The Multi-Scalar Inequities of Climate Adaptation Finance: A Critical Review

Purpose of Review: Following a multi-scalar analytical approach, this critical literature review explores the factors that determine adaptation finance accessibility and allocation with particular attention to how the needs of climate-vulnerable communities are considered. Recent Findings: Our review reveals that climate vulnerability is not a primary determinant in the accessibility and allocation of climate adaptation finance at inter-state, sub-national and local scales. Instead, factors such as institutional capacities and financial and political interests exert significant influence. This leads to maladaptation and multi-scalar inequities where climate finance favours relatively resilient groups across scales with less support for more vulnerable populations. Summary: We argue that finance does not trickle down, but “ripples” within a climate finance arena – where we define the latter as a messy space of competition, negotiation and collaboration. To unlock equitable adaptation finance patterns, future research should focus on the multi-scalar configurations of adaptation finance beyond the international level and consider local and regional territorial and scalar politics. © The Author(s) 2024.K.V. acknowledges a fellowship from “la Caixa” Foundation (LCF/BQ/DR21/11880005). K.V. and M.O. are supported by María de Maeztu Excellence Unit 2023-2027 Ref. CEX2021- 001201-M, funded by MCIN/AEI /https://doi.org/10.13039/50110 0011033; and by the Basque Government through the BERC 2022- 2025 program. K.V. also acknowledges the ICTA-UAB María de Maeztu excellence accreditation (CEX2019-000940-M). M.G.L. acknowledges support during 2023 from a Marie Skł

Spatio-temporal correlations can drastically change the response of a MAPK pathway

Multisite covalent modification of proteins is omnipresent in eukaryotic cells. A well-known example is the mitogen-activated protein kinase (MAPK) cascade, where in each layer of the cascade a protein is phosphorylated at two sites. It has long been known that the response of a MAPK pathway strongly depends on whether the enzymes that modify the protein act processively or distributively: distributive mechanism, in which the enzyme molecules have to release the substrate molecules in between the modification of the two sites, can generate an ultrasensitive response and lead to hysteresis and bistability. We study by Green's Function Reaction Dynamics, a stochastic scheme that makes it possible to simulate biochemical networks at the particle level and in time and space, a dual phosphorylation cycle in which the enzymes act according to a distributive mechanism. We find that the response of this network can differ dramatically from that predicted by a mean-field analysis based on the chemical rate equations. In particular, rapid rebindings of the enzyme molecules to the substrate molecules after modification of the first site can markedly speed up the response, and lead to loss of ultrasensitivity and bistability. In essence, rapid enzyme-substrate rebindings can turn a distributive mechanism into a processive mechanism. We argue that slow ADP release by the enzymes can protect the system against these rapid rebindings, thus enabling ultrasensitivity and bistability

An adaptive finite element procedure for fully-coupled point contact elastohydrodynamic lubrication problems

This paper presents an automatic locally adaptive finite element solver for the fully-coupled EHL point contact problems. The proposed algorithm uses a posteriori error estimation in the stress in order to control adaptivity in both the elasticity and lubrication domains. The implementation is based on the fact that the solution of the linear elasticity equation exhibits large variations close to the fluid domain on which the Reynolds equation is solved. Thus the local refinement in such region not only improves the accuracy of the elastic deformation solution significantly but also yield an improved accuracy in the pressure profile due to increase in the spatial resolution of fluid domain. Thus, the improved traction boundary conditions lead to even better approximation of the elastic deformation. Hence, a simple and an effective way to develop an adaptive procedure for the fully-coupled EHL problem is to apply the local refinement to the linear elasticity mesh. The proposed algorithm also seeks to improve the quality of refined meshes to ensure the best overall accuracy. It is shown that the adaptive procedure effectively refines the elements in the region(s) showing the largest local error in their solution, and reduces the overall error with optimal computational cost for a variety of EHL cases. Specifically, the computational cost of proposed adaptive algorithm is shown to be linear with respect to problem size as the number of refinement levels grows

Magnetically Decorated Multiwalled Carbon Nanotubes as Dual MRI and SPECT Contrast Agents

Carbon nanotubes (CNTs) have been proposed as one of the most promising nanomaterials to be used in biomedicine for their applications in drug/gene delivery as well as biomedical imaging. The present study developed radio-labeled iron oxide decorated multi-walled CNTs (MWNT) as dual magnetic resonance (MR) and single photon emission computed tomography (SPECT) imaging agents. Hybrids containing different amounts of iron oxide were synthesized by in situ generation. Physicochemical characterisations revealed the presence of superparamagnetic iron oxide nanoparticles (SPION) granted the magnetic properties of the hybrids. Further comprehensive examinations including high resolution transmission electron microscopy (HRTEM), fast Fourier transform simulations (FFT), X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) assured the conformation of prepared SPION as γ-Fe(2)O(3). High r(2) relaxivities were obtained in both phantom and in vivo MRI compared to the clinically approved SPION Endorem(®). The hybrids were successfully radio-labeled with technetium-99m through a functionalized bisphosphonate and enabled SPECT/CT imaging and γ-scintigraphy to quantitatively analyze the biodistribution in mice. No abnormality was found by histological examination and the presence of SPION and MWNT were identified by Perls stain and Neutral Red stain, respectively. TEM images of liver and spleen tissues showed the co-localization of SPION and MWNT within the same intracellular vesicles, indicating the in vivo stability of the hybrids after intravenous injection. The results demonstrated the capability of the present SPION-MWNT hybrids as dual MRI and SPECT contrast agents for in vivo use

Expanding the molecular and phenotypic spectrum of truncating MT-ATP6 mutations

Objective To describe the clinical and functional consequences of 1 novel and 1 previously reported truncating MT-ATP6 mutation. Methods Three unrelated probands with mitochondrial encephalomyopathy harboring truncating MT-ATP6 mutations are reported. Transmitochondrial cybrid cell studies were used to confirm pathogenicity of 1 novel variant, and the effects of all 3 mutations on ATPase 6 and complex V structure and function were investigated. Results Patient 1 presented with adult-onset cerebellar ataxia, chronic kidney disease, and diabetes, whereas patient 2 had myoclonic epilepsy and cerebellar ataxia; both harbored the novel m.8782G>A; p.(Gly86*) mutation. Patient 3 exhibited cognitive decline, with posterior white matter abnormalities on brain MRI, and severely impaired renal function requiring transplantation. The m.8618dup; p.(Thr33Hisfs*32) mutation, previously associated with neurogenic muscle weakness, ataxia, and retinitis pigmentosa, was identified. All 3 probands demonstrated a broad range of heteroplasmy across different tissue types. Blue-native gel electrophoresis of cultured fibroblasts and skeletal muscle tissue confirmed multiple bands, suggestive of impaired complex V assembly. Microscale oxygraphy showed reduced basal respiration and adenosine triphosphate synthesis, while reactive oxygen species generation was increased. Transmitochondrial cybrid cell lines studies confirmed the deleterious effects of the novel m.8782 G>A; p.(Gly86*) mutation. Conclusions We expand the clinical and molecular spectrum of MT-ATP6-related mitochondrial disorders to include leukodystrophy, renal disease, and myoclonic epilepsy with cerebellar ataxia. Truncating MT-ATP6 mutations may exhibit highly variable mutant levels across different tissue types, an important consideration during genetic counseling

Consensus Considerations and Good Practice Points for Use of Continuous Glucose Monitoring Systems in Hospital Settings.

Continuous glucose monitoring (CGM) systems provide frequent glucose measurements in interstitial fluid and have been used widely in ambulatory settings for diabetes management. During the coronavirus disease 2019 (COVID-19) pandemic, regulators in the U.S. and Canada temporarily allowed for CGM systems to be used in hospitals with the aim of reducing health care professional COVID-19 exposure and limiting use of personal protective equipment. As such, studies on hospital CGM system use have been possible. With improved sensor accuracy, there is increased interest in CGM usage for diabetes management in hospitals. Laboratorians and health care professionals must determine how to integrate CGM usage into practice. The aim of this consensus guidance document is to provide an update on the application of CGM systems in hospital, with insights and opinions from laboratory medicine, endocrinology, and nursing

A Resource Aware MapReduce Based Parallel SVM for Large Scale Image Classifications

Machine learning techniques have facilitated image retrieval by automatically classifying and annotating images with keywords. Among them support vector machines (SVMs) are used extensively due to their generalization properties. However, SVM training is notably a computationally intensive process especially when the training dataset is large. This paper presents RASMO, a resource aware MapReduce based parallel SVM algorithm for large scale image classifications which partitions the training data set into smaller subsets and optimizes SVM training in parallel using a cluster of computers. A genetic algorithm based load balancing scheme is designed to optimize the performance of RASMO in heterogeneous computing environments. RASMO is evaluated in both experimental and simulation environments. The results show that the parallel SVM algorithm reduces the training time significantly compared with the sequential SMO algorithm while maintaining a high level of accuracy in classifications.National Basic Research Program (973) of China under Grant 2014CB34040