The emergence of moving learning activities online, due to Covid-19 pandemic preventive measures and fast growing technology, forced many educational institutions to design and prepare their distance learning systems on time, without adopting the best practices for an effective and holistic digital transformation. This fact highlights the need of reconsidering the models in online learning and redesigning the learning processes to meet students e-learning needs and receive a more qualitative educational experience. This study intends to show which are the perspectives of students on the next generation learning platform, post-covid, and how this can contribute to a more effective learning in HE. To serve this purpose, an internet-based survey was designed. The questionnaire was distributed via the DSV iLearn platform of the program to all participants-users of the platform, and the method of data analysis chosen was Quantitative analysis. The results of this study showed an overall satisfaction with the platform capabilities and a satisfactory level of students’ skills in the use of ICT technologies and a digital learning platform. Some important conclusions about dependencies of students’ characteristics that affect their engagement with an elearning platform also arose, while areas of improvement were identified regarding the feeling of safety using a digital platform, alternative assessment techniques, new platform technological features and interaction with peers and instructors. Under this study limitations and the potential of a future and more extensive research, the aim of these results is their incorporation in the design, implementation and optimization processes of the next generation learning platform, contributing to the Higher Education e-learning systems performance and responsiveness flexibility, taking also into consideration qualitative factors related to students’ characteristics and needs.

Malgré l’usage de médicaments antipsychotiques depuis plusieurs décennies, leur mécanisme d’action précis, autre que leur interaction avec les récepteurs dopaminergiques et sérotoninergiques, demeure peu connu. Nur77 (Nr4a1 ou NGFI-B) est un facteur de transcription de la famille des récepteurs nucléaires associé aux effets des antipsychotiques. Ceci étant dit, le mécanisme d’action de Nur77 est également peu connu. Afin de mieux comprendre les éléments impliqués avec les antipsychotiques et l’activité de Nur77, nous avons comparé les niveaux de transcrits dans le striatum suite à un traitement avec l’halopéridol chez des rats sauvages et déficients en Nur77 à l’aide de la technique de séquençage à haut débit (RNAseq) et d’une analyse bio-informatique. L’halopéridol et Nur77 ont modulé d’importants groupes de gènes associés avec la signalisation des récepteurs dopaminergiques et la synapse glutamatergique. L’analyse a révélé des modulations de gènes clés reliés à la signalisation des protéines G. Parmi les transcrits modulés significativement chez les rats traités avec halopéridol et ceux déficients en Nur77, la dual specificity phosphatase 5 (Dusp5) représente un nouveau candidat d’intérêt. En effet, nous avons confirmé que les niveaux d’ARNm et protéiques de Dusp5 dans le striatum sont associés aux mouvements involontaires anormaux (dyskinésie) dans un modèle de primates non-humains traités chroniquement avec halopéridol. Cette analyse transcriptomique a démontré des altérations rapides et importantes d’éléments impliqués dans la signalisation des protéines G par l’halopéridol, et a permis d’identifier, pour la première fois, une expression de Dusp5 dépendante de Nur77 en tant que nouvelle composante reliée avec la dyskinésie tardive. / Despite antipsychotic drugs being used for several decades, their precise mechanism of action remains elusive. Nur77 (Nr4a1 or NGFI-B) is a transcription factor of the nuclear receptor family associated with antipsychotic drug effects. However, the mechanism of action of Nur77 is also not well understood. To better understand the signaling components implicated with antipsychotic drug use and Nur77 activity, we compared striatal gene transcripts following haloperidol in wild-type and Nur77-deficient rats using Next Generation RNA Sequencing (RNAseq) and a bioinformatics analysis. Haloperidol and Nur77 modulated important subsets of striatal genes associated with dopamine receptor signaling and glutamate synapses. The analysis revealed modulations of key components of G protein signaling that are consistent with a rapid adaptation of striatal cells that may partially explain long-term haloperidol-induced dopamine D2 receptor upregulation. Amongst significantly modulated transcripts in rats treated with haloperidol and rats deficient in Nur77, dual specificity phosphatase 5 (Dusp5) represents a new and very interesting candidate. Indeed, we confirmed that striatal Dusp5 mRNA and protein levels were associated with abnormal involuntary movements (dyskinesia) in non-human primates chronically exposed to haloperidol. This transcriptomic analysis showed important haloperidol-induced G protein-coupled receptor signaling alterations that may support a regulatory role of Nur77 in dopamine D2 receptor signaling pathways and identified, for the first time, a putative Nur77-dependent expression of Dusp5 as a new signaling component for antipsychotic drug-induced tardive dyskinesia.

Longitudinal personalized electronic health record (LPHR) provides a holistic view of health records for individuals and offers a consistent patient-controlled information system for managing the health care of patients. Except for the patients in Veterans Affairs health care service, however, no LPHR is available for the general population in the U.S. that can integrate the existing patients' electronic health records throughout life of care. Such a gap may be contributed mainly by the fact that existing patients' electronic health records are scattered across multiple health care facilities and often not shared due to privacy and security concerns from both patients and health care organizations. The main objective of this dissertation is to address these roadblocks by designing a scalable and interoperable LPHR with patient-controlled and mutually-trusted security and privacy. Privacy and security are complex problems. Specifically, without a set of access control policies, encryption alone cannot secure patient data due to insider threat. Moreover, in a distributed system like LPHR, so-called race condition occurs when access control policies are centralized while decisions making processes are localized. We propose a formal definition of secure LPHR and develop a blockchain-enabled next generation access control (BeNGAC) model. The BeNGAC solution focuses on patient-managed secure authorization for access, and NGAC operates in open access surroundings where users can be centrally known or unknown. We also propose permissioned blockchain technology - Hyperledger Fabric (HF) - to ease the shortcoming of race condition in NGAC that in return enhances the weak confidentiality protection in HF. Built upon BeNGAC, we further design a blockchain-enabled secure and trusted (BEST) LPHR prototype in which data are stored in a distributed yet decentralized database. The unique feature of the proposed BEST-LPHR is the use of blockchain smart contracts allowing BeNGAC policies to govern the security, privacy, confidentiality, data integrity, scalability, sharing, and auditability. The interoperability is achieved by using a health care data exchange standard called Fast Health Care Interoperability Resources. We demonstrated the feasibility of the BEST-LPHR design by the use case studies. Specifically, a small-scale BEST-LPHR is built for sharing platform among a patient and health care organizations. In the study setting, patients have been raising additional ethical concerns related to consent and granular control of LPHR. We engineered a Web-delivered BEST-LPHR sharing platform with patient-controlled consent granularity, security, and privacy realized by BeNGAC. Health organizations that holding the patient's electronic health record (EHR) can join the platform with trust based on the validation from the patient. The mutual trust is established through a rigorous validation process by both the patient and built-in HF consensus mechanism. We measured system scalability and showed millisecond-range performance of LPHR permission changes. In this dissertation, we report the BEST-LPHR solution to electronically sharing and managing patients' electronic health records from multiple organizations, focusing on privacy and security concerns. While the proposed BEST-LPHR solution cannot, expectedly, address all problems in LPHR, this prototype aims to increase EHR adoption rate and reduce LPHR implementation roadblocks. In a long run, the BEST-LPHR will contribute to improving health care efficiency and the quality of life for many patients. / Doctor of Philosophy / Longitudinal personalized electronic health record (LPHR) provides a holistic view of health records for individuals and offers a consistent patient-controlled information system for managing the health care of patients. Except for the patients in Veterans Affairs health care service, however, no LPHR is available for the general population in the U.S. that can integrate the existing patients' electronic health records throughout life of care. Such a gap may be contributed mainly by the fact that existing patients' electronic health records are scattered across multiple health care facilities and often not shared due to privacy and security concerns from both patients and health care organizations. The main objective of this dissertation is to address these roadblocks by designing a scalable and interoperable LPHR with patient-controlled and mutually-trusted security and privacy. We propose a formal definition of secure LPHR and develop a novel blockchain-enabled next generation access control (BeNGAC) model, that can protect security and privacy of LPHR. Built upon BeNGAC, we further design a blockchain-enabled secure and trusted (BEST) LPHR prototype in which data are stored in a distributed yet decentralized database. The health records on BEST-LPHR are personalized to the patients with patient-controlled security, privacy, and granular consent. The unique feature of the proposed BEST-LPHR is the use of blockchain technology allowing BeNGAC policies to govern the security, privacy, confidentiality, data integrity, scalability, sharing, and auditability. The interoperability is achieved by using a health care data exchange standard. We demonstrated the feasibility of the BEST-LPHR design by the use case studies. Specifically, a small-scale BEST-LPHR is built for sharing platform among a patient and health care organizations. We engineered a Web-delivered BEST-LPHR sharing platform with patient-controlled consent granularity, security, and privacy realized by BeNGAC. Health organizations that holding the patient's electronic health record (EHR) can join the platform with trust based on the validation from the patient. The mutual trust is established through a rigorous validation process by both the patient and built-in blockchain consensus mechanism. We measured system scalability and showed millisecond-range performance of LPHR permission changes. In this dissertation, we report the BEST-LPHR solution to electronically sharing and managing patients' electronic health records from multiple organizations, focusing on privacy and security concerns. While the proposed BEST-LPHR solution cannot, expectedly, address all problems in LPHR, this prototype aims to increase EHR adoption rate and reduce LPHR implementation roadblocks. In a long run, the BEST-LPHR will contribute to improving health care efficiency and the quality of life for many patients.

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Affinity ligands are important molecules used in affinity chromatography for purification of significant substances from complex mixtures. To find affinity ligands specific to important target molecules could be a challenging process. Cytiva uses the powerful phage display technique to find new promising affinity ligands. The phage display technique is a method run in several enrichment cycles. When developing new affinity ligands, a protein scaffold library with a diversity of up to 1010-1011 different protein scaffold variants is run through the enrichment cycles.  The result from the phage display rounds is screened for target molecule binding followed by sequencing, usually with one of the conventional screening methods ELISA or Biacore followed by Sanger sequencing. However, the throughput of these analyses are unfortunately very low, often with only a few hundred screened clones. Therefore, Next Generation Sequencing or NGS, has become an increasingly popular screening method for phage display libraries which generates millions of sequences from each phage display round. This creates a need for a robust data analysis pipeline to be able to interpret the large amounts of data.  In this project, a pipeline for analysis of NGS data of phage displayed libraries has been developed at Cytiva. Cytiva uses NGS as one of their screening methods of phage displayed protein libraries because of the high throughput compared to the conventional screening methods. The purpose is to find new affinity ligands for purification of essential substances used in drugs.  The pipeline has been created using the object-oriented programming language R and consists of several analyses covering the most important steps to be able to find promising results from the NGS data. With the developed pipeline the user can analyze the data on both DNA and protein sequence level and per position residue breakdown, as well as filter the data based on specific amino acids and positions. This gives a robust and thorough analysis which can lead to promising results that can be used in the development of novel affinity ligands for future purification products.

Droplet-based microfluidics is a powerful tool permitting massive-scale single-cell analysis in pico-/nano-liter water-in-oil droplets. It has been integrated into various library preparation techniques to accomplish high-throughput scRNA-seq, scDNA-seq, scATAC-seq, scChIP-seq, as well as scMulti-omics-seq. These advanced technologies have been providing unique and novel insights into both normal differentiation and disease development at single-cell level. In this thesis, we develop four new droplet-based tools for single-cell omics profiling. First, the developed Drop-BS is the first droplet-based platform to construct single-cell bisulfite sequencing libraries for DNA methylome profiling and allows production of BS library of 2,000-10,000 single cells within 2 d. We applied the technology to separately profile mixed cell lines, mouse brain tissues, and human brain tissues to reveal cell type heterogeneity. Second, the new Drop-ChIP platform only requires two steps of droplet generation to achieve multiple steps of reactions in droplets such as single-cell lysis, chromatin fragmentation, ChIP, and barcoding. Third, we aim to establish a droplet-based platform to accomplish high-throughput full-length RNA-seq (Drop-full-seq), which both current tube-based and droplet-based methods cannot realize. Last, we constructed an in-house droplet-based tool to assist single-cell ATAC-seq library preparation (Drop-ATAC), which provided a low-cost and facile protocol to conduct scATAC-seq in laboratories without the expensive instrument. / Doctor of Philosophy / Microfluidics is a collection of techniques to manipulate fluids in the micrometer scale. One of microfluidic techniques is called "droplet-based microfluidics". It can manipulate (i.e., generate, merge, sort, split, etc) pico-/nano-liter of water-in-oil droplets. First, since the water phase is separated by the continuous oil phase, these droplets are discrete and individual reactors. Second, droplet-based microfluidics can achieve highly parallel manipulation of thousands to millions of droplets. These two advantages make droplet-based microfluidics an ideal tool to perform single-cell assays. Over the past 10 years, various droplet-based platforms have been developed to study single-cell transcriptome, genome, epigenome, as well as multi-ome. To expand droplet-based tools for single-cell analysis, we aim to develop four novel platforms in this thesis. First, Drop-BS, by integrating droplet generation and droplet fusion techniques, can achieve high-throughput single-cell bisulfite sequencing library preparation. It can generate 10,000 single-cell BS libraries within 2 days which is difficult to achieve for conventional library preparation in tubes/microwells. Second, we developed a novel and facile Drop-ChIP platform to prepare single-cell ChIP-seq library. It is easy to operate since it only requires two steps of droplet generation. It also generates higher quality of data compared to previous work. In addition, we are working on the development and characterization of the other two droplet-based tools to achieve full-length single-cell RNA-seq and single-cell ATAC-seq.

Psychiatric disorders are believed to result from the combination of genetic predisposition and many environmental triggers. While the large number of disease-associated genetic variations have been recognized by previous genome-wide association studies (GWAS), the role of epigenetic mechanisms that mediate the effects of environmental factors on CNS gene activity in the etiology of most mental illnesses is still largely unclear. A growing body of evidence suggested that the abnormalities (changes in gene expression, formation of neural circuits, and behavior) involved in most psychiatric syndromes are preserved by epigenetic modifications identified in several specific brain regions. In this thesis, we developed the second generation of one of our microfluidic technologies (MOWChIP-seq) and used it to profile genome-wide histone modifications in three mental illness-related biological studies: the effect of psychedelics in mice, schizophrenia, and the effect of maternal immune activation in mice offspring. The second generation of MOWChIP-seq was designed to generate histone modification profiles from as few as 100 cells per assay with a throughput as high as eight assays in each run. Then, we applied the new MOWChIP-seq and SMART-seq2 to profile the histone modification H3K27ac and transcriptome, respectively, using NeuN+ neuronal nuclei from the mouse frontal cortex after a single dose of psychedelic administration. The epigenomic and transcriptomic changes induced by 2,5-Dimethoxy-4-iodoamphetamine (DOI), a subtype of psychedelics, in mouse neuronal nuclei at various time points suggest that the long-lasting effects of the psychedelic are more closely related to epigenomic alterations than the changes in transcriptomic patterns. Next, we comprehensively characterized epigenomic and transcriptomic features from the frontal cortex of 29 individuals with schizophrenia and 29 individually matched controls (gender and age). We found that schizophrenia subjects exhibited thousands of neuronal vs. glial epigenetic differences at regions that included several susceptibility genetic loci, such as NRXN1, RGS4 and GRIN3A. Finally, we investigated the epigenetic and transcriptomic alterations induced by the maternal immune activation (MIA) in mice offspring's frontal cortex. Pregnant mice were injected with influenza virus at GD 9.5 and the frontal cortex from mice pups (10 weeks old) were examined later. The results offered us some insights into the contribution of MIA to the etiology of some mental disorders, like schizophrenia and autism. / Doctor of Philosophy / While this field is still in its early stage, the epigenetic studies of mental disorders present promise to expand our understanding about how environmental stimulates, interacting with genetic factors, contribute to the etiology of various psychiatric syndromes, like major depression and schizophrenia. Previous clinical trials suggested that psychedelics may represent a promising long-lasting treatment for patients with depression and other psychiatric conditions. These research presented the therapeutic potential of psychedelic compounds for treating major depression and demonstrated the capability of psychedelics in increasing dendritic density and stimulating synapse formation. However, the molecular mechanism mediating the clinical effectiveness of psychedelics remain largely unexplored. Our study revealed that epigenomic-driven changes in synaptic plasticity sustain psychedelics' long-lasting antidepressant action. Another serious mental illness is schizophrenia, which could affect how an individual feels, thinks, and behaves. Like most other mental disorders, schizophrenia results from a combination of genetic and environmental causes. Epigenetic marks allow a dynamic impact of environmental factors, including antipsychotic medications, on the access to genes and regulatory elements. Despite this, no study so far has profiled cell-type-specific genome-wide histone modifications in postmortem brain samples from schizophrenia subjects or the effect of antipsychotic treatment on such epigenetic marks. Here we show the first comprehensive epigenomic characterization of the frontal cortex of 29 individuals with schizophrenia and 29 matched controls. The process of brain development is surprisingly sensitive to a lot of environmental insults. Epidemiological studies have recognized maternal immune activation as a risk factor that may change the normal developmental trajectory of the fetal brain and increase the odds of developing a range of psychiatric disorders, including schizophrenia and autism, in its lifetime. Given the prevalence of the coronavirus, uncovering the molecular mechanism underlie the phenotypic alterations has become more urgent than before, for both prevention and treatment.