Extraluminal Colonic Carcinoma Invading into Kidney: A Case Report and Review of the Literature

Renal metastasis from primary colon cancer is very rare, comprising less than 3% of secondary renal neoplasms. There are just 11 cases reported in the medical literature of colonic adenocarcinoma metastatic to the kidney. Of these cases, none occurred via direct invasion. We report a unique case of a 51-year-old female with extraluminal colonic adenocarcinoma which directly invaded into the kidney. Additionally, we investigate the causal relationship between the site of invasion and a previous stab injury by reviewing the role of the peritoneum and Gerota's fascia in preventing the spread of metastatic cancer into the perirenal space. Due to the rarity of this event, we present this case including a review of the existing literature relative to the diagnosis and treatment

TFE3 Translocation-Associated Renal Cell Carcinoma Presenting as Avascular Necrosis of the Femur in a 19-Year-Old Patient: Case Report and Review of the Literature

In the United States, renal cell carcinoma (RCC) accounts for approximately 3% of adult malignancies and 90–95% of all neoplasms arising from the kidney. According to the National Cancer Institute, 58 240 new cases and 13 040 deaths from renal cancer will occur in 2010. RCC usually occurs in older adults between the ages of 50 and 70 and is rare in young adults and children. We describe a case of a TFE3 translocation-associated RCC in a 19-year-old patient presenting as avascular necrosis of the femur. Due to the rarity of this malignancy, we present this case including a review of the existing literature relative to diagnosis and treatment

SPPL: Probabilistic Programming with Fast Exact Symbolic Inference

We present the Sum-Product Probabilistic Language (SPPL), a new probabilistic programming language that automatically delivers exact solutions to a broad range of probabilistic inference queries. SPPL translates probabilistic programs into sum-product expressions, a new symbolic representation and associated semantic domain that extends standard sum-product networks to support mixed-type distributions, numeric transformations, logical formulas, and pointwise and set-valued constraints. We formalize SPPL via a novel translation strategy from probabilistic programs to sum-product expressions and give sound exact algorithms for conditioning on and computing probabilities of events. SPPL imposes a collection of restrictions on probabilistic programs to ensure they can be translated into sum-product expressions, which allow the system to leverage new techniques for improving the scalability of translation and inference by automatically exploiting probabilistic structure. We implement a prototype of SPPL with a modular architecture and evaluate it on benchmarks the system targets, showing that it obtains up to 3500x speedups over state-of-the-art symbolic systems on tasks such as verifying the fairness of decision tree classifiers, smoothing hidden Markov models, conditioning transformed random variables, and computing rare event probabilities

The design of the DUPIC spent fuel bundle counter

A neutron coincidence detector had been designed to measure the amount of curium in the fuel bundles and associated process samples used in the direct use of plutonium in Canadian deuterium-uranium (CANDU) fuel cycle. All of the sample categories are highly radioactive from the fission products contained in the pressurized water reactor (PWR) spent fuel feed stock. Substantial shielding is required to protect the He-3 detectors from the intense gamma rays. The Monte Carlo neutron and photon calculational code has been used to design the counter with a uniform response profile along the length of the CANDU-type fuel bundle. Other samples, including cut PWR rods, process powder, waste, and finished rods, can be measured in the system. This report describes the performance characteristics of the counter and support electronics. 3 refs., 23 figs., 6 tabs

Optimal Approximate Sampling from Discrete Probability Distributions

This paper addresses a fundamental problem in random variate generation: given access to a random source that emits a stream of independent fair bits, what is the most accurate and entropy-efficient algorithm for sampling from a discrete probability distribution $(p_1, \dots, p_n)$, where the probabilities of the output distribution $(\hat{p}_1, \dots, \hat{p}_n)$ of the sampling algorithm must be specified using at most $k$ bits of precision? We present a theoretical framework for formulating this problem and provide new techniques for finding sampling algorithms that are optimal both statistically (in the sense of sampling accuracy) and information-theoretically (in the sense of entropy consumption). We leverage these results to build a system that, for a broad family of measures of statistical accuracy, delivers a sampling algorithm whose expected entropy usage is minimal among those that induce the same distribution (i.e., is "entropy-optimal") and whose output distribution $(\hat{p}_1, \dots, \hat{p}_n)$ is a closest approximation to the target distribution $(p_1, \dots, p_n)$ among all entropy-optimal sampling algorithms that operate within the specified $k$-bit precision. This optimal approximate sampler is also a closer approximation than any (possibly entropy-suboptimal) sampler that consumes a bounded amount of entropy with the specified precision, a class which includes floating-point implementations of inversion sampling and related methods found in many software libraries. We evaluate the accuracy, entropy consumption, precision requirements, and wall-clock runtime of our optimal approximate sampling algorithms on a broad set of distributions, demonstrating the ways that they are superior to existing approximate samplers and establishing that they often consume significantly fewer resources than are needed by exact samplers

Design impacts of safeguards and security requirements for a US MOX fuel fabrication facility

The disposition of plutonium that is no longer required for the nation`s defense is being structured to mitigate risks associated with the material`s availability. In the 1997 Record of Decision, the US Government endorsed a dual-track approach that could employ domestic commercial reactors to effect the disposition of a portion of the plutonium in the form of mixed oxide (MOX) reactor fuels. To support this decision, the Office of Materials Disposition requested preparation of a document that would review US requirements for safeguards and security and describe their impact on the design of a MOX fuel fabrication facility. The intended users are potential bidders for the construction and operation of the facility. The document emphasizes the relevant DOE Orders but also considers the Nuclear Regulatory Commission (NRC) requirements. Where they are significantly different, the authors have highlighted this difference and provided guidance on the impact to the facility design. Finally, the impacts of International Atomic Energy Agency (IAEA) safeguards on facility design are discussed. Security and materials control and accountability issues that influence facility design are emphasized in each area of discussion. This paper will discuss the prepared report and the issues associated with facility design for implementing practical, modern safeguards and security systems into a new MOX fuel fabrication facility

Rapid haplotype inference for nuclear families

Hapi is a new dynamic programming algorithm that ignores uninformative states and state transitions in order to efficiently compute minimum-recombinant and maximum likelihood haplotypes. When applied to a dataset containing 103 families, Hapi performs 3.8 and 320 times faster than state-of-the-art algorithms. Because Hapi infers both minimum-recombinant and maximum likelihood haplotypes and applies to related individuals, the haplotypes it infers are highly accurate over extended genomic distances.National Institutes of Health (U.S.) (NIH grant 5-T90-DK070069)National Institutes of Health (U.S.) (Grant 5-P01-NS055923)National Science Foundation (U.S.) (Graduate Research Fellowship