Therapeutic potential of endothelin receptor modulators: lessons from human clinical trials.

The endothelin system, and in particular endothelin receptors, are targets for therapeutic intervention in human diseases. Endothelin receptor antagonists have reached clinical use for treating pulmonary arterial hypertension, and are under clinical investigation for several other diseases, such as cancer, vasospasm or fibrogenic diseases. We review the molecules that have been evaluated in the main clinical trials, from the point of view of receptor selectivity and of their chemical characteristics which were important for efficacy in pulmonary hypertension. We will also discuss future use of antagonists to endothelin receptor(s) in several human diseases and what should be the necessary properties of the future molecules for efficacy in diseases where the presently tested molecules displayed suboptimal efficacy.

Modeling the impact of lesions in the human brain.

Lesions of anatomical brain networks result in functional disturbances of brain systems and behavior which depend sensitively, often unpredictably, on the lesion site. The availability of whole-brain maps of structural connections within the human cerebrum and our increased understanding of the physiology and large-scale dynamics of cortical networks allow us to investigate the functional consequences of focal brain lesions in a computational model. We simulate the dynamic effects of lesions placed in different regions of the cerebral cortex by recording changes in the pattern of endogenous ("resting-state") neural activity. We find that lesions produce specific patterns of altered functional connectivity among distant regions of cortex, often affecting both cortical hemispheres. The magnitude of these dynamic effects depends on the lesion location and is partly predicted by structural network properties of the lesion site. In the model, lesions along the cortical midline and in the vicinity of the temporo-parietal junction result in large and widely distributed changes in functional connectivity, while lesions of primary sensory or motor regions remain more localized. The model suggests that dynamic lesion effects can be predicted on the basis of specific network measures of structural brain networks and that these effects may be related to known behavioral and cognitive consequences of brain lesions.

Reactivation of protein aggregates by mortalin and Tid1-the human mitochondrial Hsp70 chaperone system.

The mitochondrial 70-kDa heat shock protein (mtHsp70), also known in humans as mortalin, is a central component of the mitochondrial protein import motor and plays a key role in the folding of matrix-localized mitochondrial proteins. MtHsp70 is assisted by a member of the 40-kDa heat shock protein co-chaperone family named Tid1 and a nucleotide exchange factor. Whereas, yeast mtHsp70 has been extensively studied in the context of protein import in the mitochondria, and the bacterial 70-kDa heat shock protein was recently shown to act as an ATP-fuelled unfolding enzyme capable of detoxifying stably misfolded polypeptides into harmless natively refolded proteins, little is known about the molecular functions of the human mortalin in protein homeostasis. Here, we developed novel and efficient purification protocols for mortalin and the two spliced versions of Tid1, Tid1-S, and Tid1-L and showed that mortalin can mediate the in vitro ATP-dependent reactivation of stable-preformed heat-denatured model aggregates, with the assistance of Mge1 and either Tid1-L or Tid1-S co-chaperones or yeast Mdj1. Thus, in addition of being a central component of the protein import machinery, human mortalin together with Tid1, may serve as a protein disaggregating machine which, for lack of Hsp100/ClpB disaggregating co-chaperones, may carry alone the scavenging of toxic protein aggregates in stressed, diseased, or aging human mitochondria.

Postmortem whole-body CT angiography: evaluation of two contrast media solutions.

OBJECTIVE: The objective of our study was to establish a standardized procedure for postmortem whole-body CT-based angiography with lipophilic and hydrophilic contrast media solutions and to compare the results of these two methods. MATERIALS AND METHODS: Minimally invasive postmortem CT angiography was performed on 10 human cadavers via access to the femoral blood vessels. Separate perfusion of the arterial and venous systems was established with a modified heart-lung machine using a mixture of an oily contrast medium and paraffin (five cases) and a mixture of a water-soluble contrast medium with polyethylene glycol (PEG) 200 in the other five cases. Imaging was executed with an MDCT scanner. RESULTS: The minimally invasive femoral approach to the vascular system provided a good depiction of lesions of the complete vascular system down to the level of the small supplying vessels. Because of the enhancement of well-vascularized tissues, angiography with the PEG-mixed contrast medium allowed the detection of tissue lesions and the depiction of vascular abnormalities such as pulmonary embolisms or ruptures of the vessel wall. CONCLUSION: The angiographic method with a water-soluble contrast medium and PEG as a contrast-agent dissolver showed a clearly superior quality due to the lack of extravasation through the gastrointestinal vascular bed and the enhancement of soft tissues (cerebral cortex, myocardium, and parenchymal abdominal organs). The diagnostic possibilities of these findings in cases of antemortem ischemia of these tissues are not yet fully understood.

Method to assess and optimise dependability of complex macro-systems: application to a railway signalling system

Achieving a high degree of dependability in complex macro-systems is challenging. Because of the large number of components and numerous independent teams involved, an overview of the global system performance is usually lacking to support both design and operation adequately. A functional failure mode, effects and criticality analysis (FMECA) approach is proposed to address the dependability optimisation of large and complex systems. The basic inductive model FMECA has been enriched to include considerations such as operational procedures, alarm systems. environmental and human factors, as well as operation in degraded mode. Its implementation on a commercial software tool allows an active linking between the functional layers of the system and facilitates data processing and retrieval, which enables to contribute actively to the system optimisation. The proposed methodology has been applied to optimise dependability in a railway signalling system. Signalling systems are typical example of large complex systems made of multiple hierarchical layers. The proposed approach appears appropriate to assess the global risk- and availability-level of the system as well as to identify its vulnerabilities. This enriched-FMECA approach enables to overcome some of the limitations and pitfalls previously reported with classical FMECA approaches.

Use of human MAR elements to improve retroviral vector production.

Retroviral vectors have many favorable properties for gene therapies, but their use remains limited by safety concerns and/or by relatively lower titers for some of the safer self-inactivating (SIN) derivatives. In this study, we evaluated whether increased production of SIN retroviral vectors can be achieved from the use of matrix attachment region (MAR) epigenetic regulators. Two MAR elements of human origin were found to increase and to stabilize the expression of the green fluorescent protein transgene in stably transfected HEK-293 packaging cells. Introduction of one of these MAR elements in retroviral vector-producing plasmids yielded higher expression of the viral vector RNA. Consistently, viral titers obtained from transient transfection of MAR-containing plasmids were increased up to sixfold as compared with the parental construct, when evaluated in different packaging cell systems and transfection conditions. Thus, use of MAR elements opens new perspectives for the efficient generation of gene therapy vectors.

Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

Effects of different management systems on porosity of oxisols in Paraná, Brazil

Soils play a fundamental role in the production of human foods. The Oxisols in the state of Paraná are among the richest and most productive soils in Brazil, but degradation and low porosity are frequently documented, due to intensive farming involving various management strategies and the application of high-tech solutions. This study aims to investigate changes in the porosity of two Red Oxisols (Latossolos Vermelhos), denoted LVef (eutroferric) and LVdf (dystroferric) under conventional and no-tillage soil management, with a succession of annual crops of soybean, maize and wheat over a continuous period of more than 20 years. After describing the soil profiles under native forest, no-tillage management and conventional tillage using the crop profile method, deformed and non-deformed soil samples were collected from the volumes most compacted by human intervention and the physical, chemical and mineralogical properties analyzed. The various porosity classes (total pore volume, inter-aggregate porosity between channels and biological cavities) and intra-aggregate porosity (determined in 10 cm³ saturated clods subjected to a pressure of -10 kPa to obtain a pore volume with a radius (r eq), > 15 μm and < 15 μm). The results showed that the effects of no-tillage farming on porosity are more pronounced in both soil types. Porosity of the LVdf was higher than pf the LVef soil, whatever the management type. In the LVdf soil, only pores with a radius of > 15 μm were affected by farming whereas in the LVef soil, pores with a radius of < 15 μm were affected as well.

Self-similar community structure in a network of human interactions

We propose a procedure for analyzing and characterizing complex networks. We apply this to the social network as constructed from email communications within a medium sized university with about 1700 employees. Email networks provide an accurate and nonintrusive description of the flow of information within human organizations. Our results reveal the self-organization of the network into a state where the distribution of community sizes is self-similar. This suggests that a universal mechanism, responsible for emergence of scaling in other self-organized complex systems, as, for instance, river networks, could also be the underlying driving force in the formation and evolution of social networks.

Two-step chromatographic purification of human plasma alpha(1)-acid glycoprotein: its application to the purification of rare phenotype samples of the protein and their study by chromatography on immobilized metal chelate affinity adsorbent.

Alpha1-Acid glycoprotein (AAG) or orosomucoid was purified to homogeneity from human plasma by a separate two-step method using chromatography on immobilized Cibacron Blue F3G-A to cross-linked agarose and chromatography on hydroxyapatite. The conditions for the pre-purification of AAG by chromatography on immobilized Cibacron Blue F3G-A were first optimized using different buffer systems with different pH values. The overall yield of the combined techniques was 80% and ca. 12 mg of AAG were purified from an initial total amount of ca. 15 mg in a ca. 40 ml sample of human plasma. This method was applied to the purification of AAG samples corresponding to the three main phenotypes of the protein (FI*S/A, F1/A and S/A), from individual human plasma previously phenotyped for AAG. A study by isoelectric focusing with carrier ampholytes showed that the microheterogeneity of the purified F1*S/A, F1/A and S/A AAG samples was similar to that of AAG in the corresponding plasma, thus suggesting that no apparent desialylation of the glycoprotein occurred during the purification steps. This method was also applied to the purification of AAG samples corresponding to rare phenotypes of the protein (F1/A*AD, S/A*X0 and F1/A*C1) and the interactions of these variants with immobilized copper(II) ions were then studied at pH 7, by chromatography on an iminodiacetate Sepharose-Cu(II) gel. It was found that the different variants encoded by the first of the two genes coding for AAG in humans (i.e. the F1 and S variants) interacted non-specifically with the immobilized ligand, whereas those encoded by the second gene of AAG (i.e. the A, AD, X0 and C1 variants) strongly bound to immobilized Cu(II) ions. These results suggested that chromatography on an immobilized affinity Cu(II) adsorbent could be helpful to distinguish between the respective products of the two highly polymorphic genes which code for human AAG.

Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models

Evaluating other individuals with respect to personality characteristics plays a crucial role in human relations and it is the focus of attention for research in diverse fields such as psychology and interactive computer systems. In psychology, face perception has been recognized as a key component of this evaluation system. Multiple studies suggest that observers use face information to infer personality characteristics. Interactive computer systems are trying to take advantage of these findings and apply them to increase the natural aspect of interaction and to improve the performance of interactive computer systems. Here, we experimentally test whether the automatic prediction of facial trait judgments (e.g. dominance) can be made by using the full appearance information of the face and whether a reduced representation of its structure is sufficient. We evaluate two separate approaches: a holistic representation model using the facial appearance information and a structural model constructed from the relations among facial salient points. State of the art machine learning methods are applied to a) derive a facial trait judgment model from training data and b) predict a facial trait value for any face. Furthermore, we address the issue of whether there are specific structural relations among facial points that predict perception of facial traits. Experimental results over a set of labeled data (9 different trait evaluations) and classification rules (4 rules) suggest that a) prediction of perception of facial traits is learnable by both holistic and structural approaches; b) the most reliable prediction of facial trait judgments is obtained by certain type of holistic descriptions of the face appearance; and c) for some traits such as attractiveness and extroversion, there are relationships between specific structural features and social perceptions.

Artificial muscle: the human chimera is the future.

Severe heart failure and cerebral stroke are broadly associated with the impairment of muscular function that conventional treatments struggle to restore. New technologies enable the construction of "smart" materials that could be of great help in treating diseases where the main problem is muscle weakness. These materials "behave" similarly to biological systems, because the material directly converts energy, for example electrical energy into movement. The extension and contraction occur silently like in natural muscles. The real challenge is to transfer this amazing technology into devices that restore or replace the mechanical function of failing muscle. Cardiac assist devices based on artificial muscle technology could envelope a weak heart and temporarily improve its systolic function, or, if placed on top of the atrium, restore the atrial kick in chronic atrial fibrillation. Artificial sphincters could be used to treat urinary incontinence after prostatectomy or faecal incontinence associated with stomas. Artificial muscles can restore the ability of patients with facial paralysis due to stroke or nerve injury to blink. Smart materials could be used to construct an artificial oesophagus including peristaltic movement and lower oesophageal sphincter function to replace the diseased oesophagus thereby avoiding the need for laparotomy to mobilise stomach or intestine. In conclusion, in the near future, smart devices will integrate with the human body to fill functional gaps due to organ failure, and so create a human chimera.

Delinquency-Related Systems in Iowa: Substance Abuse, Mental Health, Education and Job Traning, April 2006

This report was developed to provide summary information to allow state agency staff, practitioners and juvenile justice system officials to access specific sections of Iowa’s Three Year Plan. It includes the “Service Network” section of Iowa’s 2006 Juvenile Justice and Delinquency Prevention Act formula grant Three-Year Plan. The complete Three Year Plan serves as Iowa’s application for Juvenile Justice and Delinquency Prevention Act formula grant funding. The information included in this report overviews some of the systems and services that relate to Iowa’s delinquency and CINA systems. The systems and services discussed include substance abuse , mental health, alternative or special education, and job training.

Integrative molecular bioinformatics study of human adrenocortical tumors : microRNA, tissue-specific target prediction, and pathway analysis.

MicroRNAs (miRs) are involved in the pathogenesis of several neoplasms; however, there are no data on their expression patterns and possible roles in adrenocortical tumors. Our objective was to study adrenocortical tumors by an integrative bioinformatics analysis involving miR and transcriptomics profiling, pathway analysis, and a novel, tissue-specific miR target prediction approach. Thirty-six tissue samples including normal adrenocortical tissues, benign adenomas, and adrenocortical carcinomas (ACC) were studied by simultaneous miR and mRNA profiling. A novel data-processing software was used to identify all predicted miR-mRNA interactions retrieved from PicTar, TargetScan, and miRBase. Tissue-specific target prediction was achieved by filtering out mRNAs with undetectable expression and searching for mRNA targets with inverse expression alterations as their regulatory miRs. Target sets and significant microarray data were subjected to Ingenuity Pathway Analysis. Six miRs with significantly different expression were found. miR-184 and miR-503 showed significantly higher, whereas miR-511 and miR-214 showed significantly lower expression in ACCs than in other groups. Expression of miR-210 was significantly lower in cortisol-secreting adenomas than in ACCs. By calculating the difference between dCT(miR-511) and dCT(miR-503) (delta cycle threshold), ACCs could be distinguished from benign adenomas with high sensitivity and specificity. Pathway analysis revealed the possible involvement of G2/M checkpoint damage in ACC pathogenesis. To our knowledge, this is the first report describing miR expression patterns and pathway analysis in sporadic adrenocortical tumors. miR biomarkers may be helpful for the diagnosis of adrenocortical malignancy. This tissue-specific target prediction approach may be used in other tumors too.

Long-Range Goals for Iowa's Criminal & Juvenile Justice Systems, February 2005

Iowa Code Section 216A.135 requires the Criminal and Juvenile Justice Planning Advisory Council (CJJPAC) to submit a long-range plan for Iowa's justice system to the Governor and General Assembly every five years. The Criminal and Juvenile Justice Advisory Council directed that the 2005 plan be developed with input from the public. A public hearing was held in September 2004, utilizing the Iowa Communications Network at 5 sites across Iowa. Using the information gained, the Council developed new goals and strategies and modified others from the 2000 plan. The 2005 Long Range Goals for Iowa’s Criminal and Juvenile Justice Systems, organized as follows, are meant to facilitate analyses and directions for justice system issues and concerns in Iowa.