Mentor Where You Are: Informal Mentoring of Student Workers

This paper reviews the Transportation Security Administration's forthcoming computerized profiling system called "Secure Flight." Secure Flight is the latest generation of so-called "computer assisted passenger pre-screening systems." Such systems invite considerable privacy and civil liberty concerns, evoking references to an Orwellian society. This article confronts the central legal, political, and social tension borne of profiling systems such as Secure Flight, namely the conflict between the ideal of a "right to be let alone" (Warren and Brandeis 1890) relative to the post-Sept. 11 ultimatum of former American Airlines Chairman and CEO Robert L. Crandall (2002): "You want to travel on the airline system? You give up your privacy. You don't want to give up your privacy? Don't fly. Your privacy isn't equal to the safety of the rest of us.

Selection of housekeeping genes for gene expression studies in human reticulocytes using real-time PCR

BACKGROUND: Control genes, which are often referred to as housekeeping genes, are frequently used to normalise mRNA levels between different samples. However, the expression level of these genes may vary among tissues or cells and may change under certain circumstances. Thus, the selection of housekeeping genes is critical for gene expression studies. To address this issue, 7 candidate housekeeping genes including several commonly used ones were investigated in isolated human reticulocytes. For this, a simple ΔCt approach was employed by comparing relative expression of 'pairs of genes' within each sample. On this basis, stability of the candidate housekeeping genes was ranked according to repeatability of the gene expression differences among 31 samples. RESULTS: Initial screening of the expression pattern demonstrated that 1 of the 7 genes was expressed at very low levels in reticulocytes and was excluded from further analysis. The range of expression stability of the other 6 genes was (from most stable to least stable): GAPDH (glyceraldehyde 3-phosphate dehydrogenase), SDHA (succinate dehydrogenase), HPRT1 (hypoxanthine phosphoribosyl transferase 1), HBS1L (HBS1-like protein) and AHSP (alpha haemoglobin stabilising protein), followed by B2M (beta-2-microglobulin). CONCLUSION: Using this simple approach, GAPDH was found to be the most suitable housekeeping gene for expression studies in reticulocytes while the commonly used B2M should be avoided

Semantics for Noninterference with Interaction Trees

Noninterference is the strong information-security property that a program does not leak secrets through publicly-visible behavior. In the presence of effects such as nontermination, state, and exceptions, reasoning about noninterference quickly becomes subtle. We advocate using interaction trees (ITrees) to provide compositional mechanized proofs of noninterference for multi-language, effectful, nonterminating programs, while retaining executability of the semantics. We develop important foundations for security analysis with ITrees: two indistinguishability relations, leading to two standard notions of noninterference with adversaries of different strength, along with metatheory libraries for reasoning about each. We demonstrate the utility of our results using a simple imperative language with embedded assembly, along with a compiler into that assembly language

Semantics for Noninterference with Interaction Trees (Artifact)

Noninterference is the strong information-security property that a program does not leak secrets through publicly-visible behavior. In the presence of effects such as nontermination, state, and exceptions, reasoning about noninterference quickly becomes subtle. We advocate using interaction trees (ITrees) to provide compositional mechanized proofs of noninterference for multi-language, effectful, nonterminating programs, while retaining executability of the semantics. We develop important foundations for security analysis with ITrees: two indistinguishability relations, leading to two standard notions of noninterference with adversaries of different strength, along with metatheory libraries for reasoning about each. We demonstrate the utility of our results using a simple imperative language with embedded assembly, along with a compiler into that assembly language

A high protein diet (3.4 g/kg/d) combined with a heavy resistance training program improves body composition in healthy trained men and women - a follow-up investigation

Background The consumption of a high protein diet (\u3e4 g/kg/d) in trained men and women who did not alter their exercise program has been previously shown to have no significant effect on body composition. Thus, the purpose of this investigation was to determine if a high protein diet in conjunction with a periodized heavy resistance training program would affect indices of body composition, performance and health. Methods Forty-eight healthy resistance-trained men and women completed this study (mean ± SD; Normal Protein group [NP n = 17, four female and 13 male]: 24.8 ± 6.9 yr; 174.0 ± 9.5 cm height; 74.7 ± 9.6 kg body weight; 2.4 ± 1.7 yr of training; High Protein group [HP n = 31, seven female and 24 male]: 22.9 ± 3.1 yr; 172.3 ± 7.7 cm; 74.3 ± 12.4 kg; 4.9 ± 4.1 yr of training). Moreover, all subjects participated in a split-routine, periodized heavy resistance-training program. Training and daily diet logs were kept by each subject. Subjects in the NP and HP groups were instructed to consume their baseline (~2 g/kg/d) and \u3e3 g/kg/d of dietary protein, respectively. Results Subjects in the NP and HP groups consumed 2.3 and 3.4 g/kg/day of dietary protein during the treatment period. The NP group consumed significantly (p \u3c 0.05) more protein during the treatment period compared to their baseline intake. The HP group consumed more (p \u3c 0.05) total energy and protein during the treatment period compared to their baseline intake. Furthermore, the HP group consumed significantly more (p \u3c 0.05) total calories and protein compared to the NP group. There were significant time by group (p ≤ 0.05) changes in body weight (change: +1.3 ± 1.3 kg NP, −0.1 ± 2.5 HP), fat mass (change: −0.3 ± 2.2 kg NP, −1.7 ± 2.3 HP), and % body fat (change: −0.7 ± 2.8 NP, −2.4 ± 2.9 HP). The NP group gained significantly more body weight than the HP group; however, the HP group experienced a greater decrease in fat mass and % body fat. There was a significant time effect for FFM; however, there was a non-significant time by group effect for FFM (change: +1.5 ± 1.8 NP, +1.5 ± 2.2 HP). Furthermore, a significant time effect (p ≤ 0.05) was seen in both groups vis a vis improvements in maximal strength (i.e., 1-RM squat and bench) vertical jump and pull-ups; however, there were no significant time by group effects (p ≥ 0.05) for all exercise performance measures. Additionally, there were no changes in any of the blood parameters (i.e., basic metabolic panel). Conclusion Consuming a high protein diet (3.4 g/kg/d) in conjunction with a heavy resistance-training program may confer benefits with regards to body composition. Furthermore, there is no evidence that consuming a high protein diet has any deleterious effects

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