Definition of valid proteomic biomarkers: a bayesian solution

Clinical proteomics is suffering from high hopes generated by reports on apparent biomarkers, most of which could not be later substantiated via validation. This has brought into focus the need for improved methods of finding a panel of clearly defined biomarkers. To examine this problem, urinary proteome data was collected from healthy adult males and females, and analysed to find biomarkers that differentiated between genders. We believe that models that incorporate sparsity in terms of variables are desirable for biomarker selection, as proteomics data typically contains a huge number of variables (peptides) and few samples making the selection process potentially unstable. This suggests the application of a two-level hierarchical Bayesian probit regression model for variable selection which assumes a prior that favours sparseness. The classification performance of this method is shown to improve that of the Probabilistic K-Nearest Neighbour model

Basin structure in the two-dimensional dissipative circle map

Fractal basin structure in the two-dimensional dissipative circle map is examined in detail. Numerically obtained basin appears to be riddling in the parameter region where two periodic orbits co-exist near a boundary crisis, but it is shown to consist of layers of thin bands.Comment: published in J. Phys. Soc. Jpn., 72, 1943-1947 (2003

Liver regeneration after living donor transplantation: Adult‐to‐adult living donor liver transplantation cohort study

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109827/1/lt23966.pd

Energy expenditure during sleep, sleep deprivation and sleep following sleep deprivation in adult humans

Sleep has been proposed to be a physiological adaptation to conserve energy, but little research has examined this proposed function of sleep in humans. We quantified effects of sleep, sleep deprivation and recovery sleep on whole-body total daily energy expenditure (EE) and on EE during the habitual day and nighttime. We also determined effects of sleep stage during baseline and recovery sleep on EE. Seven healthy participants aged 22 ± 5 years (mean ± s.d.) maintained ∼8 h per night sleep schedules for 1 week before the study and consumed a weight-maintenance diet for 3 days prior to and during the laboratory protocol. Following a habituation night, subjects lived in a whole-room indirect calorimeter for 3 days. The first 24 h served as baseline – 16 h wakefulness, 8 h scheduled sleep – and this was followed by 40 h sleep deprivation and 8 h scheduled recovery sleep. Findings show that, compared to baseline, 24 h EE was significantly increased by ∼7% during the first 24 h of sleep deprivation and was significantly decreased by ∼5% during recovery, which included hours awake 25–40 and 8 h recovery sleep. During the night time, EE was significantly increased by ∼32% on the sleep deprivation night and significantly decreased by ∼4% during recovery sleep compared to baseline. Small differences in EE were observed among sleep stages, but wakefulness during the sleep episode was associated with increased energy expenditure. These findings provide support for the hypothesis that sleep conserves energy and that sleep deprivation increases total daily EE in humans

Experiments in lifelog organisation and retrieval at NTCIR

Lifelogging can be described as the process by which individuals use various software and hardware devices to gather large archives of multimodal personal data from multiple sources and store them in a personal data archive, called a lifelog. The Lifelog task at NTCIR was a comparative benchmarking exercise with the aim of encouraging research into the organisation and retrieval of data from multimodal lifelogs. The Lifelog task ran for over 4 years from NTCIR-12 until NTCIR-14 (2015.02–2019.06); it supported participants to submit to five subtasks, each tackling a different challenge related to lifelog retrieval. In this chapter, a motivation is given for the Lifelog task and a review of progress since NTCIR-12 is presented. Finally, the lessons learned and challenges within the domain of lifelog retrieval are presented

Liver transplant recipient survival benefit with living donation in the model for endstage liver disease allocation era

Receipt of a living donor liver transplant (LDLT) has been associated with improved survival compared with waiting for a deceased donor liver transplant (DDLT). However, the survival benefit of liver transplant has been questioned for candidates with Model for Endstage Liver Disease (MELD) scores <15, and the survival advantage of LDLT has not been demonstrated during the MELD allocation era, especially for low MELD patients. Transplant candidates enrolled in the Adult‐to‐Adult Living Donor Liver Transplantation Cohort Study after February 28, 2002 were followed for a median of 4.6 years. Starting at the time of presentation of the first potential living donor, mortality for LDLT recipients was compared to mortality for patients who remained on the waiting list or received DDLT (no LDLT group) according to categories of MELD score (<15 or ≥15) and diagnosis of hepatocellular carcinoma (HCC). Of 868 potential LDLT recipients (453 with MELD <15; 415 with MELD ≥15 at entry), 712 underwent transplantation (406 LDLT; 306 DDLT), 83 died without transplant, and 73 were alive without transplant at last follow‐up. Overall, LDLT recipients had 56% lower mortality (hazard ratio [HR] = 0.44, 95% confidence interval [CI] 0.32‐0.60; P < 0.0001). Among candidates without HCC, mortality benefit was seen both with MELD <15 (HR = 0.39; P = 0.0003) and MELD ≥15 (HR = 0.42; P = 0.0006). Among candidates with HCC, a benefit of LDLT was not seen for MELD <15 (HR = 0.82, P = 0.65) but was seen for MELD ≥15 (HR = 0.29, P = 0.043). Conclusion: Across the range of MELD scores, patients without HCC derived a significant survival benefit when undergoing LDLT rather than waiting for DDLT in the MELD liver allocation era. Low MELD candidates with HCC may not benefit from LDLT. (H EPATOLOGY 2011;54:1313–1321)Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86878/1/24494_ftp.pd