Miracle-dz/UniFAR-FTUs huggingface dataset:
Causal mediation analysis for time-varying heritable risk factors with Mendelian Randomization

Understanding the causal pathogenic mechanisms of diseases is crucial in clinical research. When randomized controlled experiments are not available, Mendelian Randomization (MR) offers an alternative, leveraging genetic mutations as a natural “experiment” to mitigate environmental confoundings. However, most MR analyses treat the risk factors as static variables, potentially oversimplifying dynamic risk factor effects. The framework of life-course MR has been introduced to address this issue. However, current methods face challenges especially when the age-specific GWAS datasets have limited cohort sizes and there are substantial correlations between time points for a single trait. This study proposes a novel approach, estimating a unified system of structural equations for a sequence of temporally ordered heritable traits, requiring only GWAS summary statistics. The method facilitates statistical inference on direct, indirect, and path-wise causal effects and demonstrates superior efficiency and reliability, particularly with noisy GWAS data. By incorporating a spike-and-slab prior for genetic effects, the approach can address extreme polygenicity and weak instrument bias. Through this methodology, we uncovered a protective effect of BMI on breast cancer during a confined period of childhood development. We also analyzed how BMI, systolic blood pressure (SBP), and low-density cholesterol levels influence stroke risk across childhood and adulthood, and identified the intriguing relationships between these risk factors.
Using high-resolution functional MRI to differentiate impacts of strabismic and anisometropic amblyopia on evoked ocular dominance activity in humans

We employed high-resolution functional MRI (fMRI) to distinguish the impacts of anisometropia and strabismus (the two most frequent causes of amblyopia) on the evoked ocular dominance (OD) response. Sixteen amblyopic participants (8 females), comprising 8 individuals with strabismus, 7 with anisometropia, 1 with deprivational amblyopia, along with 8 individuals with normal visual acuity (1 female), participated in this study for whom, we measured the difference between the response to stimulation of the two eyes, across early visual areas (V1-V4). In controls, as expected from the organization of OD columns, the evoked OD response formed a striped pattern that was mostly confined to V1. Compared to controls, the OD response in amblyopic participants formed larger fused patches that extended into downstream visual areas. Moreover, both anisometropic and strabismic participants showed stronger OD responses in V1, as well as in downstream visual areas V2-V4. Although this increase was most pronounced in V1, the correlation between the OD response level and the interocular visual acuity difference (measured behaviorally) was stronger in higher-level visual areas (V2–V4). Beyond these common effects, and despite similar densities of amblyopia between the anisometropic and strabismic participants, we found a greater increase in the size of V1 portion that responded preferentially to fellow eye stimulation in anisometropic compared to strabismic individuals. We also found a greater difference between the amplitudes of the response to binocular stimulation, in those regions that responded preferentially to the fellow vs. amblyopic eye, in anisometropic compared to strabismic subjects. In contrast, strabismic subjects demonstrated increased correlation between the OD responses evoked within V1 superficial and deep cortical depths, whereas anisometropic subjects did not. These results provide some of the first direct functional evidence for distinct impacts of strabismus and anisometropia on the mesoscale functional organization of the human visual system, thus extending what was inferred previously about amblyopia from animal models.
Megakaryocyte maturation involves activation of the adaptive unfolded protein response

Endoplasmic reticulum stress triggers the unfolded protein response (UPR) to promote cell survival or apoptosis. Transient endoplasmic reticulum stress activation has been reported to trigger megakaryocyte production, and UPR activation has been reported as a feature of megakaryocytic cancers. However, the role of UPR signaling in megakaryocyte biology is not fully understood. We studied the involvement of UPR in human megakaryopoiesis using PMA (phorbol 12-myristate 13-acetate)-induced maturation of megakaryoblastic cell lines and thrombopoietin-induced differentiation of human peripheral blood-derived progenitors. Our results demonstrate that the adaptive UPR is a feature of megakaryocytic maturation, independent of ER stress-induced apoptosis, with the IRE1α UPR pathway being particularly important. Differentiation did not alter the ability of megakaryocytic cells to respond to the endoplasmic reticulum stressors DTT or thapsigargin. However, thapsigargin, but not DTT, inhibited differentiation, consistent with the involvement of Ca2+ signaling in megakaryopoiesis.
PDG-Arena: An ecophysiological model for characterizing tree-tree interactions in heterogeneous stands

In the context of ongoing climate and biodiversity crises, mixed forest stands are increasingly considered as a sustainable management alternative to mono-cultures. We developed a new individual-based and process-based forest growth model, PDG-Arena, to simulate mixed forest functioning and test ecophysiological interactions among trees in mixed stands. The model builds upon of a validated ecophysiological stand-scale model and integrates tree competition for light and water. We evaluated the simulation performance of PDG-Arena using annual growth data from 39 common beech and silver fir monospecific and mixed plots in the French Alps. PDG-Arena showed similar performance as the validated stand-scale model when simulating even-age and monospecific forests, and significantly better performance when using structure-diverse and species-diverse inventories. It also showed a significant positive effect of species mixing on gross primary production, canopy absorbance and transpiration. Our results thus show that tree-level process-based models such as PDG-Arena, formally simulating interspecific interactions, are needed to better understand and simulate the functioning of mixed stands.
A micro-RNA drives a 100-million-year adaptive evolution of melanic patterns in butterflies and moths

A long-standing question in biology is how phenotypic variation is generated from genetic variation. In Lepidoptera (butterflies and moths), an enigmatic genomic region containing the protein-coding gene cortex is repeatedly used to generate intraspecific wing color pattern polymorphisms in multiple species. While emerging evidence suggest that cortex itself is not the effector gene of this locus, the identity of the effector remains unknown. Here, we investigated two deeply conserved miRNAs embedded in the cortex locus, and discovered that one of them, mir-193, promotes melanic wing color. This miRNA is likely derived from an intron of a gigantic long non-coding RNA spanning the entire locus, and it elicits its function by directly repressing multiple pigmentation genes including a well-known melanin pathway gene, ebony. The function of mir-193 is conserved in a nymphalid and a pierid butterfly, belonging two families that diverged 90 Mya. We propose that mir-193 is the effector gene of this hotspot locus underlying a 100-million-year evolution of intraspecific wing color pattern polymorphisms in Lepidoptera. Our results suggest that small non-coding RNAs can drive adaptive evolution in animals.
KDM6A Regulates Immune Response Genes in Multiple Myeloma

The histone H3K27 demethylase KDM6A is a tumor suppressor in multiple cancers, including multiple myeloma (MM). We created isogenic MM cells disrupted for KDM6A and tagged the endogenous protein to facilitate genome wide studies. KDM6A binds genes associated with immune recognition and cytokine signaling. Most importantly, KDM6A binds and activates NLRC5 and CIITA encoding regulators of Major Histocompatibility Complex (MHC) genes. Patient data indicate that NLRC5 and CIITA, are downregulated in MM with low KDM6A expression. Chromatin analysis shows that KDM6A binds poised and active enhancers and KDM6A loss led to decreased H3K27ac at enhancers, increased H3K27me3 levels in body of genes bound by KDM6A and decreased gene expression. Reestablishing histone acetylation with an HDAC3 inhibitor leads to upregulation of MHC expression, offering a strategy to restore immunogenicity of KDM6A deficient tumors. Loss of Kdm6a in murine RAS-transformed fibroblasts led to increased growth in vivo associated with decreased T cell infiltration. Statement of significance We show that KDM6A participates in immune recognition of myeloma tumor cells by directly regulating the expression of the master regulators of MHC-I and II, NLRC5 and CIITA. The expression of these regulators can by rescued by the HDAC3 inhibitors in KDM6A-null cell lines.
Impaired drainage through capillary-venous networks contributes to age-related white matter loss

The gradual loss of cerebral white matter contributes to cognitive decline during aging. However, microvascular networks that support the metabolic demands of white matter remain poorly defined. We used in vivo deep multi-photon imaging to characterize microvascular networks that perfuse cortical layer 6 and corpus callosum, a highly studied region of white matter in the mouse brain. We show that these deep tissues are exclusively drained by sparse and wide-reaching venules, termed principal cortical venules, which mirror vascular architecture at the human cortical-U fiber interface. During aging, capillary networks draining into deep branches of principal cortical venules are selectively constricted, reduced in density, and diminished in pericyte numbers. This causes hypo-perfusion in deep tissues, and correlates with gliosis and demyelination, whereas superficial tissues become relatively hyper-perfused. Thus, age-related impairment of capillary-venular drainage is a key vascular deficit that contributes to the unique vulnerability of cerebral white matter during brain aging.
Evolutionary forecasting of phenotypic and genetic outcomes of experimental evolution in Pseudomonas

Experimental evolution is often highly repeatable, but the underlying causes are generally unknown, which prevents extension of evolutionary forecasts to related species. Data on adaptive phenotypes, mutation rates and targets from the Pseudomonas fluorescens SBW25 Wrinkly Spreader system combined with mathematical models of the genotype-to-phenotype map allowed evolutionary forecasts to be made for several related Pseudomonas species. Predicted outcomes of experimental evolution in terms of phenotype, types of mutations, relative rates of pathways and mutational targets were then tested in Pseudomonas protegens Pf-5. As predicted, most mutations were found in three specific regulatory pathways resulting in increased production of Pel exopolysaccharide. Mutations were, as predicted, mainly found to disrupt negative regulation with a smaller number in upstream promoter regions. Mutated regions in proteins could also be predicted, but most mutations were not identical to those previously found. This study demonstrates the potential of short-term evolutionary forecasting in experimental populations. Impact statement Conservation of genotype-to-phenotype maps allows successful prediction of short-term evolution in P. protegens Pf-5 and lays the foundation for evolutionary forecasting in other Pseudomonas.
Designing for Hand Ownership in Interaction with Virtual and Augmented Reality

This thesis explores the body ownership illusion during interaction in virtual and augmented reality; in particular, how the body ownership illusion can be leveraged to overcome the limitations of our physical bodies and to create more efficient and engaging interactions. Body ownership is the feeling that we inhabit a physical body through which we experience and interact with the world. The conviction that your physical hand is, in fact, your own hand is an example of body ownership. Under certain conditions, this feeling can also be elicited by an artificial body or body part (e.g., a rubber hand or virtual hand), which is then known as the body ownership illusion. The research questions presented in this thesis were explored with two independent prototypes: the first consisting of an augmented reality system and the second providing a virtual reality experience. The state-of-the-art for immersive virtual reality involves interaction with the virtual world through controllers that we perceive as tools hovering in mid-air. We cannot see our hands gripping these tools, nor do we have any other indicators of our body in our peripheral vision or through the presence of shadows. This ghostly, body-less state is unnatural, and a constant reminder that the world we see around us is not real. Our sense of presence is limited if we do not have a body that inhabits part of the environment we perceive. In augmented reality applications on the other hand, we typically perceive our own body as part of the real world and can sometimes use our physical hands to manipulate virtual content. In contrast to virtual hands, however, our physical hands cannot be flexibly adapted for a specific task (e.g., to extend our reach). Hence, I explore how we can create a virtual body representation to which the user feels strongly connected through the body ownership illusion. I argue that this inherently changes the quality of the interaction; while traditionally the screen forms a clear boundary between the real and the virtual world, the illusion of owning a virtual body moves this border into our own minds. In other words, a virtual body representation may seem just as real to us as our physical bodies. The findings presented in this thesis show that we can control an unnaturally long virtual arm that we perceive as part of our body. Furthermore, we can feel body ownership of a virtual hand that appears to be reaching towards an overhead target, while our physical hand rests comfortably at waist-level. We may even feel body ownership of a very unrealistic, disconnected virtual hand, if it follows our movements naturally. The results thus indicate that our mental body representation may be more malleable than was previously believed. Consequently, while most approaches to interaction design attempt to modify the virtual world or tools with which we interact, we can instead augment the body we interact through or at least our mental image thereof.
TECHNISCHE UNIVERSITÄT DRESDEN

Multimodal image fusion merge images of different modalities into a single image which contains greater information than any of the input images. In medical field, multimodal image fusion plays a crucial role in providing medical practitioners sufficient information about the input images for clinical purposes. In recent years, deep learning (DL) based image fusion has achieved remarkable breakthroughs and state of the art results owing to strong capability in feature extraction. One of the challenge that is common in the DL based image fusion is the unavailability of ground truth. Therefore, this thesis aims to develop a convolutional neural network based medical image fusion approach on two different 2D modalities without any groundtruth information. The end to end learning framework proposed in this work presents a completely new approach to deal with the problem of multimodal medical image fusion where we obtain a fused image as an output of our network. Our results shows that we achieve promising results after comparing the method with other medical image fusion approaches available in literature.
RNA secondary structure design under simple and complex constraints (RNA Sekundärstruktur-Design unter einfachen und komplexen Bedingungen)

The structure of RNA molecules is often crucial for their function. Therefore, designing RNA sequences that fold into a given structure with special features is of interest for biologists in several fields. In this thesis, we introduce two approaches dealing with RNA sequence design. First, we develop a fast and successful new approach to the inverse RNA folding problem, called INFO-RNA, incorporating constraints on the sequence, while afterwards, our interests focus on a more complex design of RNA sequences taking into account the translated amino acid sequences. We develop an algorithm, called SECISDesign, that solves this complex task successfully. In the first part of this thesis, we focus on the search for an RNA sequence S = S1.Sn that folds into a given secondary structure T and fulfills given constraints C = C1.Cn on the primary sequence. Our new approach INFO-RNA consists of two parts; a dynamic programming method for good initial sequences and a following improved stochastic local search that uses an effective neighbor selection method. During the initialization, we design a sequence that among all sequences adopts the given structure with the lowest possible energy. There is no other sequence that has lower energy when folding into this structure. Nevertheless, the sequence is not guaranteed to fold into T since it can have even less energy when folding into another structure. Therefore, the resulting sequence is processed further using a stochastic local search. For the selection of neighbors during this search, we use a kind of look-ahead of one selection step applying an additional energy-based criterion. Afterwards, the pre-ordered neighbors are tested using the actual optimization criterion of minimizing the structure distance between the target structure and the structure of the considered neighbor with minimal free energy or maximizing the probability of folding into T . We compare INFO-RNA to the two existing tools RNAinverse and RNA-SSD using artificial as well as biological test sets. Running INFO-RNA, we perform better than RNAinverse and in most cases, we obtain better results than RNA-SSD, the probably best inverse RNA folding tool on the market.
A Multi-mode RCPSP with Stochastic Nonrenewable Resource Consumption

Many processes within production scheduling and project management involve the scheduling of a number of activities, each activity having a certain duration and requiring a certain amount of limited resources. The duration and resource requirements of activities are commonly the result of estimations, and thus generally subject to uncertainty. If this uncertainty is not taken into account the resulting schedules may not be robust in the sense that, when executed, the uncertainty may cause the schedules to take longer than expected, consume more resources, or be outright infeasible. We propose a new variant of the Multi-mode Resource-Constrained Project Scheduling Problem, where the nonrenewable resource requirements of each mode is given by a Gaussian distribution, and the nonrenewable resource constraints must be satisﬁed with a certain probability (cid:15) . Such constraints are also known as chance constraints. We present a Conic Quadratic Integer Program model of the problem, and describe and experiment with a branch-and-cut algorithm for solving the problem. In each node of the branch-and-bound tree, the branching decisions are propagated in order to remove variables from the problem, and thus improve bounds. In addition we experiment with cutting on the conic quadratic resource constraints. Computational experiments show that the branch-and-cut algorithm outperforms CPLEX 12.1. We ﬁnally examine the “cost of uncertainty” by investigating the relation between values of (cid:15) , the makespan, and the solution time. and involve the scheduling of a number of activities, each activity having a certain duration and requiring a certain amount of limited The duration and resource requirements of activities are commonly the of estima-tions, and thus generally subject to uncertainty. If this uncertainty is not taken into account the resulting schedules may not be robust in the sense that, when executed, the uncertainty may cause the schedules to take longer than expected, consume moreresources, or be outright infeasible. We propose a new variant of the Multi-mode Resource-Constrained Project Scheduling Problem, where the nonrenewable resource requirements of each mode is given by a Gaussian distribution, and the nonrenewable resource constraints must be satisfied with a certain probability p. Such constraints are also known as chance constraints. We present a Conic Quadratic Integer Program model of the problem, and describe and experiment with a branch-and-cut algorithm for solving the problem. In each node of thebranch-and-bound tree, the branching decisions are propagated in order to remove variables from the problem, and thus improve bounds. In addition we experiment with cutting on the conic quadratic resource constraints. Computational experiments show that the branch-and-cut algorithm outperforms CPLEX 12.1. We finally examine the “cost of uncertainty” by investigating the relation between values of p, the makespan, and the solution time.
Wiretap channel with side information

This paper gives a rate equivocation achievable region for the discrete memoryless wiretap channel with side information. The secrecy capacities in some special cases are also determined. We extend our result to the Gaussian case. It is very interesting to find that, for the wiretap channel, unlike the dirty paper channel, the side information helps to get larger secrecy capacity. Moreover, the rate equivocation region is also larger than that of the Gaussian wiretap channel where the side information is absent
Economic Implications of a Changing Yield Weather Relationship

ECONOMIC IMPLICATIONS OF A CHANGING YIELD WEATHER RELATIONSHIP Regan Arntz-Gray Advisor: University of Guelph, 2017 Professor Alan P. Ker Average corn yield in Iowa and Ontario has significantly increased, potentially suggesting a corresponding increase in input demand. This implies that the yield-weather relationship may have changed over time. Yield modelling is used to test this hypothesis, and the results suggest that the yield-precipitation relationship is dynamic. To estimate the impact of neglecting the dynamic relationship, both a static and dynamic model were used to forecast yields given simulated weather from the current climate, as well as from a potential future climate. The yield forecasts were then used to estimate and compare the resulting actuarially fair premium rates. The results showed that using the static model led to biased premium rate estimates. Expected yield was calculated under both current and future climate. The mean expected yield was higher given climate change in Ontario but lower in Iowa. In both Iowa and Ontario, the variance of expected yield was higher under climate change.
Lateral Motion Control for Obstacle Avoidance in Autonomous Driving Based on Deep Reinforcement Learning

To assist autonomous vehicles in confronting obstacle collision avoidance situations, a model-free lateral motion control method is proposed for vehicle collision avoidance based on deep reinforcement learning (DRL). Specifically, Twin Delayed Deep Deterministic Policy Gradients (TD3) is introduced as the main framework for training the lateral controller. A dynamic driving risk field is established to assess the driving risk by quantifying the impact of obstacles and roads on the host vehicle. Then, a reward function accounting for both driving risk and comfort is incorporated into the TD3 training framework to optimize steering performance. Finally, the TD3 agent learns to control steering maneuvers for obstacle avoidance in a fashion that maximizes cumulative rewards via trials and errors in the CARLA simulation environment. The training and testing results showed that the proposed controller could generate real-time, safe and comfortable steering maneuvers to avoid collision in dense traffic scenarios.