Objective To study the effect and methods of orbital septum fat redistribution or autogenous orbital septum granular fat transplantation methods on upper eyelid depression.Methods Blepharoplasty incision was used to cut open musculus orbicularis oculi and septum orbitale horizontally,exposuring orbital fat,the conjunction was relieved between orbit fat envelope and septum orbitale,displaced orbital fat was extended and replaced to the depression region of upper eyelid,and the wound was routinely sutured.For orbital septum fat volume depletion,appropriate amount of granular fat was injected into depression region and put fat backward upto supraorbital margin.Then,resetting orbital septum and closing incision were conducted with suture technique of blepharoplasty.Results Of 48 cases (96 eyes),32 cases was followed-up for 6 to 42 months with mean of 11 months.All cases got ideal outcomes,with smooth and symmetry lid folds,depression of upper eyelid were fixed and no complication occurred.Conclusions Orbital septum fat redistribution or autogenous orbital septum granular fat transplantation can anatomically reduce the fat distribution of upper eyelid.This method is easy and effective to improve the appearance of eyelid.
Key words:
Depression of upper eyelid; Orbital septum fat redistribution; Granular fat transplantation
The plasma protein von Willebrand factor (VWF) is essential for hemostasis initiation at sites of vascular injury. The platelet-binding A1 domain of VWF is connected to the VWF N-terminally located D'D3 domain through a relatively unstructured amino acid sequence, called here the N-terminal linker. This region has previously been shown to inhibit the binding of VWF to the platelet surface receptor glycoprotein Ibα (GpIbα). However, the molecular mechanism underlying the inhibitory function of the N-terminal linker has not been elucidated. Here, we show that an aspartate at position 1261 is the most critical residue of the N-terminal linker for inhibiting binding of the VWF A1 domain to GpIbα on platelets in blood flow. Through a combination of molecular dynamics simulations, mutagenesis, and A1-GpIbα binding experiments, we identified a network of salt bridges between Asp1261 and the rest of A1 that lock the N-terminal linker in place such that it reduces binding to GpIbα. Mutations aimed at disrupting any of these salt bridges activated binding unless the mutated residue also formed a salt bridge with GpIbα, in which case the mutations inhibited the binding. These results show that interactions between charged amino acid residues are important both to directly stabilize the A1-GpIbα complex and to indirectly destabilize the complex through the N-terminal linker.
Central nervous system lymphatic drainage system (CNSLDS) is composed of glymphatic system, perivascular lymphatic drainage pathways and meningeal lymphatic vessels. Based on new findings of structures and functions of CNSLDS, CNSLDS is one of the mechanisms for promoting the clearance of β-amyloid (Aβ). CNSLDS functions physiologically as a route of drainage for Aβ from glymphatic system or perivascular lymphatic drainage pathways to meningeal lymphatic vessels, and the meningeal lymphatic vessels helps Aβ drainage to the nearby lymph nodes. In this review, we summarize the key component elements (structure and function) in the clearance of Aβ during the CNS lymphatic drainage. Also, we highlight their potential roles in the pathogenesis of Alzheimer's disease and their clinical importance in diagnosis and treatment of neurologic diseases associated with Aβ, including Alzheimer's disease.
Key words:
Central nervous system; Lymphatic drainage system; Glymphatic system; Meningeal lymphatic vessel; β-amyloid protein
Hierarchical federated learning (HFL) has recently emerged as a more practical machine learning (ML) paradigm, which enables edge servers (ESs) in close proximity to conduct partial model aggregation. Despite its utility, local training and model aggregation incur considerable computation and communication time. client selection (CS) has proven effective for minimizing latency. However, CS faces the following challenges in hierarchical federated learning (HFL). First, the accessible clients, computation resources and network bandwidth are time-varying and unpredictable. Second, certain dynamics can only be observed after the decisions are made. Third, multiple ESs face different unknown clients, increasing the difficulty of selecting clients in an online manner. Finally, resource usage may be excessively violated during the training process. Existing HFL researches are insufficient to tackle these challenges. This work proposes a multi- ESs CS framework (MCS), which is based on multiarmed bandit (MAB) technique. MCS aims to reduce the cumulative computation and communication time, using two algorithms: 1) an online learning-based CS algorithm (OCA) makes the CS decisions for each ES, based on empirical learning results; and 2) a randomized rounding algorithm (RRA) converts fractional decisions obtained by OCA into binary solutions. Theoretically, MCS can enjoy the sublinear regret and violation compared to the optimal strategy. Practically, extensive experiments on real-world data sets demonstrate the empirical superiority of MCS over multiple state-of-the-art algorithms in minimizing cumulative latency.
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