Les intoxications médicamenteuses volontaires répétées (IMVr) constituent un problème de santé publique croissant, mais sous-évalué et traité comme une conduite suicidaire, alors qu’elles appartiendraient au registre des addictions. Notre travail abonde dans ce sens en montrant que les suicides alcoolisés se font principalement par IMV et correspondent au deliberate self-harm syndrom ; les suicidants récidivants se distinguent par un névrosisme et un attachement anxieux typiques des addictions ; les IMVr même suicidaires témoignent d’une relation addictive aux médicaments ; le facteur le plus prédictif d’IMV serait de l’avoir envisagée. Conceptualisé de façon translationnelle et argumenté par une approche pharmacologique, nous proposons un modèle dimensionnel des conduites suicidaires, intégré au continuum des addictions, confirmant l’hypothèse initiale et l’intérêt d’un raisonnement transnosographique diagnostique et thérapeutique dans le champ des pathologies mentales. / Repeated Self-poisoning (RSP) constitute an under evaluated but growing public health problem, treated as a suicidal rather than an addictive behavior. Our work brings arguments by showing that suicides involving alcohol are mainly by self-poisoning and correspond to deliberate self-harm syndrome; repeat suicide attempters are identified by a neuroticism and anxious attachment typically found in addicts; even suicidal RSP shows addictive behavior involving medicines; the best predictor of self-poisoning is having thought about it. Conceptualized through a translational approach and supported by pharmacological arguments, we propose a multidimensional model of suicidal behaviors, that could integrate the continuum of addictive behaviors. This confirms the initial hypothesis and the viability of a transnosographic concept for diagnosis and treatment of mental illnesses.

Mechanical properties of cells and their environment have an undeniable impact on physiological and pathological processes such as tissue development or cancer metastasis. Hence, there is a pressing need for establishing and validating methodologies for measuring the mechanical properties of cells, as well as for deciphering the molecular underpinnings that govern the mechanical phenotype. During my doctoral research, I addressed these needs by pushing the boundaries of the field of single-cell mechanics in four projects, two of which were method-oriented and two explored important biological questions. First, I consolidated real-time deformability cytometry as a method for high-throughput single-cell mechanical phenotyping and contributed to its transformation into a versatile image-based cell characterization and sorting platform. Importantly, this platform can be used not only to sort cells based on image-derived parameters, but also to train neural networks to recognize and sort cells of interest based on raw images. Second, I performed a cross-laboratory study comparing three microfluidics-based deformability cytometry approaches operating at different timescales in two standardized assays of osmotic shock and actin disassembly. This study revealed that while all three methods are sensitive to osmotic shock-induced changes in cell deformability, the method operating at the shortest timescale is not suited for detection of actin cytoskeleton changes. Third, I demonstrated changes in cell mechanical phenotype associated with cell fate specification on the example of differentiation and de-differentiation along the neural lineage. In the process of reprogramming to pluripotency, neural precursor cells acquired progressively stiffer phenotype, that was reversed in the process of neural differentiation. The stiff phenotype of induced pluripotent stem cells was equivalent to that of embryonic stem cells, suggesting that mechanical properties of cells are inherent to their developmental stage. Finally, I identified and validated novel target genes involved in the regulation of mechanical properties of cells. The targets were identified using machine learning-based network analysis of transcriptomic profiles associated with mechanical phenotype change, and validated computationally as well as in genetic perturbation experiments. In particular, I showed that the gene with the best in silico performance, CAV1, changes the mechanical properties of cells when silenced or overexpressed. Identification of novel targets for mechanical phenotype modification is crucial for future explorations of physiological and pathological roles of cell mechanics. Together, this thesis encompasses a collection of contributions at the frontier of single-cell mechanical characterization across timescales and cell state transitions, and lays ground for turning cell mechanics from a correlative phenomenological parameter to a controllable property.:Abstract Kurzfassung List of Publications Contents Introduction Chapter 1 — Background 1.1. Mechanical properties as a marker of cell state in health and disease 1.2. Functional relevance of single-cell mechanical properties 1.3. Internal structures determining mechanical properties of cells 1.4. Cell as a viscoelastic material 1.5. Methods to measure single-cell mechanical properties Aims and scope of this thesis Chapter 2 — RT-DC as a versatile method for image-based cell characterization and sorting 2.1. RT-DC for mechanical characterization of cells 2.1.1. Operation of the RT-DC setup 2.1.2. Extracting Young’s modulus from RT-DC data 2.2. Additional functionalities implemented to the RT-DC setup 2.2.1. 1D fluorescence readout in three spectral channels 2.2.2. SSAW-based active cell sorting 2.3. Beyond assessment of cell mechanics — emerging applications 2.3.1. Deformation-assisted population separation and sorting 2.3.2. Brightness-based identification and sorting of blood cells 2.3.3. Transferring molecular specificity into label-free cell sorting 2.4. Discussion 2.5. Key conclusions 2.6. Materials and experimental procedures 2.7. Data analysis Chapter 3 — A comparison of three deformability cytometry classes operating at different timescales 3.1. Results 3.1.1. Representatives of the three deformability cytometry classes 3.1.2. Osmotic shock-induced deformability changes are detectable in all three methods 3.1.3. Ability to detect actin disassembly is method-dependent 3.1.4. Strain rate increase decreases the range of deformability response to actin disassembly in sDC 3.2. Discussion 3.3. Key conclusions 3.4. Materials and methods Chapter 4 — Mechanical journey of neural progenitor cells to pluripotency and back 4.1. Results 4.1.1. fNPCs become progressively stiffer during reprogramming to pluripotency 4.1.2. Transgene-dependent F-class cells are more compliant than ESC-like iPSCs 4.1.3. Surface markers unravel mechanical subpopulations at intermediate reprogramming stages 4.1.4. Neural differentiation of iPSCs mechanically mirrors reprogramming of fNPCs 4.1.5. The closer to the pluripotency, the higher the cell stiffness 4.2. Discussion 4.3. Key conclusions 4.4. Materials and methods Chapter 5 — Data-driven approach for de novo identification of cell mechanics regulators 5.1. Results 5.1.1. An overview of the mechanomics approach 5.1.2. Model systems characterized by mechanical phenotype changes 5.1.3. Discriminative network analysis on discovery datasets 5.1.4. Conserved functional network module comprises five genes 5.1.5. CAV1 performs best at classifying soft and stiff cell states in validation datasets 5.1.6. Perturbing expression levels of CAV1 changes cells stiffness 5.2. Discussion 5.3. Key conclusions 5.4. Materials and methods Conclusions and Outlook Appendix A Appendix B Supplementary Tables B.1 – B.2 Supplementary Figures B.1 – B.9 Appendix C Supplementary Tables C.1 – C.2. Supplementary Figures C.1 – C.5 Appendix D Supplementary Tables D.1 – D.6 Supplementary Figures D.1 – D.7 List of Figures List of Tables List of Abbreviations. List of Symbols References Acknowledgements

<p>Heterosis, or hybrid vigor, is a phenomenon observed in both plant and animal systems where hybrid offspring perform better when compared to their parents. For hybrid plants, this can result in increased biomass, crop yields, and vigor when compared to the inbred parents. Even though heterosis has been used in agriculture for over a century, the molecular mechanisms that result in hybrid vigor remain elusive even after years of investigation. A molecular understanding of heterosis is desirable because it will speed up the process of breeding compatible inbred lines for developing hybrid seeds, and it will provide us with the knowledge to potentially engineer inbred lines that can mimic the beneficial phenotypic effects of heterosis, eliminating the need for farmers to buy new hybrid seeds every year. The goal of this research project is to identify genes that are required for heterotic phenotypes in maize. Our working hypothesis is that a mutation in genes that are essential for heterosis will cause an altered heterotic phenotype in hybrid maize plants. To test this hypothesis, we applied combined approaches of EMS mutagenesis, trait phenotyping in field and controlled conditions, bulk segregant analysis, whole genome sequencing, and bioinformatics analysis. First, we applied a forward genetics approach to identify mutant hybrids with altered heterosis and detected potential causal genes <em>via</em> whole genome sequencing. We identified one mutation occurring in a protein coding gene (gene ID <em>Zm00001eb305590</em>) located in a region of interest on chromosome 7, whose genotypes across various samples assayed fit the observed segregation pattern of hybrid traits. This mutation leads to a moderate or high-level codon change, indicating that this gene may play a role in mediating heterosis in maize. By investigating this gene with further studies, the learned knowledge could speed up the process of hybrid maize breeding by selecting compatible inbred lines through sequencing or by engineering hybrids that have favorable alleles for this gene.</p>

Indiana University-Purdue University Indianapolis (IUPUI) / In the past, the present and the future, Lebanon has been an important link between the East and the West. It was always known as the ‘Switzerland of the East’. Over the years, it was a hotspot for different civilizations that uniquely shaped the genomic backbone of the current Lebanese. It is also a good representation of genetically admixed individuals with diverse phenotype characteristics and unique features. Lebanon, quite like other Middle Eastern populations, lacks sufficient genetic studies that helps to better comprehend the complex genomic composition of different traits and diseases. The lack of good representation of the Middle East and North Africa (MENA) region in global studies has led to ambiguity in discovering special ancestry markers and patterns in the Lebanese genome. Yet, in this study, a thorough investigation into a Lebanese collection shows new patterns that potentially would be helpful in forensic and genealogical applications. The investigation into the autosomal and Y-STRs revealed unique alleles that would be valuable in future forensic investigation analysis. In addition, the assessment of phenotype prediction models to predict eye, hair and skin color showed promising results in terms of prediction performance. Those results encourage the future use of intelligence tools in the regions that in return would aid in serving justice and furthering science research. In fact, ancestry and genetic distance studies confirms the presence of admixture within Lebanon between Europe and North Africa. / 2029-06-01

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Autism Spectrum Disorder (ASD) is a condition that carries high costs for families and the healthcare system, requiring extensive management both in terms of diagnosis and treatment. The implementation of AI-based systems in clinical practice represents a possible supportive solution that can help clinicians by providing more systematic meth- ods to monitor child behavior. The main advantage over more traditional observational approaches is to offer quantitative and refined analysis solutions that can be ecological at the same time. The relevance of AI in clinical applications can have a role both in the challenge of early detection and in designing intervention programs better tai- lored to the specific functioning of children with ASD. The research project presented in this dissertation focused on developing AI-based systems for fine-grained analysis of autism-related social behaviors and their validation in concrete clinical environments. Specifically, in Chapter 2, our first study is presented, which targets on implementing a computational phenotyping system to address the need for new early markers of the condition. Through fine-grained analytics of facial dynamics in videos, we identified a set of features that distinguished young (6-12 months) infants with ASD (18 ASD, 15 non-ASD) during unconstrained at-home interactions. In Chapters 3 and 4, we introduce EYE-C, a Behavior Imaging model for robust analysis of eye contact episodes in eco- logical therapist-child interactions. The system was validated in the clinical setting for personalized early intervention. First, we investigated the influence of extracted features in categorizing spectrum heterogeneity across a sample of 62 preschool (&lt;6 years) chil- dren with ASD. Further, we tested our metrics as predictors of early intensive treatment outcomes in a sub-sample of 18 subjects with ASD. The project aims to demonstrate the feasibility of effective computational systems that are robust to the high variability of unstructured interactions, with emphasis on the applicative value in real-world scenar- ios. Even though based on limited sample sizes, the work presented may offer interesting insights into the perspective of integrating AI into clinical practice. The research project was funded by an FBK scholarship and developed in a double in- ternship at ODFLab (University of Trento) and the FBK Data Science for Health (DSH) research unit.

<p>  </p> <p>Dicamba (3,6-dichloro-2-methoxybenzoic acid) and 2,4-D (2,4-dichlorophenoxyacetic acid) are two widely used herbicides for broadleaf weed control in soybeans. However, off-target application of dicamba and 2,4-D can cause severe damage to sensitive vegetation and crops. Early detection and assessment of off-target damage caused by these herbicides are necessary to help plant diagnostic labs and state regulatory agencies collect more information of the on-site conditions so to develop solutions to resolve the issue in the future. In 2021, the study was conducted to detect damage to soybean leaves caused by dicamba and 2,4-D by using LeafSpec, an accurate handheld hyperspectral leaf scanner. . High resolution single leaf hyperspectral images of 180 soybean plants in the greenhouse exposed to nine different herbicide treatments were taken 1, 7, 14, 21 and 28 days after herbicide spraying. Pairwise PLS-DA models based on spectral features were able to distinguish leaf damage caused by two different modes of action herbicides, specifically dicamba and 2,4-D, as early as 2 hours after herbicide spraying. In the spatial distribution analysis, texture and morphological features were selected for separating the dosages of herbicide treatments. Compared to the mean spectrum method, new models built upon the spectrum, texture, and morphological features, improved the overall accuracy to over 70% for all evaluation dates. The combined features are able to classify the correct dosage of the right herbicide as early as 7 days after herbicide sprays. Overall, this work has demonstrated the potential of using spectral and spatial features of LeafSpec hyperspectral images for early and accurate detection of dicamba and 2,4-D damage in soybean plants.</p> <p>   </p>

[pt] O presente trabalho aborda o monitoramento remoto em tempo real de pessoas com Transtorno Afetivo Bipolar e sua interação com seus dispositivos móveis. Algumas abordagens na área da ciência da computação apresentam experiências para coletar informações subjetivas as quais podem ser usadas por especialistas em benefício de pessoas com necessidades específicas. Por outra parte, a fenotipagem digital é um campo de ciência multidisciplinar, usado para descrever uma nova abordagem para acompanhar a interação dos usuários com seu smartphone e poder mapear transtornos fazendo uso dos sensores do celular. Embora algumas pesquisas avaliem as vantagens dos aplicativos móveis para o tratamento das doenças mentais, na literatura não foi encontrada nenhuma solução que faça uso da análise do fenótipo digital e a visualização da informação em tempo real como marcadores de estado e traço de intervenção terapêutica não invasiva, ao detectar precocemente alterações nos padrões comportamentais dos pacientes. Neste trabalho, apresentamos o BraPolar, uma m-Health para monitoramento remoto de pacientes com Transtorno Afetivo Bipolar, apresentando em tempo real flutuações de humor e comportamentos nos participantes através da interação com seus dispositivos móveis. A fenotipagem digital coletada é apresentada aos especialistas em tempo real poderá ajudar a prever alterações no comportamento das pessoas antes que atinjam consequências funcionais extremas. Neste estudo, apresentamos avaliações de usabilidade piloto envolvendo seis usuários não portadores da doença e cinco especialistas para avaliar a percepção que tem do aplicativo. Apresentamos também um estudo longitudinal realizado durante um mês e avaliação em tempo real com especialistas das áreas de psicologia e psiquiatria. Finalmente, apresentamos o potencial de BraPolar no monitoramento remoto de pessoas com transtorno bipolar. / [en] This work addresses the real-time remote monitoring of people with Bipolar Affective Disorder and their interaction with their mobile devices. Some approaches in computer science present experiments for collecting subjective information that can be used by experts for the benefit of people with specific needs. On the other hand, digital phenotyping is a multidisciplinary field of science, used to describe a new approach to monitor user interaction withtheir smartphone and to map disturbances using cell phone sensors. Although, some research assesses the advantages of mobile applications for the treatment of mental illness but no solution has been found in the literature that makes use of digital phenotype analysis and real-time information visualization as state markers and non-invasive therapeutic intervention traits by early detection of changes in patients behavioral patterns. In this dissertation we present BraPolar, an m-Health for remote monitoring of patients with Bipolar Affective Disorder, featuring real-time fluctuations in mood and behavior in participants through interaction with their mobile devices. Collected digital phenotyping presented to real-time specialists can help predict changes in people s behavior before they reach extreme functional consequences. In this study, we present pilot usability assessments involving six non-disease users and five experts to assess their perception of the application. We also present a longitudinal study conducted over a month and real-time evaluation with experts in the fields of psychology and psychiatry. Finally, we present the potential of BraPolar in remote monitoring of people with bipolar disorder.

<p> Significant portions of nitrogen (40–60%), phosphorus (80–90%) and potash (30–50%) applied in agricultural fields are not taken up by plants, causing serious issues for farmers and the environment. Fertilizer losses result in greater fertilizer input costs and the cost of fertilizer is projected to increase due to limited ore resources and increasing fossil fuel prices. Moreover, excess fertilizer application can contaminate water and air, resulting in human health problems. Leaching fertilizers also induce eutrophication, acid rain and global climate change. Therefore, developing crops with high nutrient uptake efficiency is important for economic and environmental sustainability of agriculture. Crop improvement depends on efficiency and accuracy of genotyping and phenotyping. Genotyping has improved in recent years and is generally efficient and accurate. In contrast, improvements in phenotyping lag far behind. Lack of high-throughput (efficient, accurate and inexpensive) phenotyping (HTP) methods limit the speed of genetic improvement. As a result, there is an increasing interest in development of HTP for predicting crop nutrient status. My research addresses whether hyperspectral data in the visible-near-infrared range (HS-VNIR) acquired by a handheld device or an unmanned aerial vehicle (UAV) can be used for predicting maize nutrient status. Proximal and remote sensing data coupled with ground reference measurements of hybrid maize nutrient status were collected in fertilizer strip trials conducted at Purdue Agricultural Centers located throughout Indiana. Statistical models were developed to predict nutrient status based on HS-VNIR with coefficients of determination of cross-validation [R² (CV)] used to evaluate the performance of the predictive models. Models with acceptable goodness-of-fit [R² (CV) > 0.30] were considered satisfactory. These studies demonstrated that models developed using handheld proximal sensing data performed adequately for predicting N, K, Mg, Ca, P, S, Mn, Zn and B. Similarly, models developed using UAV-based HS-VNIR could be used to predict N, K, Mg, Ca, P, S, Mn, Zn and B. Models that combine proximal and remote sensing data also performed well with predictions of N, K, Mg, Ca, P, S, Mn, Zn and B. In conclusion, handheld or UAV-based hyperspectral imaging can provide corn breeders with HTP data on the status of all macronutrients (N, K, Mg, Ca, P, S) and some micronutrients (Mn, Zn, B). Deployment of this technology may provide a valuable tool to support development of cultivars with improved nutrient uptake efficiencies.  </p>

Raman spectroscopy, together with multivariate statistical analyses, has proven to be a near real-time analytical technique capable of phenotyping cells, tissues and organs. This dissertation will show exclusively the application of the Raman spectroscopy phenotypic profiling method to; (i) microbial toxicity, (ii) ex-vivo organ perfusion, and (iii) subcellular location targeting. Real-time analytical methods for monitoring living biological systems will enable study of the physiological changes associated with growth, genetic manipulations, and adverse environmental conditions. Most existing analytical methods (NMR exempt), though highly accurate, must be performed off-line and most require destruction of the studied sample. These attributes make these methodologies less desirable to the study of physiological changes of cells, tissues, and organs. In this work, Raman spectroscopy has been identified and shown to be a good candidate for real-time analysis mainly because it can be performed: (i) in near real-time, (ii) non-destructively and with minimal sample preparation, (iii) through a glass barrier (i.e., can be performed in situ), and (iv) with minimal spectral interference from water. Here, Raman spectroscopy was used in combination with multivariate statistics to analyze the differing toxic effects of 4-C chain alcohols on E. coli. Good correlations were established between Raman spectra and off-line analytical techniques used to measure: (i) saturated, unsaturated, and cyclopropane fatty acids; (ii) amino acid composition of total protein; and (iii) cell membrane fluidity. Also, Raman 'fingerprint' analysis was used to discriminate among different phenotypic responses of cells. In addition, this methodology was applied to analyze perfusates of organs maintained by the VasoWave® organ perfusion system. Raman fingerprints can be used to assess organ health, and it is believed this data can be used to inform decisions such as whether or not to transplant an organ. Finally, molecular biology techniques were used to design and produce specific protein targets harboring a silver binding domain fusion, which upon release migrate to specific subcellular locations. By employing the related technique of surface-enhanced Raman scattering (SERS), which produces a highly amplified Raman signal in the presence of metallic nanoparticle substrates (e.g., silver nanoparticles), different regions of the E. coli cell structure were studied. The target regions studied by the technique included: (i) outer cell membrane, (ii) periplasm, and the (iii) cytoplasm. / Ph. D.