A parameter estimation subroutine package

Linear least squares estimation and regression analyses continue to play a major role in orbit determination and related areas. FORTRAN subroutines have been developed to facilitate analyses of a variety of parameter estimation problems. Easy to use multipurpose sets of algorithms are reported that are reasonably efficient and which use a minimal amount of computer storage. Subroutine inputs, outputs, usage and listings are given, along with examples of how these routines can be used

A parameter estimation subroutine package

Linear least squares estimation and regression analyses continue to play a major role in orbit determination and related areas. A library of FORTRAN subroutines were developed to facilitate analyses of a variety of estimation problems. An easy to use, multi-purpose set of algorithms that are reasonably efficient and which use a minimal amount of computer storage are presented. Subroutine inputs, outputs, usage and listings are given, along with examples of how these routines can be used. The routines are compact and efficient and are far superior to the normal equation and Kalman filter data processing algorithms that are often used for least squares analyses

A parameter estimation subroutine package

Linear least squares estimation and regression analyses continue to play a major role in orbit determination and related areas. In this report we document a library of FORTRAN subroutines that have been developed to facilitate analyses of a variety of estimation problems. Our purpose is to present an easy to use, multi-purpose set of algorithms that are reasonably efficient and which use a minimal amount of computer storage. Subroutine inputs, outputs, usage and listings are given along with examples of how these routines can be used. The following outline indicates the scope of this report: Section (1) introduction with reference to background material; Section (2) examples and applications; Section (3) subroutine directory summary; Section (4) the subroutine directory user description with input, output, and usage explained; and Section (5) subroutine FORTRAN listings. The routines are compact and efficient and are far superior to the normal equation and Kalman filter data processing algorithms that are often used for least squares analyses

SPODP - Single Precision Orbit Determination Program

Single Precision Orbit Determination program - computer program for accurate tracking and prediction of lunar and interplanetary spacecraft motio

In Deadly Time: The Lasting On of Waste in Mayhew’s London

This paper examines the temporal dimension of waste in Henry Mayhew’s London Labour and the London Poor as an instance of how modernity has produced a largely hidden domain of the non-identical and indeterminate. Through a consideration of the phenomena of uselessness, decay and poverty I argue that the temporal dimension of waste is constituted as a corrosive or malign ‘Deadly Time.’ In placing such emphasis on time directed towards death, I aim to show that Mayhew’s undisciplined researches can be seen as a valuable source for understanding why modern thinking struggles to come to terms with waste

The genetic susceptibility to type 2 diabetes may be modulated by obesity status: implications for association studies

<p>Abstract</p> <p>Background</p> <p>Considering that a portion of the heterogeneity amongst previous replication studies may be due to a variable proportion of obese subjects in case-control designs, we assessed the association of genetic variants with type 2 diabetes (T2D) in large groups of obese and non-obese subjects.</p> <p>Methods</p> <p>We genotyped <it>RETN</it>, <it>KCNJ11</it>, <it>HNF4A</it>, <it>HNF1A</it>, <it>GCK</it>, <it>SLC30A8</it>, <it>ENPP1</it>, <it>ADIPOQ</it>, <it>PPARG</it>, and <it>TCF7L2 </it>polymorphisms in 1,283 normoglycemic (NG) and 1,581 T2D obese individuals as well as in 3,189 NG and 1,244 T2D non-obese subjects of European descent, allowing us to examine T2D risk over a wide range of BMI.</p> <p>Results</p> <p>Amongst non-obese individuals, we observed significant T2D associations with <it>HNF1A </it>I27L [odds ratio (OR) = 1.14, <it>P </it>= 0.04], <it>GCK </it>-30G>A (OR = 1.23, <it>P </it>= 0.01), <it>SLC30A8 </it>R325W (OR = 0.87, <it>P </it>= 0.04), and <it>TCF7L2 </it>rs7903146 (OR = 1.89, <it>P </it>= 4.5 × 10^-23), and non-significant associations with <it>PPARG </it>Pro12Ala (OR = 0.85, <it>P </it>= 0.14), <it>ADIPOQ </it>-11,377C>G (OR = 1.00, <it>P </it>= 0.97) and <it>ENPP1 </it>K121Q (OR = 0.99, <it>P </it>= 0.94). In obese subjects, associations with T2D were detected with <it>PPARG </it>Pro12Ala (OR = 0.73, <it>P </it>= 0.004), <it>ADIPOQ </it>-11,377C>G (OR = 1.26, <it>P </it>= 0.02), <it>ENPP1 </it>K121Q (OR = 1.30, <it>P </it>= 0.003) and <it>TCF7L2 </it>rs7903146 (OR = 1.30, <it>P </it>= 1.1 × 10^-4), and non-significant associations with <it>HNF1A </it>I27L (OR = 0.96, <it>P </it>= 0.53), <it>GCK </it>-30G>A (OR = 1.15, <it>P </it>= 0.12) and <it>SLC30A8 </it>R325W (OR = 0.95, <it>P </it>= 0.44). However, a genotypic heterogeneity was only found for <it>TCF7L2 </it>rs7903146 (<it>P </it>= 3.2 × 10^-5) and <it>ENPP1 </it>K121Q (<it>P </it>= 0.02). No association with T2D was found for <it>KCNJ11</it>, <it>RETN</it>, and <it>HNF4A </it>polymorphisms in non-obese or in obese individuals.</p> <p>Conclusion</p> <p>Genetic variants modulating insulin action may have an increased effect on T2D susceptibility in the presence of obesity, whereas genetic variants acting on insulin secretion may have a greater impact on T2D susceptibility in non-obese individuals.</p

Obesity, metabolic factors and risk of different histological types of lung cancer: A Mendelian randomization study.

BACKGROUND: Assessing the relationship between lung cancer and metabolic conditions is challenging because of the confounding effect of tobacco. Mendelian randomization (MR), or the use of genetic instrumental variables to assess causality, may help to identify the metabolic drivers of lung cancer. METHODS AND FINDINGS: We identified genetic instruments for potential metabolic risk factors and evaluated these in relation to risk using 29,266 lung cancer cases (including 11,273 adenocarcinomas, 7,426 squamous cell and 2,664 small cell cases) and 56,450 controls. The MR risk analysis suggested a causal effect of body mass index (BMI) on lung cancer risk for two of the three major histological subtypes, with evidence of a risk increase for squamous cell carcinoma (odds ratio (OR) [95% confidence interval (CI)] = 1.20 [1.01-1.43] and for small cell lung cancer (OR [95%CI] = 1.52 [1.15-2.00]) for each standard deviation (SD) increase in BMI [4.6 kg/m2]), but not for adenocarcinoma (OR [95%CI] = 0.93 [0.79-1.08]) (Pheterogeneity = 4.3x10-3). Additional analysis using a genetic instrument for BMI showed that each SD increase in BMI increased cigarette consumption by 1.27 cigarettes per day (P = 2.1x10-3), providing novel evidence that a genetic susceptibility to obesity influences smoking patterns. There was also evidence that low-density lipoprotein cholesterol was inversely associated with lung cancer overall risk (OR [95%CI] = 0.90 [0.84-0.97] per SD of 38 mg/dl), while fasting insulin was positively associated (OR [95%CI] = 1.63 [1.25-2.13] per SD of 44.4 pmol/l). Sensitivity analyses including a weighted-median approach and MR-Egger test did not detect other pleiotropic effects biasing the main results. CONCLUSIONS: Our results are consistent with a causal role of fasting insulin and low-density lipoprotein cholesterol in lung cancer etiology, as well as for BMI in squamous cell and small cell carcinoma. The latter relation may be mediated by a previously unrecognized effect of obesity on smoking behavior

Rb binds c-Jun and activates transcription.

The retinoblastoma protein (Rb) acts as a critical cell-cycle regulator and loss of Rb function is associated with a variety of human cancer types. Here we report that Rb binds to members of the AP-1 family of transcription factors, including c-Jun, and stimulates c-Jun transcriptional activity from an AP-1 consensus sequence. The interaction involves the leucine zipper region of c-Jun and the B pocket of Rb as well as a C-terminal domain. We also present evidence that the complexes are found in terminally differentiating keratinocytes and cells entering the G1 phase of the cell cycle after release from serum starvation. The human papillomavirus type 16 E7 protein, which binds to both c-Jun and Rb, inhibits the ability of Rb to activate c-Jun. The results provide evidence of a role for Rb as a transcriptional activator in early G1 and as a potential modulator of c-Jun expression during keratinocyte differentiation