A novel magnetic fluid shock absorber with levitating magnets

The paper presents a shock absorber whose working element includes two magnetic fluid rings around a group of magnets. The damping efficiency of this shock absorber is investigated by the free oscillations of an elastic plate and can be well explained with the classical equations of motion. In the shock absorber, a nonlinear equivalent stiffness is provided by the magnetic repulsion force, which controls the movement of the working element and varies in conformity to a power law. Through the theoretical and experimental study on the magnetic repulsion force, the nonlinear equivalent stiffness is determined and depends on the initial distance between the working element and the repulsion magnet. For an oscillation with the amplitude of 1mm and frequency of 1.1 Hz, the damping efficiency is inversely proportional to the nonlinear equivalent stiffness

A Bayesian method for linear, inequality-constrained adjustment and its application to GPS positioning

One of the typical approaches to linear, inequality-constrained adjustment (LICA) is to solve a least-squares (LS) problem subject to the linear inequality constraints. The main disadvantage of this approach is that the statistical properties of the estimate are not easily determined and thus no general conclusions about the superiority of the estimate can be made. A new approach to solving the LICA problem is proposed. The linear inequality constraints are converted into prior information on the parameters with a uniform distribution, and consequently the LICA problem is reformulated into a Bayesian estimation problem. It is shown that the LS estimate of the LICA problem is identical to the Bayesian estimate based on the mode of the posterior distribution. Finally, the Bayesian method is applied to GPS positioning. Results for four field tests show that, when height information is used, the GPS phase ambiguity resolution can be improved significantly and the new approach is feasible

Identification of Four Potential Epigenetic Modulators from the NCI Structural Diversity Library Using a Cell-Based Assay

Epigenetic pathways help control the expression of genes. In cancer and other diseases, aberrant silencing or overexpression of genes, such as those that control cell growth, can greatly contribute to pathogenesis. Access to these genes by the transcriptional machinery is largely mediated by chemical modifications of DNA or histones, which are controlled by epigenetic enzymes, making these enzymes attractive targets for drug discovery. Here we describe the characterization of a locus derepression assay, a fluorescence-based mammalian cellular system which was used to screen the NCI structural diversity library for novel epigenetic modulators using an automated imaging platform. Four structurally unique compounds were uncovered that, when further investigated, showed distinct activities. These compounds block the viability of lung cancer and melanoma cells, prevent cell cycle progression, and/or inhibit histone deacetylase activity, altering levels of cellular histone acetylation

Surgical Instruction Generation with Transformers

Automatic surgical instruction generation is a prerequisite towards intra-operative context-aware surgical assistance. However, generating instructions from surgical scenes is challenging, as it requires jointly understanding the surgical activity of current view and modelling relationships between visual information and textual description. Inspired by the neural machine translation and imaging captioning tasks in open domain, we introduce a transformer-backboned encoder-decoder network with self-critical reinforcement learning to generate instructions from surgical images. We evaluate the effectiveness of our method on DAISI dataset, which includes 290 procedures from various medical disciplines. Our approach outperforms the existing baseline over all caption evaluation metrics. The results demonstrate the benefits of the encoder-decoder structure backboned by transformer in handling multimodal context

Research advances and trends in the surgical treatment of carpal tunnel syndrome from 2003 to 2022: A CiteSpace-based bibliometric analysis

BackgroundCarpal Tunnel Syndrome (CTS) is one of the most common peripheral neuropathies. The typical symptoms are tingling and numbness in the median nerve distribution of the hand. Current treatment for CTS includes general conservative treatment and surgical treatment. Surgical treatment plays a crucial role in the management of CTS, but little bibliometric analysis has been conducted on it. Therefore, this study aimed to map the literature co-citation network using CiteSpace (6.1 R4) software. Research frontiers and trends were identified by retrieving subject headings with significant changing word frequency trends, which can be used to predict future research advances in the surgical treatment of CTS.MethodsPublications on the surgical treatment of CTS in the Web of Science database were collected between 2003 and 2022. CiteSpace software was applied to visualize and analyze publications, countries, institutions, journals, authors, references, and keywords.ResultsA total of 336 articles were collected, with the USA being the major publishing power in all countries/regions. JOURNAL OF HAND SURGERY AMERICAN VOLUME was the journal with the most published and co-cited articles. Based on keyword and reference co-citation analysis, keywords such as CTS, surgery, release, median nerve, and diagnosis were the focus of the study.ConclusionThe results of this bibliometric study provide clinical research advances and trends in the surgical treatment of patients with CTS over the past 20 years, which may help researchers to identify hot topics and explore new directions for future research in the field

Multiple phenotypic changes in mice after knockout of the B3gnt5 gene, encoding Lc3 synthase--a key enzyme in lacto-neolacto ganglioside synthesis

<p>Abstract</p> <p>Background</p> <p>Ganglioside biosynthesis occurs through a multi-enzymatic pathway which at the lactosylceramide step is branched into several biosynthetic series. Lc3 synthase utilizes a variety of galactose-terminated glycolipids as acceptors by establishing a glycosidic bond in the beta-1,3-linkage to GlcNaAc to extend the lacto- and neolacto-series gangliosides. In order to examine the lacto-series ganglioside functions in mice, we used gene knockout technology to generate Lc3 synthase gene <it>B3gnt5-</it>deficient mice by two different strategies and compared the phenotypes of the two null mouse groups with each other and with their wild-type counterparts.</p> <p>Results</p> <p><it>B3gnt5 </it>gene knockout mutant mice appeared normal in the embryonic stage and, if they survived delivery, remained normal during early life. However, about 9% developed early-stage growth retardation, 11% died postnatally in less than 2 months, and adults tended to die in 5-15 months, demonstrating splenomegaly and notably enlarged lymph nodes. Without lacto-neolacto series gangliosides, both homozygous and heterozygous mice gradually displayed fur loss or obesity, and breeding mice demonstrated reproductive defects. Furthermore, <it>B3gnt5 </it>gene knockout disrupted the functional integrity of B cells, as manifested by a decrease in B-cell numbers in the spleen, germinal center disappearance, and less efficiency to proliferate in hybridoma fusion.</p> <p>Conclusions</p> <p>These novel results demonstrate unequivocally that lacto-neolacto series gangliosides are essential to multiple physiological functions, especially the control of reproductive output, and spleen B-cell abnormality. We also report the generation of anti-IgG response against the lacto-series gangliosides 3'-isoLM1 and 3',6'-isoLD1.</p

Peripheral Administration of the Soluble TNF Inhibitor XPro1595 Modifies Brain Immune Cell Profiles, Decreases Beta-Amyloid Plaque Load, and Rescues Impaired Long-Term Potentiation in 5xFAD Mice

Clinical and animal model studies have implicated inflammation and peripheral immune cell responses in the pathophysiology of Alzheimer’s disease (AD). Peripheral immune cells including T cells circulate in the cerebrospinal fluid (CSF) of healthy adults and are found in the brains of AD patients and AD rodent models. Blocking entry of peripheral macrophages into the CNS was reported to increase amyloid burden in an AD mouse model. To assess inflammation in the 5xFAD (Tg) mouse model, we first quantified central and immune cell profiles in the deep cervical lymph nodes and spleen. In the brains of Tg mice, activated (MHCII+, CD45high, and Ly6Chigh) myeloid-derived CD11b+ immune cells are decreased while CD3+ T cells are increased as a function of age relative to non-Tg mice. These immunological changes along with evidence of increased mRNA levels for several cytokines suggest that immune regulation and trafficking patterns are altered in Tg mice. Levels of soluble Tumor Necrosis Factor (sTNF) modulate blood-brain barrier (BBB) permeability and are increased in CSF and brain parenchyma post-mortem in AD subjects and Tg mice. We report here that in vivo peripheral administration of XPro1595, a novel biologic that sequesters sTNF into inactive heterotrimers, reduced the age-dependent increase in activated immune cells in Tg mice, while decreasing the overall number of CD4+ T cells. In addition, XPro1595 treatment in vivo rescued impaired long-term potentiation (LTP) measured in brain slices in association with decreased Aβ plaques in the subiculum. Selective targeting of sTNF may modulate brain immune cell infiltration, and prevent or delay neuronal dysfunction in AD

SD-Net: joint surgical gesture recognition and skill assessment.

PURPOSE: Surgical gesture recognition has been an essential task for providing intraoperative context-aware assistance and scheduling clinical resources. However, previous methods present limitations in catching long-range temporal information, and many of them require additional sensors. To address these challenges, we propose a symmetric dilated network, namely SD-Net, to jointly recognize surgical gestures and assess surgical skill levels only using RGB surgical video sequences. METHODS: We utilize symmetric 1D temporal dilated convolution layers to hierarchically capture gesture clues under different receptive fields such that features in different time span can be aggregated. In addition, a self-attention network is bridged in the middle to calculate the global frame-to-frame relativity. RESULTS: We evaluate our method on a robotic suturing task from the JIGSAWS dataset. The gesture recognition task largely outperforms the state of the arts on the frame-wise accuracy up to [Formula: see text] 6 points and the F1@50 score [Formula: see text] 8 points. We also keep the 100% predicted accuracy for the skill assessment task using LOSO validation scheme. CONCLUSION: The results indicate that our architecture is able to obtain representative surgical video features by extensively considering the spatial, temporal and relational context from raw video input. Furthermore, the better performance in multi-task learning implies that surgical skill assessment has a complementary effects to gesture recognition task

Data-driven train set crash dynamics simulation

© 2016 Informa UK Limited, trading as Taylor & Francis GroupTraditional finite element (FE) methods are arguably expensive in computation/simulation of the train crash. High computational cost limits their direct applications in investigating dynamic behaviours of an entire train set for crashworthiness design and structural optimisation. On the contrary, multi-body modelling is widely used because of its low computational cost with the trade-off in accuracy. In this study, a data-driven train crash modelling method is proposed to improve the performance of a multi-body dynamics simulation of train set crash without increasing the computational burden. This is achieved by the parallel random forest algorithm, which is a machine learning approach that extracts useful patterns of force–displacement curves and predicts a force–displacement relation in a given collision condition from a collection of offline FE simulation data on various collision conditions, namely different crash velocities in our analysis. Using the FE simulation results as a benchmark, we compared our method with traditional multi-body modelling methods and the result shows that our data-driven method improves the accuracy over traditional multi-body models in train crash simulation and runs at the same level of efficiency