A Low-spin Mn(III) Complex

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Altered expression of heat shock protein-27 and monocyte chemoattractant protein-1 after acute spinal cord injury: a pilot study

Published online: 2019-10-07Background: Spinal cord injury (SCI) leads to serious complications involving primary trauma and progressive loss due to inflammation, local ischemia, or infection. Despite a worldwide annual incidence of 15 to 40 cases per million, methylprednisolone is the only treatment available to alleviate neurologic dysfunction; therefore, research is currently focused on identifying novel targets by biochemical and molecular studies. Purpose: Here, we investigated the expression of various molecular markers at the messenger ribonucleic acid (mRNA) and protein level at day 0 and day 30 post-SCI. Methods: Enzyme-linked immunosorbent assay (ELISA) was performed to determine the expression of CASPASE-3 and heat shock protein-27 (HSP-27) in serum samples. Real-time polymerase chain reaction (RT-PCR) was performed to determine the level of mRNA expression of vascular endothelial growth factor receptor-1 (VEGFR-1), VEGFR-2, HSP-27, monocyte chemoattractant protein-1 (MCP-1), and CASPASE-3. Results: HSP-27 expression at day 30, as compared with day 0, showed significant downregulation. In contrast, there was elevated expression of MCP-1. ELISA analysis showed no significant change in the expression of CASPASE-3 or HSP-27. Conclusion: There may be possible opposing role of HSP-27 and MCP-1 governing SCI. Their association can be studied by designing in vitro studies.Vidyasagar Boraiah, Shweta Modgil, Kaushal Sharma, Vivek Podder, Madhava Sai Sivapuram, Gurwattan S. Miranpuri, Akshay Anand, Vijay Gon

On the quest for selective constraints shaping the expressivity of the genes casting retropseudogenes in human

<p>Abstract</p> <p>Background</p> <p>Pseudogenes, the nonfunctional homologues of functional genes are now coming to light as important resources regarding the study of human protein evolution. Processed pseudogenes arising by reverse transcription and reinsertion can provide molecular record on the dynamics and evolution of genomes. Researches on the progenitors of human processed pseudogenes delved out their highly expressed and evolutionarily conserved characters. They are reported to be short and GC-poor indicating their high efficiency for retrotransposition. In this article we focused on their high expressivity and explored the factors contributing for that and their relevance in the milieu of protein sequence evolution.</p> <p>Results</p> <p>We here, analyzed the high expressivity of these genes configuring processed or retropseudogenes by their immense connectivity in protein-protein interaction network, an inclination towards alternative splicing mechanism, a lower rate of mRNA disintegration and a slower evolutionary rate. While the unusual trend of the upraised disorder in contrast with the high expressivity of the proteins encoded by processed pseudogene ancestors is accredited by a predominance of hub-protein encoding genes, a high propensity of repeat sequence containing genes, elevated protein stability and the functional constraint to perform the transcription regulatory jobs. Linear regression analysis demonstrates mRNA decay rate and protein intrinsic disorder as the influential factors controlling the expressivity of these retropseudogene ancestors while the latter one is found to have the most significant regulatory power.</p> <p>Conclusions</p> <p>Our findings imply that, the affluence of disordered regions elevating the network attachment to be involved in important cellular assignments and the stability in transcriptional level are acting as the prevailing forces behind the high expressivity of the human genes configuring processed pseudogenes.</p

Design and analysis of vibration energy harvesters based on peak response statistics

Energy harvesting using cantilever piezoelectric vibration energy harvesters excited by Gaussian broadband random base excitation is considered. The optimal design and analysis of energy harvesters under random excitation is normally performed using the mean and standard deviation of a response quantity of interest, such as the voltage. An alternative approach based on the statistics of the peak voltage is developed in this paper. Three extreme response characteristics, namely (a) level crossing, (b) response peaks above certain level, and (c) fractional time spend above a certain level, have been employed. Two cases, namely the harvesting circuit with and without an inductor, have been considered. Exact closed-form expressions have been derived for number of level crossings, statistics of response peaks and fractional time spend above a certain level for the output voltage. It is shown that these quantities can be related to the standard deviation of the voltage and its derivative with respect to time. Direct numerical simulation has been used to validate the analytical expressions. Based on the analytical results, closed-form expressions for optimal system parameters have been proposed. Numerical examples are given to illustrate the applicability of the analytical results

A Deep Learning Framework for the Detection of Abnormality in Cerebral Blood Flow Velocity Using Transcranial Doppler Ultrasound

Transcranial doppler (TCD) ultrasound is a non-invasive imaging technique that can be used for continuous monitoring of blood flow in the brain through the major cerebral arteries by calculating the cerebral blood flow velocity (CBFV). Since the brain requires a consistent supply of blood to function properly and meet its metabolic demand, a change in CBVF can be an indication of neurological diseases. Depending on the severity of the disease, the symptoms may appear immediately or may appear weeks later. For the early detection of neurological diseases, a classification model is proposed in this study, with the ability to distinguish healthy subjects from critically ill subjects. The TCD ultrasound database used in this study contains signals from the middle cerebral artery (MCA) of 6 healthy subjects and 12 subjects with known neurocritical diseases. The classification model works based on the maximal blood flow velocity waveforms extracted from the TCD ultrasound. Since the signal quality of the recorded TCD ultrasound is highly dependent on the operator's skillset, a noisy and corrupted signal can exist and can add biases to the classifier. Therefore, a deep learning classifier, trained on a curated and clean biomedical signal can reliably detect neurological diseases. For signal classification, this study proposes a Self-organized Operational Neural Network (Self-ONN)-based deep learning model Self-ResAttentioNet18, which achieves classification accuracy of 96.05% with precision, recall, f1 score, and specificity of 96.06%, 96.05%, 96.06%, and 96.09%, respectively. With an area under the ROC curve of 0.99, the model proves its feasibility to confidently classify middle cerebral artery (MCA) waveforms in near real-time.This work was made possible by the High Impact grant of Qatar University # QUHI-CENG-22_23-548 and student grant: QUST-1-CENG-2023-796. The statements made herein are solely the responsibility of the authors.Scopu

On the Degradation of Retained Austenite in Transformation Induced Plasticity Steel

© 2020, The Minerals, Metals & Materials Society and ASM International. A transformation-induced plasticity steel was thermomechanically processed and then transformed to bainite at an isothermal transformation temperature of 723 K for 1800 seconds, which exceeds the time required for completion of the bainite transformation. The formation of lenticular-shaped carbides with a triclinic lattice and internal substructure was found after thermomechanical processing. After 16 years of storage at room temperature, the decomposition of retained austenite into pearlite was observed for the first time at this temperature

Extreme Evolutionary Disparities Seen in Positive Selection across Seven Complex Diseases

Positive selection is known to occur when the environment that an organism inhabits is suddenly altered, as is the case across recent human history. Genome-wide association studies (GWASs) have successfully illuminated disease-associated variation. However, whether human evolution is heading towards or away from disease susceptibility in general remains an open question. The genetic-basis of common complex disease may partially be caused by positive selection events, which simultaneously increased fitness and susceptibility to disease. We analyze seven diseases studied by the Wellcome Trust Case Control Consortium to compare evidence for selection at every locus associated with disease. We take a large set of the most strongly associated SNPs in each GWA study in order to capture more hidden associations at the cost of introducing false positives into our analysis. We then search for signs of positive selection in this inclusive set of SNPs. There are striking differences between the seven studied diseases. We find alleles increasing susceptibility to Type 1 Diabetes (T1D), Rheumatoid Arthritis (RA), and Crohn's Disease (CD) underwent recent positive selection. There is more selection in alleles increasing, rather than decreasing, susceptibility to T1D. In the 80 SNPs most associated with T1D (p-value <7.01×10−5) showing strong signs of positive selection, 58 alleles associated with disease susceptibility show signs of positive selection, while only 22 associated with disease protection show signs of positive selection. Alleles increasing susceptibility to RA are under selection as well. In contrast, selection in SNPs associated with CD favors protective alleles. These results inform the current understanding of disease etiology, shed light on potential benefits associated with the genetic-basis of disease, and aid in the efforts to identify causal genetic factors underlying complex disease

Income redistribution in the European Union

We explore the redistributive effects of taxes and benefits in the 27 member states of the European Union (EU) using EUROMOD, the tax-benefit microsimulation model for the EU. As well as describing redistributive effects in aggregate, we assess and compare the effectiveness of eight individual types of policy in reducing income disparities. We derive results for the 27 members of the EU using policies in effect in 2010 and present them for each country separately as well as for the EU as a whole

Prevalence of G2P[4] and G12P[6] Rotavirus, Bangladesh

Rotavirus strains not covered by licensed vaccines are increasing