i>η</i> after the partitioning (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0020173#s4" target="_blank">Materials and Methods</a>) as a function of θ<i> </i>. The white triangle is the value of <i>η</i> of the original PHN for <i>ɛ</i> = 10⁻⁵, which corresponds to the limiting value θ<i> </i> = 0.</p><p>doi:10.1371/journal.pcbi. 0020173.g005</p></div

The Protein Homology Network

<div><p>A representation of a few of the largest connected components of the PHN for <i>ɛ</i> = 10⁻³⁰ (39,321 nodes, 4.4 × 10⁶ links, see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0020173#pcbi-0020173-sd001" target="_blank">Protocol S1</a>). Points represent proteins, and links represent sequence homology relationships with a BLAST E-value smaller than <i>ɛ</i>. The modular structure of the PHN is clearly visible in the figure, where many tightly connected groups of proteins appear to be linked together to form globally sparse connected components. By increasing the value of <i>ɛ,</i> the number of links wiring the network grows, causing many smaller components to coalesce into a single giant cluster. Moreover, many of the sparse points of the figure, which appear not to belong to any compact cluster, also join some compact region, increasing the network modularity.</p><p>doi:10.1371/journal.pcbi. 0020173.g002</p></div

VirB3 PHN-Families Phylogeny

<div><p>The PHN-Families of nonconserved genes correlate with their molecular phylogeny. Shown here is the Maximum Likelihood tree of the 33 VIRB3 proteins classified in three PHN-Families (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0020173#pcbi-0020173-st001" target="_blank">Table S1</a>). PHN-Families are enclosed in circles, color-coded as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0020173#pcbi-0020173-g008" target="_blank">Figure 8</a>, and coincide with monophyletic branches of the phylogenetic tree. Numbers are bootstrap values, and the ruler shows the number of point-accepted mutations.</p><p>doi:10.1371/journal.pcbi. 0020173.g007</p></div

SctJ PHN-Family: Network and Phylogeny

<div><p>In this example we show a representation of a single PHN-family, compared with a reconstruction of the evolutionary history of its components based on molecular phylogenetic data. The two subgroups clearly visible in the PHN representation coincide with monophyletic clades of the phylogenetic tree.</p><p>(A) Network representation of the SctJ PHN-family (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0020173#pcbi-0020173-sd001" target="_blank">Protocol S1</a>). Spheres represent proteins; edges are homology relations, color-coded according to the homology level <i>ɛ_ij</i>. The two subgroups are YscJ (T3SS) and FliF (flagellar) proteins. For <i>ɛ</i> = 10⁻⁵, this portion of the PHN falls in the giant component, for the presence of false homology relations with seven outlier proteins (blue spheres, external links to the giant component not shown). After the overlap procedure with θ<i> </i> = 0.5, false links are removed, and all the members of the SctJ family fall in a single PHN-family, shown by the circle.</p><p>(B) Maximum likelihood phylogenetic tree of the SctJ family. Numbers are bootstrap values. The YscJ and FliF subgroups correspond to two distinct evolutionary clades. Organism and group names in the T3SS clade refer to the T3SS classification shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0020173#pcbi-0020173-g008" target="_blank">Figure 8</a>.</p><p>doi:10.1371/journal.pcbi. 0020173.g006</p></div

PHN Topology

<div><p>The compactness index, <i>η,</i> and the clustering index, <i>C,</i> shown here as a function of the E-value cutoff <i>ɛ,</i> describe the global and local topology of the network, respectively. For growing values of <i>ɛ, η</i> rapidly decreases towards 0, while <i>C</i> always has values well above 0.8. These results indicate that the PHN is formed by compact regions that are loosely connected to form globally sparse connected components.</p><p>doi:10.1371/journal.pcbi. 0020173.g004</p></div

PHN Giant Component

<div><p>The fraction <i>n_G</i> of nodes included in the largest connected component of the PHN is shown as a function of the homology cutoff <i>ɛ</i>.</p><p>doi:10.1371/journal.pcbi. 0020173.g003</p></div

Explaining Diversity in Metagenomic Datasets by Phylogenetic-Based Feature Weighting

<div><p>Metagenomics is revolutionizing our understanding of microbial communities, showing that their structure and composition have profound effects on the ecosystem and in a variety of health and disease conditions. Despite the flourishing of new analysis methods, current approaches based on statistical comparisons between high-level taxonomic classes often fail to identify the microbial taxa that are differentially distributed between sets of samples, since in many cases the taxonomic schema do not allow an adequate description of the structure of the microbiota. This constitutes a severe limitation to the use of metagenomic data in therapeutic and diagnostic applications. To provide a more robust statistical framework, we introduce a class of feature-weighting algorithms that discriminate the taxa responsible for the classification of metagenomic samples. The method unambiguously groups the relevant taxa into clades without relying on pre-defined taxonomic categories, thus including in the analysis also those sequences for which a taxonomic classification is difficult. The phylogenetic clades are weighted and ranked according to their abundance measuring their contribution to the differentiation of the classes of samples, and a criterion is provided to define a reduced set of most relevant clades. Applying the method to public datasets, we show that the data-driven definition of relevant phylogenetic clades accomplished by our ranking strategy identifies features in the samples that are lost if phylogenetic relationships are not considered, improving our ability to mine metagenomic datasets. Comparison with supervised classification methods currently used in metagenomic data analysis highlights the advantages of using phylogenetic information.</p></div