Penerapan Strategi Pembelajaran Inkuiri Dipadukan Media Audio Visual Untuk Meningkatkan Kualitas Pembelajaran Biologi Siswa Kelas VII D SMP N 1 Jaten

– The aim of this research is improve quality of biology learning for conditioning class, student attitude in class, the performance of teacher and student motivation of learning in student class VII-D 1st Junior High School Of Jaten through the application of strategies for inquiry learning combined audio visual media. This research was classroom action research with planning, action, observation, and reflection steps. Data was collected using questionnaire, observation, and interview. The validation of data using method and observer triangulation techniques. The data analyzed by descriptive. The result in cycles I describes that mean of observation data in conditioning class indicators are 70,20%, students\u27 attitudes in class are 62,77%, performance of teachers in the learning are 80% and students motivation in learning are 68,18%. For the questionnaire, observation data in conditioning class indicators are 74,53%, students\u27 attitudes in class are 74,13%, and students motivation in learning are 74,38%. The result in cycles II describes that mean of observation data in conditioning class indicators are 80,81%, students\u27 attitudes in class are 80.09%, performance of teachers in the learning are 96,67% and student motivation in learning are 83,71%. For the questionnaire, observation data in conditioning class indicators are 83,87%, students\u27 attitudes in class are 82,49%, and students motivation in learning are 79,43%. In addition, this research also uses interview to know effect of research in quality of biology learning. The result of interview shows that students\u27 attitudes more positive, students motivation more increase and classroom climate more conducive on learning activities. The conclusion of research describes that the combination of audio visual media in inquiry learning strategies can improve quality of biology learning for conditioning class, students\u27 attitudes, performance of teachers in the learning and motivation of learning in student class VII-D in 1st junior high school of jaten

Giant Valley Splitting and Valley Polarized Plasmonics in Group V Transition-Metal Dichalcogenide Monolayers

Two-dimensional group VI transition-metal dichalcogenides (TMDs) provide a promising platform to encode and manipulate quantum information in the valleytronics. However, the two valleys are energetically degenerate, protected by time-reversal symmetry (TRS). To lift this degeneracy, one needs to break the TRS by either applying an external magnetic field or using a magnetic rare-earth oxide substrate. Here, we predict a different strategy to achieve this goal. We propose that the ferromagnetic group V TMD monolayer, in which the TRS is intrinsically broken, can produce a larger valley and spin splitting. A polarized ZnS(0001) surface is also used as a substrate, which shifts the valleys to the low-energy regime (near the Fermi level). Moreover, by calculating its collective electronic excitation behaviors, we show that such a system hosts a giant valley polarized terahertz plasmonics. Our results demonstrate a new way to design and use valleytronic devices, which are both fundamentally and technologically significant

Conductive Polymer Protonated Nanocellulose Aerogels for Tunable and Linearly Responsive Strain Sensors

Strong and highly conductive aerogels have been assembled from cellulose nanofibrils (CNFs) protonated with conductive poly­(3,4-ethylene dioxythiophene)/poly­(styrene sulfonate) (PEDOT/PSS) complex at equal mass or less. Protonating CNF surface carboxylates and hydrogen-bonding CNF surface carboxyls with PSS in PEDOT/PSS generated PEDOT/PSS/CNF aerogels that were up to ten times stronger while as conductive as neat PEDOT/PSS aerogel, attributed to the transformation of PEDOT benzoid structure to the more electron transfer-preferred quinoid structure. Ethylene glycol vapor annealing further increased the conductivity of PEDOT/PSS/CNF aerogels by 2 orders of magnitude. The poly­(dimethylsiloxane) (PDMS)-infused conductive PEDOT/PSS/CNF aerogel (70 wt % CNF) transform a resistance-insensitive PDMS-infused PEDOT/PSS aerogel (gauge factor of 1.1 × 10^–4) into a stretchable, sensitive, and linearly responsive strain sensor (gauge factor of 14.8 at 95% strain)

Global Quantitative Modeling of Chromatin Factor Interactions

<div><p>Chromatin is the driver of gene regulation, yet understanding the molecular interactions underlying chromatin factor combinatorial patterns (or the “chromatin codes”) remains a fundamental challenge in chromatin biology. Here we developed a global modeling framework that leverages chromatin profiling data to produce a systems-level view of the macromolecular complex of chromatin. Our model ultilizes maximum entropy modeling with regularization-based structure learning to statistically dissect dependencies between chromatin factors and produce an accurate probability distribution of chromatin code. Our unsupervised quantitative model, trained on genome-wide chromatin profiles of 73 histone marks and chromatin proteins from modENCODE, enabled making various data-driven inferences about chromatin profiles and interactions. We provided a highly accurate predictor of chromatin factor pairwise interactions validated by known experimental evidence, and for the first time enabled higher-order interaction prediction. Our predictions can thus help guide future experimental studies. The model can also serve as an inference engine for predicting unknown chromatin profiles — we demonstrated that with this approach we can leverage data from well-characterized cell types to help understand less-studied cell type or conditions.</p></div

Top 20 predicted positive pairwise interactions based on pairwise interaction model with regularization.

<p>Bold pairs are in evaluation standards (experimentally validated).</p

Model provided accurate predictor for experimentally validated chromatin factor interactions.

<p>(A) Heatmap visualization of maximum entropy model pair-wise interaction energy scores (upper right) compared with correlation z-scores (lower left); The heatmap is ordered to position positive interactions close to diagonal so positively interacting factors tend to be adjacent to each other. H3K23ac-, H1-, H3-, H4- represent the depletion of these factors respectively. For comparison with interaction scores, correlations were transformed to z-scores by Fisher transformation and rescaled to make standard deviation equal to the standard deviation of pair-wise interaction energy scores. In figure legend, the corresponding correlation (left) and interaction energy score (right) at each z-score level is shown. The interaction energy score prediction is robust to changing bin size in data processing (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003525#pcbi.1003525.s002" target="_blank">Figure S2</a>). (B) Precision-recall curves for predicting known interactions. Precision-recall curve shows the performance of using interaction energy score to classify interaction at all thresholds. Precision-recall curve of L1-regularized pair-wise interaction maximum entropy model interaction energy scores (red, solid) is compared to unregularized pair-wise interaction maximum entropy model interaction energy scores (black, solid), Bayesian network bootstrap score (black, dashed), Pearson correlation coefficients (grey, dashed), Partial correlation (grey, solid), and mutual information (grey, dot). Maximum entropy models, Bayesian network model and mutual information are computed on discretized data, while correlation and partial correlation were computed on continuous data without discretization.</p

Schematic overview of chromatin factor interaction maximum entropy model.

<p>Chromatin factor patterns were extracted from ChIP data by binning and thresholding algorithms (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003525#s4" target="_blank">Methods</a>). We then learned a maximum entropy model that estimates the distribution of chromatin factor patterns by low-order (pairwise or pairwise & triplet) interactions. The model was then applied to prediction of chromatin factor interactions and performing context-based prediction of chromatin profiles.</p

Me₂(CH₂Cl)SiCN: Bifunctional Cyanating Reagent for the Synthesis of Tertiary Alcohols with a Chloromethyl Ketone Moiety via Ketone Cyanosilylation

We report a novel bifunctional cyanating reagent, Me₂(CH₂Cl)­SiCN, which paves the way to a one-pot sequential synthesis of tertiary alcohols featuring a chloromethyl ketone moiety via enantioselective ketone cyanosilylation. This method contributes to gram-scale enantioselective total synthesis of the aggregation pheromone of the Colorado potato beetle, (<i>S</i>)-CPB

Pair-wise interaction network organization structure of chromatin factors.

<p>Each node represents a chromatin factor and each edge represents a pair-wise interaction. Edge color indicates sign and strength of interaction energy score (red indicates positive interaction while blue indicates negative interaction). Only interactions with interaction energy score are shown.</p

Context-based intra- and inter-cell type chromatin factor profile predictions achieve high overall performance.

<p>(A) Prediction performances on hold-out chromatin factor profiles based on partial data and chromatin model. Chromatin factor profile predictions are compared with observed chromatin profiles using receiver operating characteristics (ROC) that shows true positive rate (y-axis) and false positive rate (x-axis) at full range of prediction thresholds. The diagonal line (dashed) shows expected performance of random classifier. The histogram shows frequency distribution of area under ROC curves (AUC). (B) Comparison of predicted and observed S2 cell H3K18ac chromatin profile. ChIP profile is visualized as the space-filling Hilbert curve as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003525#pcbi.1003525-Kharchenko1" target="_blank">[11]</a>, therefore adjacent genomic locations are also close to each other in this 2D representation. Predicted profile based on BG3 cell model is colored yellow, with darker color showing higher probability; Observed binarized profile is colored blue; Overlap between predicted and observed profile is therefore green. H3K18ac is an example chromatin factor which cannot be accurately inferred from any other single chromatin profile (the highest correlation coefficient with H3K18ac is 0.37). (C, D) Comparison of inter-cell type versus intra-cell type chromatin profile prediction performances. Performance is measured by AUC. ‘->’ indicates which cell lines the model is trained for and tested on, e.g. S2->BG3 represents predicting BG3 cell data with model trained on S2 data.</p