60 research outputs found
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%
PPIP: Automated Software for Identification of Bioactive Endogenous Peptides
Endogenous peptides
play an important role in multiple biological
processes in many species. Liquid chromatography coupled to tandem
mass spectrometry (LC–MS/MS) is an important technique for
detecting these peptides on a large scale. We present PPIP, which
is a dedicated peptidogenomics software for identifying endogenous
peptides based on peptidomics and RNA-Seq data. This software automates
the de novo transcript assembly based on RNA-Seq data, construction
of a protein reference database based on the de novo assembled transcripts,
peptide identification, function analysis, and HTML-based report generation.
Different function components are integrated using Docker technology.
The Docker image of PPIP is available at https://hub.docker.com/r/shawndp/ppip, and the source code under GPL-3 license is available at https://github.com/Shawn-Xu/PPIP. A user manual of PPIP is available at https://shawn-xu.github.io/PPIP
PPIP: Automated Software for Identification of Bioactive Endogenous Peptides
Endogenous peptides
play an important role in multiple biological
processes in many species. Liquid chromatography coupled to tandem
mass spectrometry (LC–MS/MS) is an important technique for
detecting these peptides on a large scale. We present PPIP, which
is a dedicated peptidogenomics software for identifying endogenous
peptides based on peptidomics and RNA-Seq data. This software automates
the de novo transcript assembly based on RNA-Seq data, construction
of a protein reference database based on the de novo assembled transcripts,
peptide identification, function analysis, and HTML-based report generation.
Different function components are integrated using Docker technology.
The Docker image of PPIP is available at https://hub.docker.com/r/shawndp/ppip, and the source code under GPL-3 license is available at https://github.com/Shawn-Xu/PPIP. A user manual of PPIP is available at https://shawn-xu.github.io/PPIP
PPIP: Automated Software for Identification of Bioactive Endogenous Peptides
Endogenous peptides
play an important role in multiple biological
processes in many species. Liquid chromatography coupled to tandem
mass spectrometry (LC–MS/MS) is an important technique for
detecting these peptides on a large scale. We present PPIP, which
is a dedicated peptidogenomics software for identifying endogenous
peptides based on peptidomics and RNA-Seq data. This software automates
the de novo transcript assembly based on RNA-Seq data, construction
of a protein reference database based on the de novo assembled transcripts,
peptide identification, function analysis, and HTML-based report generation.
Different function components are integrated using Docker technology.
The Docker image of PPIP is available at https://hub.docker.com/r/shawndp/ppip, and the source code under GPL-3 license is available at https://github.com/Shawn-Xu/PPIP. A user manual of PPIP is available at https://shawn-xu.github.io/PPIP
PPIP: Automated Software for Identification of Bioactive Endogenous Peptides
Endogenous peptides
play an important role in multiple biological
processes in many species. Liquid chromatography coupled to tandem
mass spectrometry (LC–MS/MS) is an important technique for
detecting these peptides on a large scale. We present PPIP, which
is a dedicated peptidogenomics software for identifying endogenous
peptides based on peptidomics and RNA-Seq data. This software automates
the de novo transcript assembly based on RNA-Seq data, construction
of a protein reference database based on the de novo assembled transcripts,
peptide identification, function analysis, and HTML-based report generation.
Different function components are integrated using Docker technology.
The Docker image of PPIP is available at https://hub.docker.com/r/shawndp/ppip, and the source code under GPL-3 license is available at https://github.com/Shawn-Xu/PPIP. A user manual of PPIP is available at https://shawn-xu.github.io/PPIP
PPIP: Automated Software for Identification of Bioactive Endogenous Peptides
Endogenous peptides
play an important role in multiple biological
processes in many species. Liquid chromatography coupled to tandem
mass spectrometry (LC–MS/MS) is an important technique for
detecting these peptides on a large scale. We present PPIP, which
is a dedicated peptidogenomics software for identifying endogenous
peptides based on peptidomics and RNA-Seq data. This software automates
the de novo transcript assembly based on RNA-Seq data, construction
of a protein reference database based on the de novo assembled transcripts,
peptide identification, function analysis, and HTML-based report generation.
Different function components are integrated using Docker technology.
The Docker image of PPIP is available at https://hub.docker.com/r/shawndp/ppip, and the source code under GPL-3 license is available at https://github.com/Shawn-Xu/PPIP. A user manual of PPIP is available at https://shawn-xu.github.io/PPIP
PPIP: Automated Software for Identification of Bioactive Endogenous Peptides
Endogenous peptides
play an important role in multiple biological
processes in many species. Liquid chromatography coupled to tandem
mass spectrometry (LC–MS/MS) is an important technique for
detecting these peptides on a large scale. We present PPIP, which
is a dedicated peptidogenomics software for identifying endogenous
peptides based on peptidomics and RNA-Seq data. This software automates
the de novo transcript assembly based on RNA-Seq data, construction
of a protein reference database based on the de novo assembled transcripts,
peptide identification, function analysis, and HTML-based report generation.
Different function components are integrated using Docker technology.
The Docker image of PPIP is available at https://hub.docker.com/r/shawndp/ppip, and the source code under GPL-3 license is available at https://github.com/Shawn-Xu/PPIP. A user manual of PPIP is available at https://shawn-xu.github.io/PPIP
PPIP: Automated Software for Identification of Bioactive Endogenous Peptides
Endogenous peptides
play an important role in multiple biological
processes in many species. Liquid chromatography coupled to tandem
mass spectrometry (LC–MS/MS) is an important technique for
detecting these peptides on a large scale. We present PPIP, which
is a dedicated peptidogenomics software for identifying endogenous
peptides based on peptidomics and RNA-Seq data. This software automates
the de novo transcript assembly based on RNA-Seq data, construction
of a protein reference database based on the de novo assembled transcripts,
peptide identification, function analysis, and HTML-based report generation.
Different function components are integrated using Docker technology.
The Docker image of PPIP is available at https://hub.docker.com/r/shawndp/ppip, and the source code under GPL-3 license is available at https://github.com/Shawn-Xu/PPIP. A user manual of PPIP is available at https://shawn-xu.github.io/PPIP
Table_1_Lysophosphatidylcholines and phosphatidylcholines as biomarkers for stroke recovery.xlsx
Stroke is a serious global public health issue, associated with severe disability and high mortality rates. Its early detection is challenging, and no effective biomarkers are available. To obtain a better understanding of stroke prevention, management, and recovery, we conducted lipidomic analyses to characterize plasma metabolic features. Lipid species were measured using an untargeted lipidomic analysis with liquid chromatography-tandem mass spectrometry. Sixty participants were recruited in this cohort study, including 20 healthy individuals and 40 patients with stroke. To investigate the association between lipids related to long-term functional recovery in stroke patients. The primary independent variable was activities of daily living (ADL) dependency upon admission to the stroke unit and at the 3-month follow-up appointment. ADL dependency was assessed using the Barthel Index. Eleven significantly altered lipid species between the stroke and healthy groups were detected and displayed in a hierarchically clustered heatmap. Acyl carnitine, triacylglycerol, and ceramides were detected as potential lipid markers. Regarding the association between lipid profiles and functional status of patients with stroke the results indicated, lysophosphatidylcholines (LPC) and phosphatidylcholines were closely associated with stroke recovery. LPC may contribute positively role in patient's rehabilitation process via an anti-inflammatory mechanism. Appropriate management or intervention for lipid levels is expected to lead to better clinical outcomes.</p
Image_1_Lysophosphatidylcholines and phosphatidylcholines as biomarkers for stroke recovery.pdf
Stroke is a serious global public health issue, associated with severe disability and high mortality rates. Its early detection is challenging, and no effective biomarkers are available. To obtain a better understanding of stroke prevention, management, and recovery, we conducted lipidomic analyses to characterize plasma metabolic features. Lipid species were measured using an untargeted lipidomic analysis with liquid chromatography-tandem mass spectrometry. Sixty participants were recruited in this cohort study, including 20 healthy individuals and 40 patients with stroke. To investigate the association between lipids related to long-term functional recovery in stroke patients. The primary independent variable was activities of daily living (ADL) dependency upon admission to the stroke unit and at the 3-month follow-up appointment. ADL dependency was assessed using the Barthel Index. Eleven significantly altered lipid species between the stroke and healthy groups were detected and displayed in a hierarchically clustered heatmap. Acyl carnitine, triacylglycerol, and ceramides were detected as potential lipid markers. Regarding the association between lipid profiles and functional status of patients with stroke the results indicated, lysophosphatidylcholines (LPC) and phosphatidylcholines were closely associated with stroke recovery. LPC may contribute positively role in patient's rehabilitation process via an anti-inflammatory mechanism. Appropriate management or intervention for lipid levels is expected to lead to better clinical outcomes.</p
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