Education•Cambridge, Massachusetts, United States•
About: Harvard University is a education organization based out in Cambridge, Massachusetts, United States. It is known for research contribution in the topics: Population & Cancer. The organization has 208150 authors who have published 530388 publications receiving 38152182 citations. The organization is also known as: Harvard & University of Harvard.
Papers published on a yearly basis
TL;DR: In this paper, the authors described a simplified version of the method and reported the results of a study of its application to different tissues, including the efficiency of the washing procedure in terms of the removal from tissue lipides of some non-lipide substances of special biochemical interest.
Abstract: Work from this laboratory resulted in the development of a method for the preparation and purification of brain lipides (1) which involved two successive operations. In the first step, the lipides were extracted by homogenizing the tissue with 2: 1 chloroform-methanol (v/v), and filtering the homogenate. In the second step, the filtrate, which contained the tissue lipides accompanied by non-lipide substances, was freed from these substances by being placed in contact with at least 5-fold its volume of water. This water washing entailed the loss of about 1 per cent of the brain lipides. This paper describes a simplified version of the method and reports the results of a study of its application to different tissues, including the efficiency of the washing procedure in terms of the removal from tissue lipides of some non-lipide substances of special biochemical interest. It also reports some pertinent ancillary findings. The modifications introduced into the method pertain only to the washing procedure. A chloroformmethanol extract of the tissue, prepared as described in the original version of the method, is mixed with 0.2 its volume of water to which, for certain purposes, different mineral salts may be added. A biphasic system without any interfacial fluff is obtained (2). The upper phase contains all of the non-lipide substances, most of the strandin, and only negligible amounts of the other lipides. The lower phase contains essentially all the tissue lipides other than strandin. In comparison with the original method, the present version has the advantage of being simpler, of being applicable to any scale desired, of substantially decreasing the losses of lipides incidental to the washing process, and, finally, of yielding a washed extract which can be taken to dryness without foaming and without splitting of the proteolipides (3).
TL;DR: This work presents DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates, which enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.
Abstract: In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html .
TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
Abstract: Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
TL;DR: This paper examines eight published reviews each reporting results from several related trials in order to evaluate the efficacy of a certain treatment for a specified medical condition and suggests a simple noniterative procedure for characterizing the distribution of treatment effects in a series of studies.
Abstract: This paper examines eight published reviews each reporting results from several related trials. Each review pools the results from the relevant trials in order to evaluate the efficacy of a certain treatment for a specified medical condition. These reviews lack consistent assessment of homogeneity of treatment effect before pooling. We discuss a random effects approach to combining evidence from a series of experiments comparing two treatments. This approach incorporates the heterogeneity of effects in the analysis of the overall treatment efficacy. The model can be extended to include relevant covariates which would reduce the heterogeneity and allow for more specific therapeutic recommendations. We suggest a simple noniterative procedure for characterizing the distribution of treatment effects in a series of studies.
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|Walter C. Willett||334||2399||413322|
|Eric S. Lander||301||826||525976|
|Meir J. Stampfer||277||1414||283776|
|Ronald C. Kessler||274||1332||328983|
|JoAnn E. Manson||270||1819||258509|
|Graham A. Colditz||261||1542||256034|
|Frank B. Hu||250||1675||253464|
|George M. Whitesides||240||1739||269833|
|Paul M. Ridker||233||1242||245097|
|Richard A. Flavell||231||1328||205119|
|Ralph B. D'Agostino||226||1287||229636|
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