Analisis Portofolio Optimal Dengan Single Index Model Untuk Meminimumkan Risiko Bagi Investor Di Bursa Efek Indonesia (Studi Pada Saham Indeks Kompas 100 Periode Februari 2010-juli 2014)

Investments can be made in the capital market, capital market instruments which are mostly attractive for investors is stock. Stock provides a return in the form of capital gains and dividends yield, not only noticing the return, investors need to pay attention to the investments risk. Unsystematis risk can be minimized by forming the optimal portfolio using one of the methods that is single index model. Study purpose is to knowing the stocks forming the optimal portfolio, the proportion of funds allocated to each stocks, the level of expectation return and risk.The method used in this research is descriptive research method with a quantitative approach. The samples used were 46 stocks in Kompas 100 Index, which meets the criteria for sampling. The results showed that 12 stocks of forming optimal portfolio, the stocks of which are UNVR, TRAM, MNCN, BHIT, JSMR, BMTR, GJTL, KLBF, AALI, CPIN, AKRA, and ASRI. Stock with highest proportion of funds is TRAM (23,52%), stock with lowest proportion of funds is AALI (0,62%). Portfolio which are formed will give return expectations by 3,05477% and carry the risk for about 0,1228%

Dynamic Responses of Chromosome-Binding Protein Complexes to Meiotic Prophase I of Mouse Spermatocyte

Meiotic prophase I (MPI) is the most important event in mammalian meiosis. The status of the chromosome-binding proteins (CBPs) and the corresponding complexes and their functions in MPI have not yet been well scrutinized. Quantitative proteomics focused on MPI-related CBPs was accomplished, in which mouse primary spermatocytes in four different subphases of MPI were collected, and chromosome-enriched proteins were extracted and quantitatively identified. According to a stringent criterion, 1136 CBPs in the MPI subphases were quantified. Looking at the dynamic patterns of CBP abundance in response to MPI progression, the patterns were broadly divided into two groups: high abundance in leptotene and zygotene or that in pachytene and diplotene. Furthermore, 152 such CBPs were regarded as 26 CBP complexes with strict filtration, in which some of these complexes were perceived to be MPI-dependent for the first time. These complexes basically belonged to four functional categories, while their dynamic abundance changes following MPI appeared; the functions of DNA replication decreased; and transcription and synapsis were activated in zygotene, pachytene, and diplotene; in contrast to the traditional prediction, condensin activity weakened in pachytene and diplotene. Profiling of protein complexes thus offered convincing evidence of the importance of CBP complexes in MPI

Biomarker Discovery and Verification of Esophageal Squamous Cell Carcinoma Using Integration of SWATH/MRM

We propose an efficient integration of SWATH with MRM for biomarker discovery and verification when the corresponding ion library is well established. We strictly controlled the false positive rate associated with SWATH MS signals and carefully selected the target peptides coupled with SWATH and MRM. We collected 10 samples of esophageal squamous cell carcinoma (ESCC) tissues paired with tumors and adjacent regions and quantified 1758 unique proteins with FDR 1% at protein level using SWATH, in which 467 proteins were abundance-dependent with ESCC. After carefully evaluating the SWATH MS signals of the up-regulated proteins, we selected 120 proteins for MRM verification. MRM analysis of the pooled and individual esophageal tissues resulted in 116 proteins that exhibited similar abundance response modes to ESCC that were acquired with SWATH. Because the ESCC-related proteins consisted of a high percentile of secreted proteins, we conducted the MRM assay on patient sera that were collected from pre- and postoperation. Of the 116 target proteins, 42 were identified in the ESCC sera, including 11 with lowered abundances postoperation. Coupling SWATH and MRM is thus feasible and efficient for the discovery and verification of cancer-related protein biomarkers

Biomarker Discovery and Verification of Esophageal Squamous Cell Carcinoma Using Integration of SWATH/MRM

We propose an efficient integration of SWATH with MRM for biomarker discovery and verification when the corresponding ion library is well established. We strictly controlled the false positive rate associated with SWATH MS signals and carefully selected the target peptides coupled with SWATH and MRM. We collected 10 samples of esophageal squamous cell carcinoma (ESCC) tissues paired with tumors and adjacent regions and quantified 1758 unique proteins with FDR 1% at protein level using SWATH, in which 467 proteins were abundance-dependent with ESCC. After carefully evaluating the SWATH MS signals of the up-regulated proteins, we selected 120 proteins for MRM verification. MRM analysis of the pooled and individual esophageal tissues resulted in 116 proteins that exhibited similar abundance response modes to ESCC that were acquired with SWATH. Because the ESCC-related proteins consisted of a high percentile of secreted proteins, we conducted the MRM assay on patient sera that were collected from pre- and postoperation. Of the 116 target proteins, 42 were identified in the ESCC sera, including 11 with lowered abundances postoperation. Coupling SWATH and MRM is thus feasible and efficient for the discovery and verification of cancer-related protein biomarkers

Expansion of the Ion Library for Mining SWATH-MS Data through Fractionation Proteomics

The strategy of sequential window acquisition of all theoretical fragment ion spectra (SWATH) is emerging in the field of label-free proteomics. A critical consideration for the processing of SWATH data is the quality of the ion library (or mass spectrometric reference map). As the availability of open spectral libraries that can be used to process SWATH data is limited, most users currently create their libraries in-house. Herein, we propose an approach to construct an expanded ion library using the data-dependent acquisition (DDA) data generated by fractionation proteomics. We identified three critical elements for achieving a satisfactory ion library during the iterative process of our ion library expansion, including a correction of the retention times (RTs) gained from fractionation proteomics, appropriate integrations of the fractionated proteomics into an ion library, and assessments of the impact of the expanded ion libraries to data mining in SWATH. Using a bacterial lysate as an evaluation material, we employed sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) to fractionate the lysate proteins and constructed the expanded ion library using the fractionation proteomics data. Compared with the ion library built from the unfractionated proteomics, approximately 20% more peptides were extracted from the expanded ion library. The extracted peptides, moreover, were acceptable for further quantitative analysis

Relative abundance of ROS-related proteins during embryogenesis.

<p>(A) Peroxidases. (B) Peroxiredoxins. (C) Doxins. The error bars indicate the standard derivation. 1 indicates the protein with Locus ID LOC_Os04g56180.1; 2, LOC_Os04g59260.1; 3, LOC_Os04g59150.1; 4, LOC_Os06g35520.1; 5, LOC_Os12g07820.1; 6, LOC_Os08g43560.1; 7, LOC_Os03g17690.1; 8, LOC_Os02g44500.1; 9, LOC_Os04g46960.2; 10, LOC_Os05g25850.1; 11, LOC_Os07g44430.1; 12, LOC_Os01g16152.1; 13, LOC_Os06g42000.1; 14, LOC_Os02g09940.1; 15, LOC_Os07g08840.1; 16, LOC_Os03g58130.1; 17, LOC_Os06g21550.1; 18, LOC_Os10g35720.1; 19, LOC_Os04g42930.1.</p

DataSheet_1_The Comparable Microenvironment Shared by Colorectal Adenoma and Carcinoma: An Evidence of Stromal Proteomics.pdf

Tumor microenvironment (TME) is a key factor involved in cancer development and metastasis. In the TME of colorectal cancer (CRC), the gene expression status of stromal tissues could influence the CRC process from normal to adenoma then carcinoma; however, the expression status at the protein level has not yet been well evaluated. A total of 22 CRC patients were recruited for this study, and the tissue regions corresponding with adjacent, adenoma, and carcinoma were carefully excised by laser capture microdissection (LCM), including a patient with adenoma and carcinoma. The individual proteomes of this cohort were implemented by high-resolution mass spectrometer under data-independent acquisition (DIA) mode. A series of informatic analysis was employed to statistically seek the proteomic characteristics related with the stroma at different stages of CRC. The identified proteins in the colorectal stromal tissues were much less than and almost overlapped with that in the corresponding epithelial tissues; however, the patterns of protein abundance in the stroma were very distinct from those in the epithelium. Although qualitative and quantitative analysis delineated the epithelial proteins specifically typified in the adjacent, adenoma, and carcinoma, the informatics in the stroma led to another deduction that such proteomes were only divided into two patterns, adjacent- and adenoma/carcinoma-dependent. The comparable proteomes of colorectal adenoma and carcinoma were further confirmed by the bulk preparation- or individual LCM-proteomics. The biochemical features of the tumor stromal proteomes were characterized as enrichment of CD4+ and CD8+ T cells, upregulated pathways of antigen presentation, and enhancement of immune signal interactions. Finally, the features of lymphoid lineages in tumor stroma were verified by tissue microarray (TMA). Based on the proteomic evidence, a hypothesis was raised that in the colorectal tissue, the TME of adenoma and carcinoma were comparable, whereas the key elements driving an epithelium from benign to malignant were likely decided by the changes of genomic mutations or/and expression within it.</p

Stress Responsive Proteins Are Actively Regulated during Rice (<i>Oryza sativa</i>) Embryogenesis as Indicated by Quantitative Proteomics Analysis

<div><p>Embryogenesis is the initial step in a plant’s life, and the molecular changes that occur during embryonic development are largely unknown. To explore the relevant molecular events, we used the isobaric tags for relative and absolute quantification (iTRAQ) coupled with the shotgun proteomics technique (iTRAQ/Shotgun) to study the proteomic changes of rice embryos during embryogenesis. For the first time, a total of 2 165 unique proteins were identified in rice embryos, and the abundances of 867 proteins were actively changed based on the statistical evaluation of the quantitative MS/MS signals. The quantitative data were then confirmed using multiple reactions monitoring (MRM) and were also supported by our previous study based on two-dimensional gel electrophoresis (2 DE). Using the proteome at 6 days after pollination (DAP) as a reference, cluster analysis of these differential proteins throughout rice embryogenesis revealed that 25% were up-regulated and 75% were down-regulated. Gene Ontology (GO) analysis implicated that most of the up-regulated proteins were functionally categorized as stress responsive, mainly including heat shock-, lipid transfer-, and reactive oxygen species-related proteins. The stress-responsive proteins were thus postulated to play an important role during seed maturation.</p></div

Relative abundance of heat shock proteins during embryogenesis.

<p>(A) HSP 20. (B) Higher molecular HSPs. (C) DnaK family HSPs. The error bars indicate the standard derivation. 1 indicates the HSP with Locus ID LOC_Os032870.1; 2, LOC_Os06g14240.1; 3, LOC_Os03g15960.1; 4, LOC_Os03g14180.1; 5, LOC_Os01g04370.1; 6, LOC_Os09g30412.1; 7, LOC_Os04g01740.1; 8, LOC_Os12g32986.1; 9, LOC_Os08g39140.1; 10, LOC_Os08g38086.3; 11, LOC_Os06g50300.1; 12, LOC_Os09g30418.1; 13, LOC_Os02g43020.1; 14, LOC_Os05g44340.1; 15, LOC_Os02g52150.2; 16, LOC_Os03g11910.1; 17, LOC_Os01g62290.1; 18, LOC_Os05g38530.1; 19, LOC_Os03g16920.1; 20, LOC_Os09g31486.1; 21, LOC_Os02g48110.1; 22, LOC_Os05g23740.1; 23, LOC_Os02g53420.1; 24, LOC_Os03g16860.1; 25, LOC_Os05g08840.1; 26, LOC_Os12g14070.1; 27, LOC_Os01g08560.1; 28, LOC_Os11g47760.1; 29, LOC_Os02g02410.1; 30, LOC_Os03g44620.2; 31, LOC_Os03g57340.1; 32, LOC_Os01g32870.1.</p