Noriaki Nakajima

Robust time estimation reconciles views of the antiquity of placental mammals.

Background Molecular studies have reported divergence times of modern placental orders long before the Cretaceous–Tertiary boundary and far older than paleontological data. However, this discrepancy may not be real, but rather appear because of the violation of implicit assumptions in the estimation procedures, such as non-gradual change of evolutionary rate and failure to correct for convergent evolution. Methodology/Principal Findings New procedures for divergence-time estimation robust to abrupt changes in the rate of molecular evolution are described. We used a variant of the multidimensional vector space (MVS) procedure to take account of possible convergent evolution. Numerical simulations of abrupt rate change and convergent evolution showed good performance of the new procedures in contrast to current methods. Application to complete mitochondrial genomes identified marked rate accelerations and decelerations, which are not obtained with current methods. The root of placental mammals is estimated to be ∼18 million years more recent than when assuming a log Brownian motion model. Correcting the pairwise distances for convergent evolution using MVS lowers the age of the root about another 20 million years compared to using standard maximum likelihood tree branch lengths. These two procedures combined revise the root time of placental mammals from around 122 million years ago to close to 84 million years ago. As a result, the estimated distribution of molecular divergence times is broadly consistent with quantitative analysis of the North American fossil record and traditional morphological views. Conclusions/Significance By including the dual effects of abrupt rate change and directly accounting for convergent evolution at the molecular level, these estimates provide congruence between the molecular results, paleontological analyses and morphological expectations. The programs developed here are provided along with sample data that reproduce the results of this study and are especially applicable studies using genome-scale sequence lengths.

Adaptive Threonine Increase in Transmembrane Regions of Mitochondrial Proteins in Higher Primates

Background The mitochondrial (mt) gene tree of placental mammals reveals a very strong acceleration of the amino acid (AA) replacement rate and a change in AA compositional bias in the lineage leading to the higher primates (simians), in contrast to the nuclear gene tree. Whether this acceleration and compositional bias were caused by adaptive evolution at the AA level or directional mutation pressure at the DNA level has been vigorously debated. Methodology/Principal Findings Our phylogenetic analysis indicates that the rate acceleration in the simian lineage is accompanied by a marked increase in threonine (Thr) residues in the transmembrane helix regions of mt DNA-encoded proteins. This Thr increase involved the replacement of hydrophobic AAs in the membrane interior. Even after accounting for lack of independence due to phylogeny, a regression analysis reveals a statistical significant positive correlation between Thr composition and longevity in primates. Conclusion/Significance Because crucial roles of Thr and Ser in membrane proteins have been proposed to be the formation of hydrogen bonds enhancing helix-helix interactions, the Thr increase detected in the higher primates might be adaptive by serving to reinforce stability of mt proteins in the inner membrane. The correlation between Thr composition in the membrane interior and the longevity of animals is striking, especially because some mt functions are thought to be involved in aging.

Use of a Latent Topic Model for Characteristic Extraction from Health Checkup Questionnaire Data.

OBJECTIVES When patients complete questionnaires during health checkups, many of their responses are subjective, making topic extraction difficult. Therefore, the purpose of this study was to develop a model capable of extracting appropriate topics from subjective data in questionnaires conducted during health checkups. METHODS We employed a latent topic model to group the lifestyle habits of the study participants and represented their responses to items on health checkup questionnaires as a probability model. For the probability model, we used latent Dirichlet allocation to extract 30 topics from the questionnaires. According to the model parameters, a total of 4381 study participants were then divided into groups based on these topics. Results from laboratory tests, including blood glucose level, triglycerides, and estimated glomerular filtration rate, were compared between each group, and these results were then compared with those obtained by hierarchical clustering. RESULTS If a significant (p < 0.05) difference was observed in any of the laboratory measurements between groups, it was considered to indicate a questionnaire response pattern corresponding to the value of the test result. A comparison between the latent topic model and hierarchical clustering grouping revealed that, in the latent topic model method, a small group of participants who reported having subjective signs of urinary disorder were allocated to a single group. CONCLUSIONS The latent topic model is useful for extracting characteristics from a small number of groups from questionnaires with a large number of items. These results show that, in addition to chief complaints and history of past illness, questionnaire data obtained during medical checkups can serve as useful judgment criteria for assessing the conditions of patients.

An Education Support System with Anonymized Medical Data Based on Thin Client System

An education support system for medical analysis with anonymized data based on thin client system is proposed for medical school students from real data in Hospital Information System (HIS). The target anonymized data in the proposed system is modified for data cleansing process from change of HIS because the data means longitudinal original data. The general user executes medical analysis based on thin client interface with diskless system. The proposed system is applied to Kochi Medical School and assists extraction of new medical knowledge from real medical data in hospital.

Effects of aging on stress-related responses of serotonergic neurons in the dorsal raphe nucleus of male rats

Responses to various stressors in the brain change with age. However, little is known about the neural mechanisms underlying age-dependent changes in stress responses. It is known that serotonin, a stress-related transmitter, is closely related with the regulation of stress responses in the brain and that serotonergic function is modulated by various factors, including estrogen, in both sexes. In the present study, to elucidate the effects of aging on stress responses in serotonergic neurons, we examined the expression levels of tryptophan hydroxylase (TPH; a marker of serotonergic neurons) in the dorsal, ventral and lateral parts of the dorsal raphe nucleus (DRN) in young and old intact male rats. In young males, repeated restraint stress significantly increased the number of TPH-positive cells in all subdivisions of the DRN. In contrast, the stress-induced increase in TPH expression was only observed in the ventral part of the DRN in old males. Pretreatment with an estrogen receptor β antagonist had no effect on the number of TPH-positive cells in the dorsal and lateral DRN in young stressed males, whereas the antagonist decreased the number of TPH-positive cells in all DRN subdivisions in old stressed males. Our results suggest that the effects of repeated stress exposure on the expression of TPH in serotonergic neurons in the DRN change with age and that estrogenic effects via estrogen receptor β on TPH expression in stressed old males differ from those in young males.

Prediction model for glucose metabolism based on lipid metabolism.

OBJECTIVES We developed a robust, long-term clinical prediction model to predict conditions leading to early diabetes using laboratory values other than blood glucose and insulin levels. Our model protects against missing data and noise that occur during long-term analysis. METHODS RESULTS of a 75-g oral glucose tolerance test (OGTT) were divided into three groups: diabetes, impaired glucose tolerance (IGT), and normal (n = 114, 235, and 325, respectively). For glucose metabolic and lipid metabolic parameters, near 30-day mean values and 10-year integrated values were compared. The relation between high-density lipoprotein cholesterol (HDL-C) and variations in HbA1c was analyzed in 158 patients. We also constructed a state space model consisting of an observation model (HDL-C and HbA1c) and an internal model (disorders of lipid metabolism and glucose metabolism) and applied this model to 116 cases. RESULTS The root mean square error between the observed HbA1c and predicted HbA1c was 0.25. CONCLUSIONS In the observation model, HDL-C levels were useful for prediction of increases in HbA1c. Even with numerous missing values over time, as occurs in clinical practice, clinically valid predictions can be made using this state space model.

Calculation algorithm of hepatorenal contrast in ultrasonic images based on fuzzy inference

A calculation algorithm for hepatorenal contrast from real ultrasonic images is proposed for analysis research of time series change in patient condition by aging. It provides automatic calculation of kidney pelvis position based on fuzzy inference, which detects kidney and liver region for hepatorenal contrast to calculate. Experimental calculation results for 150 ultrasonic images taken in real treatment from Kochi Medical School hospital show that accuracy of kidney pelvis detection is 93% and that correlation coefficient of hepatorenal contrast with normal gamma-GT is 0.82. The proposed algorithm is being considered for use in analysis of condition change in Center of Medical Information Science, Kochi Medical School.

Baseline estimation for serum creatinine for definition of acute kidney injury

Estimation process for baseline of Serum Creatinine (SCr) is constructed for Acute Kidney Injury (AKI) where baseline is necessary for definition. In order to deal with missing value, the estimation process calculates the baseline values for different stable interval, which the definition process select the appropriate interval based on the number of days from the target test day. The estimation process is applied to the real SCr data in Kochi Medical School hospital. The experiment result shows that the process extracts the 12.7% of non AKI patients as possible AKI. The constructed process can provides the appropriate non AKI patient data for retrospective study with hospital data.

Level evaluation system for cardiotocography

Level evaluation system for real cardiotocography (CTG) data of high risk patients is constructed in order to use the CTG feature as basic data in e-Learning materials. The constructed system consists of two parts, preprocessing of signal data by particle filter and level evaluation by fuzzy inference. For check of the validity, relation between calculated level and cord blood pH are analyzed with the real medical data in Kochi Medical School hospital. The experimental results show that the CTG data of the abnormal delivery situations show high level (correlation coefficient: -0.07). The proposed system provides basic educational materials by extracting signal characteristics like deceleration.

Estimation Algorithm of Butyrylcholinesterase for Cirrhosis using Neural Network

Abstract An estimation algorithm for Butyrylcholinesterase (BChE) in Kochi Medical School Hospital is proposed for early prediction of cirrhosis. It provides automatic calculation of the similarity of input BChE for known learning data constructed by Neural Network, which estimates subsequent value of input value using the similar learning data. The influence for BChE of interferon therapy is considered to determine start day for estimation and to calculate the similarity. Experimental estimation results for real data show that mean difference between the real modeled data and the estimated value is 18.4 in 32 cirrhosis patients and that the proposed algorithm distinguish cirrhosis patients from the other patients. The proposed algorithm constructs learning database automatically and provides early prediction using screening study in hospital.