LKS Asam Basa Berbasis Pendekatan Ilmiah Dalam Meningkatkan KPS Berdasarkan Kognitif Siswa

This research aimed to describe the effectiveness of scientific approach based student worksheets in improving science process skills (SPS) insight from student\u27s cognitive. The method of this research was quasi experimental with 2x2 factorial design. The population of this research was all students of XI IPA SMAN 15 Bandarlampung on 2016/2017. The sample were XI IPA-4 and the XI IPA 2 which taken by purposive sampling. The data of this study were analyzed by using two ways ANOVA test and t test. The result of this research was no interaction between learning with scientific approach based worksheets and cognitive on SPS; learning process using student worksheets scientific approach was effective to improve SPS; SPS high and low cognitive ability with learning using worksheets Scientific Approach wass higher than conventional worksheets; SPS high cognitive ability was higher than low cognitive ability with learning using worksheets scientific approach. Penelitian ini bertujuan mendeskripsikan efektivitas LKS pendekatan ilmiah dalam meningkatkan KPS berdasarkan kognitif siswa. Metode penelitian menggunakan kuasi eksperimen dengan desain faktorial 2x2. Populasi penelitian seluruh siswa kelas XI IPA di SMAN 15 Bandarlampung tahun 2016/2017. Sampel penelitian ini kelas XI IPA 4 dan kelas XI IPA 2 yang diambil dengan teknik purposive sampling. Data penelitian dianalisis menggunakan uji two ways ANOVA dan uji t. Hasil penelitian menunjukan tidak terdapat interaksi antara pembelajaran menggunakan LKS terhadap KPS berdasarkan kemampuan kognitif, pembelajaran menggunakan LKS pendekatan ilmiah efektif untuk meningkatkan KPS, KPS siswa kognitif tinggi dan rendah dengan pembelajaran menggunakan LKS pendekatan ilmiah lebih tinggi dibandingkan LKS konvensional, KPS siswa kognitif tinggi lebih tinggi dibandingkan KPS siswa kognitif rendah menggunakan LKS pendekatan ilmiah

ProteoModlR for functional proteomic analysis

BACKGROUND: High-accuracy mass spectrometry enables near comprehensive quantification of the components of the cellular proteomes, increasingly including their chemically modified variants. Likewise, large-scale libraries of quantified synthetic peptides are becoming available, enabling absolute quantification of chemically modified proteoforms, and therefore systems-level analyses of changes of their absolute abundance and stoichiometry. Existing computational methods provide advanced tools for mass spectral analysis and statistical inference, but lack integrated functions for quantitative analysis of post-translationally modified proteins and their modification stoichiometry. RESULTS: Here, we develop ProteoModlR, a program for quantitative analysis of abundance and stoichiometry of post-translational chemical modifications across temporal and steady-state biological states. While ProteoModlR is intended for the analysis of experiments using isotopically labeled reference peptides for absolute quantitation, it also supports the analysis of labeled and label-free data, acquired in both data-dependent and data-independent modes for relative quantitation. Moreover, ProteoModlR enables functional analysis of sparsely sampled quantitative mass spectrometry experiments by inferring the missing values from the available measurements, without imputation. The implemented architecture includes parsing and normalization functions to control for common sources of technical variation. Finally, ProteoModlR’s modular design and interchangeable format are optimally suited for integration with existing computational proteomics tools, thereby facilitating comprehensive quantitative analysis of cellular signaling. CONCLUSIONS: ProteoModlR and its documentation are available for download at http://github.com/kentsisresearchgroup/ProteoModlR as a stand-alone R package. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1563-6) contains supplementary material, which is available to authorized users

Ketohexokinase-mediated fructose metabolism is lost in hepatocellular carcinoma and can be leveraged for metabolic imaging

The ability to break down fructose is dependent on ketohexokinase (KHK) that phosphorylates fructose to fructose-1-phosphate (F1P). We show that KHK expression is tightly controlled and limited to a small number of organs and is down-regulated in liver and intestinal cancer cells. Loss of fructose metabolism is also apparent in hepatocellular adenoma and carcinoma (HCC) patient samples. KHK overexpression in liver cancer cells results in decreased fructose flux through glycolysis. We then developed a strategy to detect this metabolic switch in vivo using hyperpolarized magnetic resonance spectroscopy. Uniformly deuterating [2-13C]-fructose and dissolving in D2O increased its spin-lattice relaxation time (T1) fivefold, enabling detection of F1P and its loss in models of HCC. In summary, we posit that in the liver, fructolysis to F1P is lost in the development of cancer and can be used as a biomarker of tissue function in the clinic using metabolic imaging

J-aggregate Nanoparticles as Photoacoustic Contrast Agents for Prostate Cancer Imaging

Management of early stage prostate cancer (PCa) is plagued with the dilemma between active surveillance that risks progression, and aggressive treatments of potentially indolent disease that significantly reduces quality of life. This results from the inability of current diagnostic techniques to accurately distinguish between indolent and aggressive disease, which has resulted in overtreatment of PCa. Photoacoutic imaging allows for imaging of specific molecular constituents in tissue. To enable for its use in PCa imaging, we designed a novel organic nanoparticle that combines the unique spectral properties and efficient photon capture of nature's photosynthetic apparatus with the stable and specific delivery offered by nanoparticles. These Jaggregate nanoparticles are shown to produce an intense, narrow photo acoustic signal and to have nanoparticle-dependent photonic properties that enable for assessment of the state of the particle. Preliminary assessment of their use in an orthotopic PCa model showed accumulation in and delineation of the tumor boundary.M.Sc

Additional file 1: Figure S1. of ProteoModlR for functional proteomic analysis

Conceptual overview of the operations performed by ProteoModlR. (A) A set of proteoforms is digested into peptides and (B) mixed with an equimolar set of synthetic reference peptides (in blue). (C) MS signal-response is affected by differential ionization efficiency. Furthermore, MS quantification may present missing values. (D) ProteoModlR first annotates the available set of peptides, then (E) corrects errors introduced by technical and biological variability. Finally, (F) exact or approximate calculations are deployed to obtain PTM stoichiometry and abundance. (TIF 919 kb

Additional file 10: Simulated Dataset 2. of ProteoModlR for functional proteomic analysis

Normalization_dataset_no_error. The file contains a simulated datasets with identical intensity values for all peptides in all conditions. This dataset is provided as a reference to evaluate the accuracy of normalization strategies. (CSV 4 kb

Additional file 3: Figure S3. of ProteoModlR for functional proteomic analysis

Isotopologue normalization corrects for technical variability across measurements, as demonstrated on simulated data. (A) Quantitation across three replicate measurements of five peptides from a protein of interest (shades of red) and four peptides from reference proteins (shades of blue). (B) ProteoModlR corrects errors introduced by technical and biological variability. (C) Quantitation of heavy labeled standard peptides is also affected by technical variability. (D) If isotopologue normalization is chosen, ProteoModlR equalizes the intensities of the standard isotopologues for each peptide independently. (TIF 9821 kb

Additional file 7: Figure S6. of ProteoModlR for functional proteomic analysis

Output of exact (A) and approximate (B-D) calculations from simulated datasets. The input contained quantitation across three replicate measurements of four peptides, two of which phosphorylated. (TIF 25386 kb

Additional file 8: Simulated Dataset 1. of ProteoModlR for functional proteomic analysis

Normalization simulated datasets – description. Detailed description of the simulated datasets used to test ProteoModlR’s normalization module. (TXT 1 kb

Additional file 4: Figure S4. of ProteoModlR for functional proteomic analysis

Total ion current normalization corrects for technical variability across measurements in absence of isotopically encoded standards, as demonstrated on simulated data. (A) Quantitation across three replicate measurements of five peptides from a protein of interest (shades of red) and four peptides from reference proteins (shades of blue). (B) ProteoModlR corrects errors introduced by technical and biological variability. (C) Total ion current is also affected by technical variability. (D) If total ion current normalization is chosen, ProteoModlR equalizes the sum of the intensities of all peptides in each sample. (TIF 9017 kb