Getting hotter by heating less: How driven granular materials dissipate energy in excess

We investigate how the kinetic energy acquired by a dense granular system driven by an external vibration depends on the input energy. Our focus is on the dependence of the granular behavior on two main parameters: frequency and vibration amplitude. We find that there exists an optimal forcing frequency at which the system reaches the maximal kinetic energy: if the input energy is increased beyond this threshold, the system dissipates more and more energy and recovers a colder and more viscous state. Quite surprisingly, the nonmonotonic behavior is found for vibration amplitudes which are sufficiently low to keep the system always in contact with the driving oscillating plate. Studying dissipative properties of the system, we unveil a striking difference between this nonmonotonic behavior and a standard resonance mechanism. This feature is also observed at the microscopic scale of the single-grain dynamics and can be interpreted as an instance of negative specific heat. An analytically solvable model based on a generalized forced-damped oscillator well reproduces the observed phenomenology, illustrating the role of the competing effects of forcing and dissipation

Getting hotter by heating less: How driven granular materials dissipate energy in excess

We investigate how the kinetic energy acquired by a dense granular system driven by an external vibration depends on the input energy. Our focus is on the dependence of the granular behavior on two main parameters: frequency and vibration amplitude. We find that there exists an optimal forcing frequency at which the system reaches the maximal kinetic energy: if the input energy is increased beyond this threshold, the system dissipates more and more energy and recovers a colder and more viscous state. Quite surprisingly, the nonmonotonic behavior is found for vibration amplitudes which are sufficiently low to keep the system always in contact with the driving oscillating plate. Studying dissipative properties of the system, we unveil a striking difference between this nonmonotonic behavior and a standard resonance mechanism. This feature is also observed at the microscopic scale of the single-grain dynamics and can be interpreted as an instance of negative specific heat. An analytically solvable model based on a generalized forced-damped oscillator well reproduces the observed phenomenology, illustrating the role of the competing effects of forcing and dissipation

Family Resemblances? Ligand Binding and Activation of Family A and B G-Protein-Coupled Receptors Structural characterization of the parathyroid hormone receptor domains determinant for ligand binding

Abstract Over the years, the association of peptide ligands to Family B GPCRs (G-protein coupled receptors) has been characterized by a number of experimental and theoretical techniques. For the PTH (parathyroid hormone) ligand-receptor system, important insight has been provided by photoaffinity labelling experiments and the elucidation of direct contact points between ligand and receptor. Our research has focused on the structural elucidation of the receptor domains shown to be involved in the binding of PTH. Employing a combination of carefully designed receptor domains, solution-state NMR carried out in the presence of membrane mimetics and extensive computer simulations, we have obtained a well-resolved model of the ligandreceptor complex for PTH. Here, we review the development of this model and highlight some inherent limitations of the methods employed and their consequences on interpretation of the ligand-receptor model

miRNA-126 Orchestrates an Oncogenic Program in B Cell Precursor Acute Lymphoblastic Leukemia

MicroRNA (miRNA)-126 is a known regulator of hematopoietic stem cell quiescence. We engineered murine hematopoiesis to express miRNA-126 across all differentiation stages. Thirty percent of mice developed monoclonal B cell leukemia, which was prevented or regressed when a tetracycline-repressible miRNA-126 cassette was switched off. Regression was accompanied by upregulation of cell-cycle regulators and B cell differentiation genes, and downregulation of oncogenic signaling pathways. Expression of dominant-negative p53 delayed blast clearance upon miRNA-126 switch-off, highlighting the relevance of p53 inhibition in miRNA-126 addiction. Forced miRNA-126 expression in mouse and human progenitors reduced p53 transcriptional activity through regulation of multiple p53-related targets. miRNA-126 is highly expressed in a subset of human B-ALL, and antagonizing miRNA-126 in ALL xenograft models triggered apoptosis and reduced disease burden

Meta-analysis of transcriptome data identifies a novel 5-gene pancreatic adenocarcinoma classifier

Purpose: Pancreatic ductal adenocarcinoma (PDAC) is largely incurable due to late diagnosis. Superior early detection biomarkers are critical to improving PDAC survival and risk stratification. Experimental Design: Optimized meta-analysis of PDAC transcriptome datasets identified and validated key PDAC biomarkers. PDAC-specific expression of a 5-gene biomarker panel was measured by qRT-PCR in microdissected patient-derived FFPE tissues. Cell-based assays assessed impact of two of these biomarkers, TMPRSS4 and ECT2, on PDAC cells. Results: A 5-gene PDAC classifier (TMPRSS4, AHNAK2, POSTN, ECT2, SERPINB5) achieved on average 95% sensitivity and 89% specificity in discriminating PDAC from non-tumor samples in four training sets and similar performance (sensitivity = 94%, specificity = 89.6%) in five independent validation datasets. This classifier accurately discriminated PDAC from chronic pancreatitis (AUC = 0.83), other cancers (AUC = 0.89), and non-tumor from PDAC precursors (AUC = 0.92) in three independent datasets. Importantly, the classifier distinguished PanIN from healthy pancreas in the PDX1-Cre;LSL-KrasG12D PDAC mouse model. Discriminatory expression of the PDAC classifier genes was confirmed in microdissected FFPE samples of PDAC and matched surrounding non-tumor pancreas or pancreatitis. Notably, knock-down of TMPRSS4 and ECT2 reduced PDAC soft agar growth and cell viability and TMPRSS4 knockdown also blocked PDAC migration and invasion. Conclusions: This study identified and validated a highly accurate 5-gene PDAC classifier for discriminating PDAC and early precursor lesions from non-malignant tissue that may facilitate early diagnosis and risk stratification upon validation in prospective clinical trials. Cell-based experiments of two overexpressed proteins encoded by the panel, TMPRSS4 and ECT2, suggest a causal link to PDAC development and progression, confirming them as potential therapeutic targets

Translating and validating a Training Needs Assessment tool into Greek

<p>Abstract</p> <p>Background</p> <p>The translation and cultural adaptation of widely accepted, psychometrically tested tools is regarded as an essential component of effective human resource management in the primary care arena. The Training Needs Assessment (TNA) is a widely used, valid instrument, designed to measure professional development needs of health care professionals, especially in primary health care. This study aims to describe the translation, adaptation and validation of the TNA questionnaire into Greek language and discuss possibilities of its use in primary care settings.</p> <p>Methods</p> <p>A modified version of the English self-administered questionnaire consisting of 30 items was used. Internationally recommended methodology, mandating forward translation, backward translation, reconciliation and pretesting steps, was followed. Tool validation included assessing item internal consistency, using the alpha coefficient of Cronbach. Reproducibility (test – retest reliability) was measured by the kappa correlation coefficient. Criterion validity was calculated for selected parts of the questionnaire by correlating respondents' research experience with relevant research item scores. An exploratory factor analysis highlighted how the items group together, using a Varimax (oblique) rotation and subsequent Cronbach's alpha assessment.</p> <p>Results</p> <p>The psychometric properties of the Greek version of the TNA questionnaire for nursing staff employed in primary care were good. Internal consistency of the instrument was very good, Cronbach's alpha was found to be 0.985 (p < 0.001) and Kappa coefficient for reproducibility was found to be 0.928 (p < 0.0001). Significant positive correlations were found between respondents' current performance levels on each of the research items and amount of research involvement, indicating good criterion validity in the areas tested. Factor analysis revealed seven factors with eigenvalues of > 1.0, KMO (Kaiser-Meyer-Olkin) measure of sampling adequacy = 0.680 and Bartlett's test of sphericity, p < 0.001.</p> <p>Conclusion</p> <p>The translated and adapted Greek version is comparable with the original English instrument in terms of validity and reliability and it is suitable to assess professional development needs of nursing staff in Greek primary care settings.</p

Lentiviral Hematopoietic Stem Cell Gene Therapy Benefits Metachromatic Leukodystrophy

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Meta-analysis of transcriptome data identifies a novel 5-gene pancreatic adenocarcinoma classifier

Purpose Pancreatic ductal adenocarcinoma (PDAC) is largely incurable due to late diagnosis. Superior early detection biomarkers are critical to improving PDAC survival and risk stratification. Experimental Design Optimized meta-analysis of PDAC transcriptome datasets identified and validated key PDAC biomarkers. PDAC-specific expression of a 5-gene biomarker panel was measured by qRT-PCR in microdissected patient-derived FFPE tissues. Cell-based assays assessed impact of two of these biomarkers, TMPRSS4 and ECT2, on PDAC cells. Results: A 5-gene PDAC classifier (TMPRSS4, AHNAK2, POSTN, ECT2, SERPINB5) achieved on average 95% sensitivity and 89% specificity in discriminating PDAC from non-tumor samples in four training sets and similar performance (sensitivity = 94%, specificity = 89.6%) in five independent validation datasets. This classifier accurately discriminated PDAC from chronic pancreatitis (AUC = 0.83), other cancers (AUC = 0.89), and non-tumor from PDAC precursors (AUC = 0.92) in three independent datasets. Importantly, the classifier distinguished PanIN from healthy pancreas in the PDX1-Cre;LSL-KrasG12D PDAC mouse model. Discriminatory expression of the PDAC classifier genes was confirmed in microdissected FFPE samples of PDAC and matched surrounding non-tumor pancreas or pancreatitis. Notably, knock-down of TMPRSS4 and ECT2 reduced PDAC soft agar growth and cell viability and TMPRSS4 knockdown also blocked PDAC migration and invasion. Conclusions: This study identified and validated a highly accurate 5-gene PDAC classifier for discriminating PDAC and early precursor lesions from non-malignant tissue that may facilitate early diagnosis and risk stratification upon validation in prospective clinical trials. Cell-based experiments of two overexpressed proteins encoded by the panel, TMPRSS4 and ECT2, suggest a causal link to PDAC development and progression, confirming them as potential therapeutic targets