Old proteins and the Achilles heel of mass spectrometry. The role of proteomics in the etiology of human cataract

Proteomics may have enabled the root cause of a major human-blinding condition, age-related cataract, to be established. Cataract appears to result from the spontaneous decomposition of long-lived macromolecules in the human lens, and recent proteomic analysis has enabled both the particular crystallins, and the specific sites of amino acid modification within each polypeptide, to be identified. Analysis of proteins from cataract lenses has demonstrated that there are key sites on some structural proteins that show a consistently greater degree of deterioration than age-matched normal lenses. Proteomic analysis, using MS, revealed that the most abundant posttranslational modification of aged lens proteins is racemization. This is somewhat ironic, since structural isomers can be viewed as the “Achilles heel” of MS and there are typically few, if any, differences in the MS/MS spectra of tryptic peptides containing one d -amino acid. It is proposed that once a certain level of spontaneous PTM at key sites occurs, that protein–protein interactions are disrupted, and binding of complexes to cell membranes takes place that impairs cell-to-cell communication. These findings may apply more widely to age-related human diseases, in particular where the deterioration of long-lived proteins is a crucial component in the etiology.     

Proteomic analysis of early urinary biomarkers of renal changes in type 2 diabetic patients

Diabetic nephropathy (DN) is a complication associated with diabetes, leading to end-stage renal disease (ESRD). Despite significant progress in understanding DN, the cellular mechanisms leading to the renal damage are incompletely defined. In this study, with the aim to identify urine biomarkers for the early renal alterations in type 2 diabetes mellitus (T2D), we performed urinary proteomic analysis of 10 normoalbuminuric patients with T2D, 12 patients with type 2 DN (T2DN), and 12 healthy subjects. Proteins were separated by 2-DE and identified with ESI-Q-TOF MS/MS. Comparing the patients proteomic profiles with those of normal subjects, we identified 11 gradually differently changed proteins. The decreased proteins were the prostatic acid phosphatase precursor, the ribonuclease and the kallikrein-3. Eight proteins were progressively increased in both patients groups: transthyretin precursor, Ig κ chain C region, Ig κ chain V-II region Cum, Ig κ-chain V-III region SIE, carbonic anhydrase 1, plasma retinol-binding protein, β-2-microglobulin precursor, β-2-glycoprotein 1. The proteomic analysis allowed us to identify several increased urinary proteins, not only in T2DN but also in T2D patients in which the microalbuminuria was in the normal range. These patterns of urinary proteins might represent a potential tool for a better understanding of diabetic renal damage.     

Development of a multi-parametric model predictive control algorithm for insulin delivery in type 1 diabetes mellitus using clinical parameters

A multi-parametric model predictive control (mpMPC) algorithm for subcutaneous insulin delivery for individuals with type 1 diabetes mellitus (T1DM) that is computationally efficient, robust to variations in insulin sensitivity, and involves minimal burden for the user is proposed. System identification was achieved through impulse response tests feasible for ambulatory conditions on the UVa/Padova simulator adult subjects with T1DM. An alternative means of system identification using readily available clinical parameters was also investigated. A safety constraint was included explicitly in the algorithm formulation using clinical parameters typical of those available to an attending physician. Closed-loop simulations were carried out with daily consumption of 200 g carbohydrate. Controller robustness was assessed by subject/model mismatch scenarios addressing daily, simultaneous variation in insulin sensitivity and meal size with the addition of Gaussian white noise with a standard deviation of 10%. A second-order-plus-time-delay transfer function model fit the validation data with a mean (coefficient of variation) root-mean-square-error (RMSE) of 26 mg/dL (19%) for a 3 h prediction horizon. The resulting control law maintained a low risk Low Blood Glucose Index without any information about carbohydrate consumption for 90% of the subjects. Low-order linear models with clinically meaningful parameters thus provided sufficient information for a model predictive control algorithm to control glycemia. The use of clinical knowledge as a safety constraint can reduce hypoglycemic events, and this same knowledge can further improve glycemic control when used explicitly as the controller model. The resulting mpMPC algorithm was sufficiently compact to be implemented on a simple electronic device.     

Estimation of insulin sensitivity in diabetic Göttingen Minipigs

In patients with type 1 diabetes mellitus, insulin sensitivity is a parameter which strongly affects insulin therapy. Due to its time-dependent variation, this parameter can improve the strategy for automatic closed-loop blood glucose control. The aim of this work is to estimate the insulin sensitivity of patients with type 1 diabetes mellitus based on measured blood glucose concentrations. For this, an Extended Kalman Filter is used, based on a simplified version of the well-known Sorensen model. The compartment model of Sorensen was adapted to the glucose metabolic behaviour in diabetic Göttingen Minipigs by means of experimental data and reduced by neglecting unobservable state variables. Here, the Extended Kalman Filter is designed for simultaneous state and parameter estimation of insulin sensitivity using the insulin infusion rate and the meal size as input signals, and measurements of blood glucose concentration as output signal. The performance of the Extended Kalman Filter was tested in in silico studies using the minipig model, and is analysed by comparing the output signal of the filter with measurement data from the animal trials.     

知识发现在2型糖尿病数据处理中的应用研究

目的:首次将知识发现理论引入到2型糖尿病发病相关因素数据处理中,从大量实测数据中识别出有效的、潜在的、有用的、可理解的发病规律。方法:根据2型糖尿病数据的特点,选用数据挖掘C4.5算法对17072条有效的整群抽样横断面健康调查数据进行决策树分类。结果:通过训练模型给出糖尿病患病与否的决策分类树,该决策树可以直观地给出发病相关因素的不同层次的相对影响,经实验测试结果对于未患病的正确识别率为92.05%,对于患病的正确识别率为80.90%,同时得出了血糖值为5.85的分类临界值。结论:决策分类树结果同目前认识的高危因素趋于一致,说明数据挖掘C4.5算法适用于2型糖尿病的发病相关因素数据分析处理,是2型糖尿病数据处理的一种新方法,其在疾病的宏观控制中有着广阔的应用前景。     

2型糖尿病发病危险因素及其特征提取技术

综合选用数据挖掘中的EM聚类算法和C4.5分类算法，设计并进行了一系列以研究2型糖尿病发病危险因素与血糖变化关系为目的的实验。研究结果包括：发现了新的血糖门限值5.26和未发病门限值4.28，发现和验证了影响最大的8个重要发病危险因素及其对应的一系列重要临界值，定性定量相结合地给出了各个重要发病危险因素的影响程度在血糖值不同预警门限值下的变化关系等。     

The synergic role of sociotechnical and personal characteristics on work injuries in mines

Occupational injuries in mines are attributed to many factors. In this study, an attempt was made to identify the various factors related to work injuries in mines and to estimate their effects on work injuries to mine workers. An accident path model was developed to estimate the pattern and strength of relationships amongst the personal and sociotechnical variables in accident/injury occurrences. The input data for the model were the correlation matrix of 18 variables, which were collected from the case study mines. The case study results showed that there are sequential interactions amongst the sociotechnical and personal factors leading to accidents/injuries in mines. Amongst the latent endogenous constructs, job dissatisfaction and safe work behaviour show a significant positive and negative direct relationship with work injury, respectively. However, the construct safety environment has a significant negative indirect relationship with work injury. The safety environment is negatively affected by work hazards and positively affected by social support. The safety environment also shows a significant negative relationship with job stress and job dissatisfaction. However, negative personality has no significant direct or indirect effect on work injury, but it has a significant negative relationship with safe work behaviour. The endogenous construct negative personality is positively influenced by job stress and negatively influenced by social support.     

Pharmacokinetics of insulin lispro in type 2 diabetes during closed-loop insulin delivery

Insulin pharmacokinetics is not well understood during continuous subcutaneous insulin infusion in type 2 diabetes (T2D). We analyzed data collected in 11 subjects with T2D [6 male, 9 white European and two of Indian ethnicity; age 59.7(12.1) years, BMI 30.1(3.9) kg/m², fasting C-peptide 1002.2(365.8) pmol/l, fasting plasma glucose 9.6(2.2) mmol/l, diabetes duration 8.0(6.2) years and HbA1c 8.3(0.8)%; mean(SD)] who underwent a 24-h study investigating closed-loop insulin delivery at the Wellcome Trust Clinical Research Facility, Cambridge, UK. Subcutaneous delivery of insulin lispro was modulated every 15 min according to a model predictive control algorithm. Two complementary insulin assays facilitated discrimination between exogenous (lispro) and endogenous plasma insulin concentrations measured every 15–60 min. Lispro pharmacokinetics was represented by a linear two-compartment model whilst parameters were estimated using a Bayesian approach applying a closed-form model solution. The time-to-peak of lispro absorption (t_max) was 109.6 (75.5–120.5) min [median (interquartile range)] and the metabolic clearance rate (MCR_I) 1.26 (0.87–1.56) × 10⁻² l/kg/min. MCR_I was negatively correlated with fasting C-peptide (r_s = −0.84; P = .001) and with fasting plasma insulin concentration (r_s = −0.79; P = .004). In conclusion, compartmental modelling adequately represents lispro kinetics during continuous subcutaneous insulin infusion in T2D. Fasting plasma C-peptide or fasting insulin may be predictive of lispro metabolic clearance rate in T2D but further investigations are warranted.     

对Java中泛型的研究与探讨

本文阐述了Java中泛型的概念和特点,介绍了简单泛型创建和使用,探讨了泛型应用中存在的陷井。泛型最重要的特点是类型安全,泛型还可以消除类型转换等。泛型的创建和使用比较简单,但在使用中存在一定的陷阱,需要引起程序员的注意。     

The role of emotion in computer-mediated communication: A review

It has been argued that the communication of emotions is more difficult in computer-mediated communication (CMC) than in face-to-face (F2F) communication. The aim of this paper is to review the empirical evidence in order to gain insight in whether emotions are communicated differently in these different modes of communication. We review two types of studies: (1) studies that explicitly examine discrete emotions and emotion expressions, and (2) studies that examine emotions more implicitly, namely as self-disclosure or emotional styles. Our conclusion is that there is no indication that CMC is a less emotional or less personally involving medium than F2F. On the contrary, emotional communication online and offline is surprisingly similar, and if differences are found they show more frequent and explicit emotion communication in CMC than in F2F.     

The role of the New Zealand forest industry injury surveillance system in a strategic ergonomics,safety and health research programme

Forestry is among the most hazardous of all industry sectors internationally. In New Zealand, highest work-related morbidity and mortality incidence rates are found in the logging sector. The New Zealand Forest Industry Accident Reporting Scheme (ARS) is a forestry injury surveillance system maintained by the Centre for Human Factors and Ergonomics (COHFE). The paper highlights the pivotal role of an industry-wide injury surveillance system in an industry-specific strategic research programme, giving examples of the use of ARS data in identification of priority areas for ergonomics, safety and health research attention, for safety awareness feedback to the industry, and in the evaluation of injury countermeasures. An analysis of injury patterns and trends for one high-risk forestry operation (skid work) is presented to illustrate both the capabilities and limitations of the ARS in its present stage of development.     

The design and implementation of parametric types in Pascal

Parametric types offer an attractive solution to the problems of dealing with arrays in Pascal. These problems arise from the use of strong, static type checking, especially of array-type procedure arguments. Parametric types provide solutions both for the array procedure argument problem and allow the consistent inclusion of arrays with dynamic bounds. A parametric type mechanism is proposed and design issues are discussed. The inclusion of parametric types has major effects on the implementation of a language like Pascal. The implementation issues and implementation versus design tradeoffs are examined. The implementation strategy used can be extended to accommodate the standard and generic types in Ada.     

Comparison of interval and Monte Carlo simulation for the prediction of postprandial glucose under uncertainty in type 1 diabetes mellitus

In this paper, the problem of tackling uncertainty in the prediction of postprandial blood glucose is analyzed. Two simulation approaches, Monte Carlo and interval models, are studied and compared. Interval simulation is carried out using modal interval analysis. Simulation of a glucoregulatory model with uncertainty in insulin sensitivities, glucose absorption and food intake is carried out using both methods. Interval simulation is superior in predicting all severe and mild hyper- and hypoglycemia episodes. Furthermore, much less computational time is required for interval simulation than for Monte Carlo simulation.     

The role of respiratory measures to assess mental load in pilot selection

While cardiovascular measures have a long tradition of being used to determine operator load, responsiveness of the respiratory system to mental load has rarely been investigated. In this study, we assessed basic and variability measures of respiration rate (RR), partial pressure of end-tidal carbon dioxide (petCO₂) as well as performance measures in 63 male pilot candidates during completion of a complex cognitive task and subsequent recovery. Mental load was associated with an increase in RR and a decrease in respiratory variability. A significant decrease was also found for petCO₂. RR and respiratory variability showed partial and complete effects of recovery, respectively, whereas petCO₂ did not return to baseline level. Overall, a good performance was related to a stronger reactivity in RR. Our findings suggest that respiratory parameters would be a useful supplement to common measures for the assessment of mental load in pilot selection.

Practitioner Summary: Respiratory measures are a promising yet poorly investigated approach to monitor operator load. For pilot selection, we assessed respiration in response to multitasking in 63 candidates. Task-related changes as well as covariation with performance strongly support the consideration of respiratory parameters when evaluating reactivity to mental load.     

The role of scaffolding and motivation in CSCL

Recent findings from research into Computer-Supported Collaborative Learning (CSCL) have indicated that not all learners are able to successfully learn in online collaborative settings. Given that most online settings are characterised by minimal guidance, which require learners to be more autonomous and self-directed, CSCL may provide conditions more conducive to learners comfortable with greater autonomy. Using quasi-experimental research, this paper examines the impact of a redesign of an authentic CSCL environment, based upon principles of Problem-Based Learning, which aimed to provide a more explicit scaffolding of the learning phases for students. It was hypothesised that learners in a redesigned ‘Optima’ environment would reach higher levels of knowledge construction due to clearer scaffolding. Furthermore, it was expected that the redesign would produce a more equal spread in contributions to discourse for learners with different motivational profiles.     

The role of memory and concepts in learning

The extent to which concepts, memory, and planning are necessary to the simulation of intelligent behavior is a fundamental philosophical issue in Artificial Intelligence. An active and productive segement of the AI community has taken the position that multiple low-level agents, properly organized, can account for high-level behavior. Empirical research on these questions with fully operational systems has been restricted to mobile robots that do simple tasks. This paper recounts experiments with Hoyle, a system in a cerebral, rather than a physical, domain. The program learns to perform well and quickly, often outpacing its human creators at two-person, perfect information board games. Hoyle demonstrates that a surprising amount of intelligent behavior can be treated as if it were situation-determined, that often planning is unnecessary, and that the memory required to support this learning is minimal. Concepts, however, are crucial to this reactive program's ability to learn and perform.