Deciphering neuronal deficit and protein profile changes in human brain organoids from patients with creatine transporter deficiency

Creatine transporter deficiency (CTD) is an X-linked disease caused by mutations in the SLC6A8 gene. The impaired creatine uptake in the brain results in intellectual disability, behavioral disorders, language delay, and seizures. In this work, we generated human brain organoids from induced pluripotent stem cells of healthy subjects and CTD patients. Brain organoids from CTD donors had reduced creatine uptake compared with those from healthy donors. The expression of neural progenitor cell markers SOX2 and PAX6 was reduced in CTD-derived organoids, while GSK3β, a key regulator of neurogenesis, was up-regulated. Shotgun proteomics combined with integrative bioinformatic and statistical analysis identified changes in the abundance of proteins associated with intellectual disability, epilepsy, and autism. Re-establishment of the expression of a functional SLC6A8 in CTD-derived organoids restored creatine uptake and normalized the expression of SOX2, GSK3β, and other key proteins associated with clinical features of CTD patients. Our brain organoid model opens new avenues for further characterizing the CTD pathophysiology and supports the concept that reinstating creatine levels in patients with CTD could result in therapeutic efficacy

How to Compute Worst-Case Execution Time by Optimization Modulo Theory and a Clever Encoding of Program Semantics

International audienceIn systems with hard real-time constraints, it is necessary to compute upper bounds on the worst-case execution time (WCET) of programs; the closer the bound to the real WCET, the better. This is especially the case of synchronous reactive control loops with a fixed clock; the WCET of the loop body must not exceed the clock period. We compute the WCET (or at least a close upper bound thereof) as the solution of an optimization modulo theory problem that takes into account the semantics of the program, in contrast to other methods that compute the longest path whether or not it is feasible according to these semantics. Optimization modulo theory extends satisfiability modulo theory (SMT) to maximization problems. Immediate encodings of WCET problems into SMT yield formulas intractable for all current production-grade solvers; this is inherent to the DPLL(T) approach to SMT implemented in these solvers. By conjoining some appropriate "cuts" to these formulas, we considerably reduce the computation time of the SMT-solver. We experimented our approach on a variety of control programs, using the OTAWA analyzer both as baseline and as underlying microarchitectural analysis for our analysis, and show notable improvement on the WCET bound on a variety of benchmarks and control programs

Brain organoids for hypoxic-ischemic studies: from bench to bedside

Our current knowledge regarding the development of the human brain mostly derives from experimental studies on non-human primates, sheep, and rodents. However, these studies may not completely simulate all the features of human brain development as a result of species differences and variations in pre- and postnatal brain maturation. Therefore, it is important to supplement the in vivo animal models to increase the possibility that preclinical studies have appropriate relevance for potential future human trials. Three-dimensional brain organoid culture technology could complement in vivo animal studies to enhance the translatability of the preclinical animal studies and the understanding of brain-related disorders. In this review, we focus on the development of a model of hypoxic-ischemic (HI) brain injury using human brain organoids to complement the translation from animal experiments to human pathophysiology. We also discuss how the development of these tools provides potential opportunities to study fundamental aspects of the pathophysiology of HI-related brain injury including differences in the responses between males and females

Particle swarm optimization with Exponentially Varying Inertia Weight Factor for solving Multi-Area Economic Dispatch problem

This paper aimed at exploring the performance of Particle Swarm Optimisation with Exponentially Varying Inertia Weight Factor (PSO-EVIWF) for solving Multi-Area Economic Dispatch (MAED) problem with tie line constraints considering valve-point loading in each area. The effectiveness of the proposed algorithm has been verified on 4 interconnected areas with 16 generators standard test system. The paper presents the search capability and convergence behavior of the proposed method. Simulation results show that the PSO-EVIWF achieved quality solutions and smooth convergence characteristics and it is an alternative method for solving MAED problem

Gallium-68 Complexes Conjugated to Pittsburgh Compound B: Radiolabeling and Biological Evaluation

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