Differences sustained between diffuse and limited forms of juvenile systemic sclerosis in expanded international cohort. www.juvenile-scleroderma.com

OBJECTIVES: To evaluate the baseline clinical characteristics of juvenile systemic sclerosis (jSSc) patients in the international Juvenile SSc Inception Cohort (jSScC), compare these characteristics between the classically defined diffuse (dcjSSc) and limited cutaneous (lcjSSc) subtypes, and among those with overlap features. METHODS: A cross-sectional study was performed using baseline visit data. Demographic, organ system evaluation, treatment, and patient and physician reported outcomes were extracted and summary statistics applied. Comparisons between dcjSSc and lcSSc subtypes and patients with and without overlap features were performed using Chi-square and Mann Whitney U-tests. RESULTS: At data extraction 150 jSSc patients were enrolled across 42 centers, 83% were Caucasian, 80% female, dcjSSc predominated (72%), and 17% of the cohort had overlap features. Significant differences were found between dcjSSc and lcjSSc regarding the modified Rodnan Skin Score, presence of Gottron's papules, digital tip ulceration, 6 Minute walk test, composite pulmonary and cardiac involvement. All more frequent in dcSSc except for cardiac involvement. DcjSSc patients had significantly worse scores for physician rated disease activity and damage. A significantly higher occurrence of Gottron's papules, musculoskeletal involvement and composite pulmonary involvement, and significantly lower frequency of Raynaud's phenomenon, were seen in those with overlap features. CONCLUSION: Results from a large international jSSc cohort demonstrate significant differences between dcjSSc and lcjSSc patients including more globally severe disease and increased frequency of ILD in dcjSSc patients, while those with lcSSc have more frequent cardiac involvement. Those with overlap features had an unexpected higher frequency of interstitial lung disease

Assessment of protein-protein interfaces in cryo-EM derived assemblies

Structures of macromolecular assemblies derived from cryo-EM maps often contain errors that become more abundant with decreasing resolution. Despite efforts in the cryo-EM community to develop metrics for map and atomistic model validation, thus far, no specific scoring metrics have been applied systematically to assess the interface between the assembly subunits. Here, we comprehensively assessed protein–protein interfaces in macromolecular assemblies derived by cryo-EM. To this end, we developed Protein Interface-score (PI-score), a density-independent machine learning-based metric, trained using the features of protein–protein interfaces in crystal structures. We evaluated 5873 interfaces in 1053 PDB-deposited cryo-EM models (including SARS-CoV-2 complexes), as well as the models submitted to CASP13 cryo-EM targets and the EM model challenge. We further inspected the interfaces associated with low-scores and found that some of those, especially in intermediate-to-low resolution (worse than 4 Å) structures, were not captured by density-based assessment scores. A combined score incorporating PI-score and fit-to-density score showed discriminatory power, allowing our method to provide a powerful complementary assessment tool for the ever-increasing number of complexes solved by cryo-EM

Comorbidity, age, race and stage at diagnosis in colorectal cancer: a retrospective, parallel analysis of two health systems

© 2008 Zafar et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background : Stage at diagnosis plays a significant role in colorectal cancer (CRC) survival. Understanding which factors contribute to a more advanced stage at diagnosis is vital to improving overall survival. Comorbidity, race, and age are known to impact receipt of cancer therapy and survival, but the relationship of these factors to stage at diagnosis of CRC is less clear. The objective of this study is to investigate how comorbidity, race and age influence stage of CRC diagnosis. Methods : Two distinct healthcare populations in the United States (US) were retrospectively studied. Using the Cancer Care Outcomes Research and Surveillance Consortium database, we identified CRC patients treated at 15 Veterans Administration (VA) hospitals from 2003–2007. We assessed metastatic CRC patients treated from 2003–2006 at 10 non-VA, fee-for-service (FFS) practices. Stage at diagnosis was dichotomized (non-metastatic, metastatic). Race was dichotomized (white, non-white). Charlson comorbidity index and age at diagnosis were calculated. Associations between stage, comorbidity, race, and age were determined by logistic regression. Results : 342 VA and 340 FFS patients were included. Populations differed by the proportion of patients with metastatic CRC at diagnosis (VA 27% and FFS 77%) reflecting differences in eligibility criteria for inclusion. VA patients were mean (standard deviation; SD) age 67 (11), Charlson index 2.0 (1.0), and were 63% white. FFS patients were mean age 61 (13), Charlson index 1.6 (1.0), and were 73% white. In the VA cohort, higher comorbidity was associated with earlier stage at diagnosis after adjusting for age and race (odds ratio (OR) 0.76, 95% confidence interval (CI) 0.58–1.00; p = 0.045); no such significant relationship was identified in the FFS cohort (OR 1.09, 95% CI 0.82–1.44; p = 0.57). In both cohorts, no association was found between stage at diagnosis and either age or race. Conclusion : Higher comorbidity may lead to earlier stage of CRC diagnosis. Multiple factors, perhaps including increased interactions with the healthcare system due to comorbidity, might contribute to this finding. Such increased interactions are seen among patients within a healthcare system like the VA system in the US versus sporadic interactions which may be seen with FFS healthcare

Functional Assessment of Disease-Associated Regulatory Variants <i>In Vivo</i> Using a Versatile Dual Colour Transgenesis Strategy in Zebrafish

Disruption of gene regulation by sequence variation in non-coding regions of the genome is now recognised as a significant cause of human disease and disease susceptibility. Sequence variants in cis-regulatory elements (CREs), the primary determinants of spatio-temporal gene regulation, can alter transcription factor binding sites. While technological advances have led to easy identification of disease-associated CRE variants, robust methods for discerning functional CRE variants from background variation are lacking. Here we describe an efficient dual-colour reporter transgenesis approach in zebrafish, simultaneously allowing detailed in vivo comparison of spatio-temporal differences in regulatory activity between putative CRE variants and assessment of altered transcription factor binding potential of the variant. We validate the method on known disease-associated elements regulating SHH, PAX6 and IRF6 and subsequently characterise novel, ultra-long-range SOX9 enhancers implicated in the craniofacial abnormality Pierre Robin Sequence. The method provides a highly cost-effective, fast and robust approach for simultaneously unravelling in a single assay whether, where and when in embryonic development a disease-associated CRE-variant is affecting its regulatory function

Online fault diagnosis in nonlinear systems using the multiple operating regime approach

Many chemical processes exhibit highly nonlinear dynamic behavior when operated over a wide operating range. The fault diagnosis schemes based on a linear perturbation model often prove to be inadequate, with regard to addressing fault diagnosis problems in such systems. In this work, a novel multiple-operatingregimes-based technique is proposed for performing online fault diagnosis in nonlinear systems. A Bayesian approach is used to identity the combination of linear perturbation models in different operating regimes that best-represents the plant dynamics at the current operating point. Nonlinear versions of the generalized likelihood ratio (GLR) method that use multiple linear models for fault identification are proposed. The proposed multimodel based fault diagnosis approaches are computationally efficient and exploit the linearity of each submodel. To arrest the performance degradation caused by the occurrence of faults, the information provided by the fault diagnosis component is then used for online fault accommodation. The efficacy of the proposed diagnosis schemes is demonstrated by conducting simulation studies on two benchmark continuously stirred tank reactor (CSTR) systems and a high-purity binary distillation column system. Analysis of the simulation results reveals that the proposed multimodel Kalman filter-based fault diagnosis schemes outperform the linear GLR method when a nonlinear process is in a transient state over a wide operating range

Online Sensor/Actuator Failure Isolation and Reconfigurable Control Using the Generalized Likelihood Ratio Method

In processing plants, sensor and/or actuator failures can have considerable deteriorating effect on the closed-loop performance. Such failures have to be diagnosed online, as quickly as possible, and actively accommodated to arrest the performance degradation. Active failure tolerance can be achieved by employing model-based failure diagnosis techniques and redesigning/restructuring controller online. In this work, a sensor/actuator failure isolation strategy has been developed under the linear generalized likelihood ratio (GLR) framework. The strategy is then extended to isolation of sensor and actuator failures in nonlinear systems. The information on sensor/actuator failures is further used for online reconfiguration of the state estimator and the controller/control scheme. In case of sensor failure, the state estimator is reconfigured by removing the measurement of failed sensor from the measurement vector. If an observability property is preserved after sensor failure, then an inferential control scheme is employed subsequent to the failure. When an actuator failure is isolated, it is proposed to make modifications in the controller objectives or switch to a new controller to account for the loss of a degree of freedom. The efficacy of the proposed failure diagnosis and control structure reconfiguration schemes is demonstrated by conducting experimental studies on a benchmark heater mixer set up. Analysis of these results reveals that the proposed strategies are able to isolate the failures accurately and recover the closed-loop performance by online reconfiguration of the controller/control scheme

Identification of process and measurement noise covariance for state and parameter estimation using extended Kalman filter

The performance of Bayesian state estimators, such as the extended Kalman filter (EKE), is dependent on the accurate characterisation of the uncertainties in the state dynamics and in the measurements. The parameters of the noise densities associated with these uncertainties are, however, often treated as 'tuning parameters' and adjusted in an ad hoc manner while carrying out state and parameter estimation. In this work, two approaches are developed for constructing the maximum likelihood estimates (MLE) of the state and measurement noise covariance matrices from operating input-output data when the states and/or parameters are estimated using the EKF. The unmeasured disturbances affecting the process are either modelled as unstructured noise affecting all the states or as structured noise entering the process predominantly through known, but unmeasured inputs. The first approach is based on direct optimisation of the ML objective function constructed by using the innovation sequence generated from the EKF. The second approach - the extended EM algorithm - is a derivative-free method, that uses the joint likelihood function of the complete data, i.e. states and measurements, to compute the next iterate of the decision variables for the optimisation problem. The efficacy of the proposed approaches is demonstrated on a benchmark continuous fermenter system. The simulation results reveal that both the proposed approaches generate fairly accurate estimates of the noise covariances. Experimental studies on a benchmark laboratory scale heater-mixer setup demonstrate a marked improvement in the predictions of the EKE that uses the covariance estimates obtained from the proposed approaches. (C) 2011 Elsevier Ltd. All rights reserved