William Cullen Bryant and The Poetry of Natural Law

The question has more than academic interest, as noted in Lloyd Weinreb\u27s Natural Law and Justice, in which he argues for a return to natural law\u27s ontological basis.\u27 Tracing its roots in Greek expressions of natural law that allowed for free will in an otherwise determinate natural order, Weinreb surveys the history of natural law only to find that what began as ontological became deontological, which led natural law theories away from nature and reason and towards a focus on concepts of morality. He argues ultimately that such deontological theories fail to answer the question of human freedom within a causal universe in the same way that the original versions of ontological natural law also failed. In seeking, however, a return to some ontological basis for natural law as the preferred way to accommodate antinomic notions of liberty and equality, desert and entitlement, and freedom and cause, he stresses the role that an understanding of nature can provide, particularly since [i]ncreasing control over nature by the discovery of its laws increases human freedom in one sense

Heavy-light decay constants---MILC results with the Wilson action

We present the current status of our ongoing calculations of pseudoscalar meson decay constants for mesons that contain one light and one heavy quark (f_B, f_{B_s}, f_D, f_{D_s}). We are currently generating new gauge configurations that include dynamical quarks and calculating the decay constants. In addition, we have several new results for the static approximation. Those results, as well as several refinements to the analysis, are new since Lattice '96. Our current (still preliminary) value for f_B is 156 +- 11 +- 30 +- 14 MeV, where the first error is from statistical and fitting errors, the second error is an estimate of other systematic errors within the quenched approximation and the third error is an estimate of the quenching error. For the ratio f_{B_s}/f_B, we get 1.11 +- 0.02 +- 0.03 +- 0.07.Comment: 8 pages, 9 figures, LaTeX, uses espcrs2, epsf, Invited talk presented by S. Gottlieb at Lattice QCD on Parallel Computers, University of Tsukuba, March, 1997, to appear in the proceeding

Management of blunt extracranial traumatic cerebrovascular injury: a multidisciplinary survey of current practice

<p>Abstract</p> <p>Background</p> <p>Extracranial traumatic cerebrovascular injury (TCVI) is present in 1-3% of all blunt force trauma patients. Although options for the management of patients with these lesions include anticoagulation, antiplatelet agents, and endovascular treatment, the optimal management strategy for patients with these lesions is not yet established.</p> <p>Objective</p> <p>Multidisciplinary survey of clinicians about current management of TCVI.</p> <p>Methods</p> <p>A six-item multiple-choice survey was sent by electronic mail to a total of 11,784 neurosurgeons, trauma surgeons, stroke neurologists, and interventional radiologists. The survey included questions about their choice of imaging, medical management, and the use of endovascular techniques. Survey responses were analyzed according to stated specialty.</p> <p>Results</p> <p>Seven hundred eighty-five (6.7%) responses were received. Overall, a total of 325 (42.8%) respondents favored anticoagulation (heparin and/or warfarin), 247 (32.5%) favored antiplatelet drugs, 130 (17.1%) preferred both anticoagulation and antiplatelet drugs, and 57 (7.5%) preferred stenting and/or embolization. Anticoagulation was the most commonly preferred treatment among vascular surgeons (56.9%), neurologists (50.2%) and neurosurgeons (40.7%), whereas antiplatelet agents were the most common preferred treatment among trauma surgeons (41.5%). Overall, 158 (20.7%) of respondents recommended treatment of asymptomatic dissections and traumatic aneurysms, 211 (27.7%) did not recommend it, and 39.4% recommended endovascular treatment only if there is worsening of the lesion on follow-up imaging.</p> <p>Conclusions</p> <p>These data demonstrate the wide variability of physicians' management of traumatic cerebrovascular injury, both on an individual basis, and between specialties. These findings underscore the need for multicenter, randomized trials in this field.</p

Lattice Determination of Heavy-Light Decay Constants

We report on the MILC collaboration's calculation of $f_B$, $f_{B_s}$, $f_D$, $f_{D_s}$, and their ratios. Our central values come from the quenched approximation, but the quenching error is estimated from $N_F=2$ dynamical staggered lattices. We use Wilson light valence quarks and Wilson and static heavy quarks. We find, for example, f_B=157 \pm 11 {}^{+25}_{-9} {}^{+23}_{-0} \MeV, $f_{B_s}/f_B = 1.11 \pm 0.02 {}^{+0.04}_{-0.03} \pm 0.03$, f_{D_s} = 210 \pm 9 {}^{+25}_{-9} {}^{+17}_{-1} \MeV and $f_{B}/f_{D_s} = 0.75 \pm 0.03 {}^{+0.04}_{-0.02} {}^{+0.08}_{-0.00}$, where the errors are statistical, systematic (within the quenched approximation), and systematic (of quenching), respectively.Comment: 4 pages, 2 included figures. We adjust the perturbative correction calculated by Kuramashi to take into account the fact that we match to the continuum at the kinetic mass of the heavy meson, not the pole mass. This produces a 2 to 4 MeV change in final results for decay constants, and has negligible effect on decay constant ratios. We also include further explanation of various features of the analysi

Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning

OBJECTIVE Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), histological grading is a poor predictor of disease progression, and consensus molecular subtypes (CMSs) cannot be distinguished without gene expression profiling. We hypothesise that image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data and to resolve unclassifiable or heterogeneous cases. In this study, we present an image-based approach to predict CRC CMS from standard H&E sections using deep learning. DESIGN Training and evaluation of a neural network were performed using a total of n=1206 tissue sections with comprehensive multi-omic data from three independent datasets (training on FOCUS trial, n=278 patients; test on rectal cancer biopsies, GRAMPIAN cohort, n=144 patients; and The Cancer Genome Atlas (TCGA), n=430 patients). Ground truth CMS calls were ascertained by matching random forest and single sample predictions from CMS classifier. RESULTS Image-based CMS (imCMS) accurately classified slides in unseen datasets from TCGA (n=431 slides, AUC)=0.84) and rectal cancer biopsies (n=265 slides, AUC=0.85). imCMS spatially resolved intratumoural heterogeneity and provided secondary calls correlating with bioinformatic prediction from molecular data. imCMS classified samples previously unclassifiable by RNA expression profiling, reproduced the expected correlations with genomic and epigenetic alterations and showed similar prognostic associations as transcriptomic CMS. CONCLUSION This study shows that a prediction of RNA expression classifiers can be made from H&E images, opening the door to simple, cheap and reliable biological stratification within routine workflows

Image-based consensus molecular subtype classification (imCMS) of colorectal cancer using deep learning

Objective Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), histological grading is a poor predictor of disease progression, and consensus molecular subtypes (CMSs) cannot be distinguished without gene expression profiling. We hypothesise that image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data and to resolve unclassifiable or heterogeneous cases. In this study, we present an image-based approach to predict CRC CMS from standard H&E sections using deep learning. Design Training and evaluation of a neural network were performed using a total of n=1206 tissue sections with comprehensive multi-omic data from three independent datasets (training on FOCUS trial, n=278 patients; test on rectal cancer biopsies, GRAMPIAN cohort, n=144 patients; and The Cancer Genome Atlas (TCGA), n=430 patients). Ground truth CMS calls were ascertained by matching random forest and single sample predictions from CMS classifier. Results Image-based CMS (imCMS) accurately classified slides in unseen datasets from TCGA (n=431 slides, AUC)=0.84) and rectal cancer biopsies (n=265 slides, AUC=0.85). imCMS spatially resolved intratumoural heterogeneity and provided secondary calls correlating with bioinformatic prediction from molecular data. imCMS classified samples previously unclassifiable by RNA expression profiling, reproduced the expected correlations with genomic and epigenetic alterations and showed similar prognostic associations as transcriptomic CMS. Conclusion This study shows that a prediction of RNA expression classifiers can be made from H&E images, opening the door to simple, cheap and reliable biological stratification within routine workflows

Upper limits on the strength of periodic gravitational waves from PSR J1939+2134

The first science run of the LIGO and GEO gravitational wave detectors presented the opportunity to test methods of searching for gravitational waves from known pulsars. Here we present new direct upper limits on the strength of waves from the pulsar PSR J1939+2134 using two independent analysis methods, one in the frequency domain using frequentist statistics and one in the time domain using Bayesian inference. Both methods show that the strain amplitude at Earth from this pulsar is less than a few times $10^{-22}$.Comment: 7 pages, 1 figure, to appear in the Proceedings of the 5th Edoardo Amaldi Conference on Gravitational Waves, Tirrenia, Pisa, Italy, 6-11 July 200