Penggunaan Media Gambar Dalam Meningkatkan Kemampuan Membaca Permulaan Siswa Kelas I SDN Uwedaka Kecamatan Pagimana Kabupaten Banggai

Pokok permasalahan dalam penelitian ini adalah rendahnya tingkat kemampuan membaca permulaan siswa kelas I SDN Uwedaka dalam pembelajaran Bahasa Indonesia. Tujuan Penelitian adalah untuk meningkatkan kemampuan membaca permulaan siswa kelas I SDN Uwedaka Kecamatan Pagimana Kabupaten Banggai. Berdasarkan hasil observasi yang didapatkan masih terdapat beberapa siswa yang sama sekali belum bisa membaca. Pembelajaran membaca permulaan di SDN Uwedaka selama ini hanya menggunakan media pembelajaran yang konvensional yaitu dengan menggunakan papan tulis, pembelajaran yang hanya berpusat pada guru, penggunaan media dalam pembelajaran sebagai alat bantu masih sangat terbatas, hal ini menyebabkan kemampuan membaca permulaan yang masih rendah dan terlihat hampir 65% siswa masih mengalami kesulitan membaca dalam proses belajar mengajar. Metode yang digunakan adalah metode deskriptif kualitatif dan kuantitatif. Data kualitatif didapatkan dari hasil tes dan observasi siswa dan guru. data kuantitatif didapatkan dari hasil tes belajar. Desain penelitian ini mengacu pada desain oleh Kemmis dan Mc Taggart yang terdiri dari empat tahapan, yaitu perencanaan, pelaksanaan tindakan, observasi dan refleksi. Data dikumpulkan melalui penilaian proses dan penilaian hasil setiap akhir tindakan. Penelitian ini dilakukan dalam dua siklus. Pada siklus I diperoleh nilai rata-rata siswa yaitu sebesar 67 dengan ketuntasan belajar klasikal sebesar 40% serta daya serap 66,6%. Pada siklus II, nilai rata-rata meningkat menjadi 83 dengan ketuntasan klasikal sebesar 100% serta daya serap klasikal sebesar 83,3%. Bersarkan hasil penelitian maka dapat disimpulkan bahwa penggunaan media gambar dapat meningkatkan kemampuan membaca permulaan terhadap siswa kelas I SDN Uwedaka Kecamatan Pagimana Kabupaten Banggai

Personalized computer simulations of a NeoAva non-responder patient: comparison of standard and alternative schedules

The videos show personalized computer simulation of a breast tumor portion under the combined effect of chemotherapy and anti-angiogenic treatment. The simulations corresponds to 12 weeks of therapy in a luminal non-responder patient of the NeoAva clinical trial using standard and alternative schedules. Cancer, stroma cells and oxygen tension are shown. To produce the simulations we use a multiscale pharmacokinetic and pharmacodynamic model informed by individual, multisource clinical data, including histological, molecular and magnetic resonance imaging data. See reference below

Covariate Selection in High-Dimensional Generalized Linear Models With Measurement Error

<p>In many problems involving generalized linear models, the covariates are subject to measurement error. When the number of covariates <i>p</i> exceeds the sample size <i>n</i>, regularized methods like the lasso or Dantzig selector are required. Several recent papers have studied methods which correct for measurement error in the lasso or Dantzig selector for linear models in the <i>p</i> > <i>n</i> setting. We study a correction for generalized linear models, based on Rosenbaum and Tsybakov’s matrix uncertainty selector. By not requiring an estimate of the measurement error covariance matrix, this generalized matrix uncertainty selector has a great practical advantage in problems involving high-dimensional data. We further derive an alternative method based on the lasso, and develop efficient algorithms for both methods. In our simulation studies of logistic and Poisson regression with measurement error, the proposed methods outperform the standard lasso and Dantzig selector with respect to covariate selection, by reducing the number of false positives considerably. We also consider classification of patients on the basis of gene expression data with noisy measurements. Supplementary materials for this article are available online.</p

Breast MRI as a building block for patient-specific simulations of drug effect

Poster presented in the 4th Symposium of Nordic Association for Clinical Physics, Bridging Imaging and Therapy, February 6 – 8, 2017 Oslo, Norwa

Additional file 1 of Tilting the lasso by knowledge-based post-processing

More simulation results and interpretations in Appendix A and Appendix B. R-code for data is presented in Appendix C. (PDF 141 kb

Results of linear regression of the member characteristics in the cores of the MSM flirt network and the Nordic flirt network.

*<p>Not coded exclusive; member may want contact both with men and women; the sum of men, women, transsexual >100%.</p

Demographics of the Qruiser population (November 2005 –January 2006).

<p>Demographics of the Qruiser population (November 2005 –January 2006).</p

Results of logistic regression analysis for demographic characteristics of MSM targets¹ (N = 19,549).

<p>Only significant covariates are shown. Likelihood ratio test of the full model with respect to the pure intercept -2LL = 451.82 (p-value <0.00001).</p

The core structure of the MSM exclusive network.

<p> <i>The numbers of individuals in the different cores were: 1-core: 3261; 2-core: 1302; 3-core: 564 and 4-core: 249. Each node in the graph represents an interconnected sub-cluster of nodes with similar core number. The sizes of the nodes represent the logarithmic size of the clusters.</i></p

Results of a Negative Binomial regression analysis of the number of flirts sent by MSM proposers¹ (N = 1,592).

1<p>Only MSM proposers who sent at least one flirt to a MSM target were included.</p><p>Only significant covariates are shown. A positive/negative sign of a coefficient indicates increasing/decreasing activity (in terms of numbers of flirts sent) among MSM members with this characteristic. Likelihood ratio test against the constant model was -2LL = 91.90 (p-value <0.0001). The overdispersion parameter was estimated at 3.72 suggesting that the negative binomial model fitted the data better than a Poisson one.</p