Natural Language Processing using Deep Learning for Classifying Water Infrastructure Procurement Records and Calculating Unit Costs

This thesis introduces a novel ontology-based deep learning classification model specifically tailored for civil engineering applications, focusing on automating the extraction and classification of water infrastructure capital works tenders and progress certificates. Utilizing ontology for standardizing tender-bid data and employing Named Entity Recognition (NERC) for item categorization, the model adeptly addresses the challenges posed by the diversity in document styles and formats. Incorporating Long Short-Term Memory (LSTM) structures within the model enables the learning of both linear and non-linear dependencies between words. This aspect is particularly significant in tackling the unique language constructs present in tender-bid document records. The model's effectiveness is underscored by its impressive classification accuracy, achieving 92.6% for testing data and 98.7% for training data, thereby marking a significant advancement in the field. The practical application of this model through a web server highlights its adaptability and efficiency in real-world scenarios. The model's role in tasks such as unit cost calculation establishes a new benchmark in the industry, showcasing the thesis's innovative contributions in areas like ontology-based data structuring and LSTM-driven automated unit cost computation. Looking beyond its current scope, this research holds potential for broader applications and adaptations in different civil engineering domains. It lays the groundwork for future enhancements, including exploring multilingual extensions and specialized approaches aligned with evolving industry trends. This thesis amalgamates data preprocessing, deep learning, and engineering expertise to boost efficiency and accuracy significantly. The unique methodology fosters continuous improvement and broad applicability across different regions. The practical integration of this technology in civil engineering tasks, demonstrated through the web server, opens avenues for further development to encompass a wider array of applications. Future research directions include refining the framework to cater to the dynamic needs of various civil engineering domains and extending the web server's capabilities for real-time data processing and analysis. Investigating the applicability of this methodology in other engineering or interdisciplinary contexts could also provide valuable insights, extending the utility of this research. This thesis lays a solid foundation for ongoing enhancements in capital work planning and tender contract assessment within the civil engineering industry

Conditioned medium from amniotic fluid mesenchymal stem cells could modulate Alzheimer's disease-like changes in human neuroblastoma cell line SY-SY5Y in a paracrine manner

Background: Alzheimer's disease is usually diagnosed by significant extracellular deposition of beta-amyloid and intracellular neurofibrillary tangle formation. Here, we investigated the paracrine effect of amniotic fluid-derived mesenchymal stem cells on AD changes in human SH-SY5Y cells. Methods: SH-SY5Y cells were divided into five groups: Control, 0.1 mu g/ml LPS, 10 mu g/ml LPS, 0.1 mu g/ml LPS + conditioned medium, and 10 mu g/ml LPS + conditioned medium. Cells were incubated with 0.1% and 10 mu g/ml LPS for 48 h, followed by incubation with the conditioned medium of amniotic fluid-derived mesenchymal stem cells for the next 24 h. Beta-amyloid plaques were monitored by Congo-red staining. Survival and apoptosis were assessed by the MTT assay and flow cytometric analysis of Annexin-V. ELISA was used to measure the levels of neprilysin, angiotensin-converting enzyme, and Matrix Metallopmteinase-9. A PCR array was used to measure the expression of genes involved in neurogenesis. Results: Bright-field imaging showed beta-amyloid plaques in the group treated with 10 mu g/ml LPS. We found minimal effects in groups receiving 0.1 mu g/ml LPS. The data showed that the reduction in the levels of neprilysin, angiotensin-converting enzyme, and Matrix Metalloproteinase-9 in the LPS-treated cells was attenuated after incubation with the stem cell secretome (p < 0.05). Amniotic fluid stem cell secretome increased the viability of LPS-treated SH-SY5Y cells (p 0.05) and was associated with a decrease in apoptotic changes (p < 0.05). We found the modulation of several genes involved in neurogenesis in the 10 mu g/ml LPS + conditioned medium group compared to cells treated with 10 mu g/ml LPS alone. Conclusion: Amniotic fluid stem cell secretion reduces AD-like pathologies in the human neuronal lineage.Tabriz University of Medical Sciences [IR.TBZMED.REC.1396.639]This study was supported by a grant from Tabriz University of Medical Sciences (IR.TBZMED.REC.1396.639) Grant holder: Dr. Alireza Nourazarian

Calcium-Dependent Hyperexcitability in Human Stem Cell–Derived Rett Syndrome Neuronal Networks

Background: Mutations in MECP2 predominantly cause Rett syndrome and can be modeled in vitro using human stem cell–derived neurons. Patients with Rett syndrome have signs of cortical hyperexcitability, such as seizures. Human stem cell–derived MECP2 null excitatory neurons have smaller soma size and reduced synaptic connectivity but are also hyperexcitable due to higher input resistance. Paradoxically, networks of MECP2 null neurons show a decrease in the frequency of network bursts consistent with a hypoconnectivity phenotype. Here, we examine this issue. Methods: We reanalyzed multielectrode array data from 3 isogenic MECP2 cell line pairs recorded over 6 weeks (n = 144). We used a custom burst detection algorithm to analyze network events and isolated a phenomenon that we termed reverberating super bursts (RSBs). To probe potential mechanisms of RSBs, we conducted pharmacological manipulations using bicuculline, EGTA-AM, and DMSO on 1 cell line (n = 34). Results: RSBs, often misidentified as single long-duration bursts, consisted of a large-amplitude initial burst followed by several high-frequency, low-amplitude minibursts. Our analysis revealed that MECP2 null networks exhibited increased frequency of RSBs, which produced increased bursts compared with isogenic controls. Bicuculline or DMSO treatment did not affect RSBs. EGTA-AM selectively eliminated RSBs and rescued network burst dynamics. Conclusions: During early development, MECP2 null neurons are hyperexcitable and produce hyperexcitable networks. This may predispose them to the emergence of hypersynchronic states that potentially translate into seizures. Network hyperexcitability depends on asynchronous neurotransmitter release that is likely driven by presynaptic Ca2+ and can be rescued by EGTA-AM to restore typical network dynamics