biomechanical-effects-partial-decortication-on-all-suture

Biomechanical Effects of Partial Decortication on All-Suture and Conventional Suture Anchors in Different Bone Densities

While biomechanical testing has shown a correlation between decortication and anchor failure load, the effects of partial decortication on the ...

Chinese Academy of Sciences

The locations and shapes of synapses are important in reconstructing connectomes and analyzing synaptic plasticity. However, current synapse detection and segmentation methods are still not adequate for accurately acquiring the synaptic connectivity, and they cannot effectively alleviate the burden of synapse validation.

Effective automated pipeline for 3D reconstruction of synapses based on deep learning.

It is becoming increasingly clear that the quantification of mitochondria and synapses is of great significance to understand the function of biological nervous systems. Electron microscopy (EM), with the necessary resolution in three directions, is the only available imaging method to look closely into these issues. Therefore, estimating the number of mitochondria and synapses from the serial EM images is coming into prominence. Since previous studies have achieved preferable 2D segmentation performance, it holds great promise to obtain the 3D connection relationship from the 2D segmentation results.

A fast forward 3D connection algorithm for mitochondria and synapse segmentations from serial EM images.

Recent studies have supported the relation between mitochondrial functions and degenerative disorders related to ageing, such as Alzheimer's and Parkinson's diseases. Since these studies have exposed the need for detailed and high-resolution analysis of physical alterations in mitochondria, it is necessary to be able to perform segmentation and 3D reconstruction of mitochondria. However, due to the variety of mitochondrial structures, automated mitochondria segmentation and reconstruction in electron microscopy (EM) images have proven to be a difficult and challenging task. This paper puts forward an effective and automated pipeline based on deep learning to realize mitochondria segmentation in different EM images. The proposed pipeline consists of three parts: (1) utilizing image registration and histogram equalization as image pre-processing steps to maintain the consistency of the dataset; (2) proposing an effective approach for 3D mitochondria segmentation based on a volumetric, residual convolutional and deeply supervised network; and (3) employing a 3D connection method to obtain the relationship of mitochondria and displaying the 3D reconstruction results. To our knowledge, we are the first researchers to utilize a 3D fully residual convolutional network with a deeply supervised strategy to improve the accuracy of mitochondria segmentation. The experimental results on anisotropic and isotropic EM volumes demonstrate the effectiveness of our method, and the Jaccard index of our segmentation (91.8% in anisotropy, 90.0% in isotropy) and F1 score of detection (92.2% in anisotropy, 90.9% in isotropy) suggest that our approach achieved state-of-the-art results. Our fully automated pipeline contributes to the development of neuroscience by providing neurologists with a rapid approach for obtaining rich mitochondria statistics and helping them elucidate the mechanism and function of mitochondria.

Automatic Mitochondria Segmentation for EM Data Using a 3D Supervised Convolutional Network.

Mitochondrial calcium ([Ca]) dynamics plays vital roles in regulating fundamental cellular and organellar functions including bioenergetics. However, neuronal [Ca] dynamics in vivo and its regulation by brain activity are largely unknown. By performing two-photon Ca imaging in the primary motor (M1) and visual cortexes (V1) of awake behaving mice, we find that discrete [Ca] transients occur synchronously over somatic and dendritic mitochondrial network, and couple with cytosolic calcium ([Ca]) transients in a probabilistic, rather than deterministic manner. The amplitude, duration, and frequency of [Ca] transients constitute important determinants of the coupling, and the coupling fidelity is greatly increased during treadmill running (in M1 neurons) and visual stimulation (in V1 neurons). Moreover, Ca/calmodulin kinase II is mechanistically involved in modulating the dynamic coupling process. Thus, activity-dependent dynamic [Ca]-to-[Ca] coupling affords an important mechanism whereby [Ca] decodes brain activity for the regulation of mitochondrial bioenergetics to meet fluctuating neuronal energy demands as well as for neuronal information processing.

Brain activity regulates loose coupling between mitochondrial and cytosolic Ca transients.

Stabilized Cu species have been widely considered as catalytic active sites in composite copper catalysts for catalytic reactions with industrial importance. However, few examples comprehensively explicated the origin of stabilized Cu in a low-cost and widely investigated CuO/TiO system. In this study, mass producible CuO/TiO catalysts with interface-stabilized Cu were prepared, which showed excellent low-temperature CO oxidation activity. A thorough characterization and theoretical calculations proved that the strong charge-transfer effect and Ti-O-Cu hybridization in Ti-doped CuO(111) at the CuO/TiO interface contributed to the formation and stabilization of Cu species. The CO molecule adsorbed on Cu and reacted directly with Ti doping-promoted active lattice oxygen via a Mars-van Krevelen mechanism, leading to the enhanced low-temperature activity.

Elucidating the Nature of the Cu(I) Active Site in CuO/TiO for Excellent Low-Temperature CO Oxidation.

Virus identification in electron microscopy (EM) images is considered as one of the front-line method in pathogen diagnosis and re-emerging infectious agents. However, the existing methods either focused on the detection of a single virus or required large amounts of manual labeling work to segment virus. In this work, we focus on the task of virus classification and propose an effective and simple method to identify different viruses.

Virus identification in electron microscopy images by residual mixed attention network.

The controlled synthesis of large-scale ferroelectric domains with high uniformity is crucial for practical applications in next-generation nanoelectronics on the basis of their intriguing properties. Here, ultralong and highly uniform stripe domains in (110)-oriented BiFeO thin films are large-area synthesized through a pulsed laser deposition technique. Utilizing scanning transmission electron microscopy and piezoresponse force microscopy, we verified that the ferroelectric domains have one-dimensional 109° domains and the length of a domain is up to centimeter scale. More importantly, the ferroelectric displacement is directly determined on atomic-scale precision, further confirming the domain structure. We find that the unique one-dimensional ferroelectric domain significantly enhances the optical anisotropy. Furthermore, we demonstrate that the purely parallel domain patterns can be used to control photovoltaic current. These ultralong ferroelectric domains can be patterned into various functional devices, which may inspire research efforts to explore their properties and various applications.

Large-Scale Epitaxial Growth of Ultralong Stripe BiFeO Films and Anisotropic Optical Properties.

The goal of micro-connectomics research is to reconstruct the connectome and elucidate the mechanisms and functions of the nervous system via electron microscopy (EM). Due to the enormous variety of neuronal structures, neuron segmentation is among most difficult tasks in connectome reconstruction, and neuroanatomists desperately need a reliable neuronal structure segmentation method to reduce the burden of manual labeling and validation.

Deep residual contextual and subpixel convolution network for automated neuronal structure segmentation in micro-connectomics.

The UV/Cl process is commonly used to achieve a multiple-barrier disinfection and maintain residuals. The study chose methylamine as a precursor to study the formation of high-toxic halonitromethanes (HNMs) in the presence of bromide ions (Br) during UV/Cl disinfection. The maximum yield of HNMs increased first and then decreased with increasing concentration of Br. An excessively high concentration of Br induced the maximum yield of HNMs in advance. The maximum bromine incorporation factor (BIF) increased, while the maximum bromine utilization factor (BUF) decreased with the increase of Br concentration. The maximum yield of HNMs decreased as pH value increased from 6.0 to 8.0 due to the deprotonation process. The BUF value remained relatively higher under an acidic condition, while pH value had no evident influence on the BIF value. The maximum yield of HNMs and value of BUF maximized at a Cl:Br ratio of 12.5, whereas the BIF value remained relatively higher at low Cl:Br ratios (2.5 and 5). The amino group in methylamine was first halogenated, and then released into solution as inorganic nitrogen by the rupture of C-N bond or transformed to nitro group by oxidation and elimination pathways. The maximum yield of HNMs in real waters was higher than that in pure water due to the high content of dissolved organic carbon. Two real waters were sampled to verify the law of HNMs formation. This study helps to understand the HNMs formation (especially brominated species) when the UV/Cl process is adopted as a disinfection technique.

Formation of halonitromethanes from methylamine in the presence of bromide during UV/Cl disinfection.

The enhancement of osteogenesis and angiogenesis remains a great challenge for the successful regeneration of engineered tissue. Biodegradable Mg and Zn alloys have received increasing interest as potential biodegradable metallic materials, partially due to the biological functions of Mg and Zn with regard to osteogenesis and angiogenesis, respectively. In the present study, novel biodegradable Zn-xMg (x = 0.2, 0.5, 1.0 wt.%) alloys were designed and fabricated, and the effects of adding different amounts of Mg to the Zn matrix were investigated. The osteogenesis and angiogenesis beneficial effects of Zn and Mg release during the biodegradation were characterized, demonstrating coordination with the bone regeneration process in a dose-dependent manner. The results show that increased Mg content leads to a higher amount of released Mg while decreasing the Zn concentration in the extract. The osteogenesis of pre-osteoblasts was promoted in Zn-0.5Mg and Zn-1Mg due to the higher concentration of Mg. Moreover, pure Zn extract presented the highest activity in angiogenesis, owing to the highest concentration of Zn release (6.415 μg/mL); the proliferation of osteoblast cells was, however, inhibited under such a high Zn concentration. Although the concentration of Zn ion was decreased in Zn-0.5Mg and Zn-1Mg compared with pure Zn, the angiogenesis was not influenced when the concentration of Mg in the extract was sufficiently increased. Hence, Mg and Zn in Zn-Mg alloys show a dual modulation effect. The Zn-0.5Mg alloy was indicated to be a promising implant candidate due to demonstrating the appropriate activity in regulating osteogenesis and angiogenesis. The present work evaluates the effect of the Mg content in Zn-based alloys on biological activities, and the results provide guidance regarding the Zn-Mg composition in designs for orthopedic application.

Modulation of Osteogenesis and Angiogenesis Activities Based on Ionic Release from Zn-Mg Alloys.

The identification of viruses from negative staining transmission electron microscopy (TEM) images has mainly depended on experienced experts. Recent advances in artificial intelligence have enabled virus recognition using deep learning techniques. However, most of the existing methods only perform virus classification or semantic segmentation, and few studies have addressed the challenge of virus instance segmentation in TEM images. In this paper, we focus on the instance segmentation of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) and other respiratory viruses and provide experts with more effective information about viruses. We propose an effective virus instance segmentation network based on the You Only Look At CoefficienTs backbone, which integrates the Swin Transformer, dense connections and the coordinate-spatial attention mechanism, to identify SARS-CoV-2, H1N1 influenza virus, respiratory syncytial virus, Herpes simplex virus-1, Human adenovirus type 5 and Vaccinia virus. We also provide a public TEM virus dataset and conduct extensive comparative experiments. Our method achieves a mean average precision score of 83.8 and F1 score of 0.920, outperforming other state-of-the-art instance segmentation algorithms. The proposed automated method provides virologists with an effective approach for recognizing and identifying SARS-CoV-2 and assisting in the diagnosis of viruses. Our dataset and code are accessible at https://github.com/xiaochiHNU/Virus-Instance-Segmentation-Transformer-Network.

VISN: virus instance segmentation network for TEM images using deep attention transformer.

Solar carbon dioxide (CO) reduction provides an attractive alternative to producing sustainable chemicals and fuel. However, the construction of a highly active photocatalyst was challenging because of the rapid charge recombination and sluggish surface CO reduction. Herein, a unique Co-NCl single site was fabricated by loading Co species into the 2,2'-bipyridine and triazine-containing covalent organic framework (COF) for CO conversion into syngas under visible light irradiation. The resulting champion catalyst TPy-COF-Co enabled a record-high CO production rate of 426 mmol g h, associated with the unprecedented turnover number (TON) and turnover frequency (TOF) of 2095 and 1607 h, respectively. The catalyst also exhibited favorable recycling performance and widely adjustable syngas production (CO/H ratio: 1.8 : 1-1 : 16). A systematical investigation including operando synchrotron X-ray absorption fine structure (XAFS) spectroscopy, in situ attenuated total reflection surface-enhanced infrared absorption spectroscopy (ATR-SEIRAS), and theoretical calculation indicated that the triazine-based COF framework promoted the charge transfer towards the single Co-NCl sites that greatly promoted the CO activation by lowering the energy barrier of *COOH generation, facilitating the CO transformation. This work highlights the great potential of the molecular regulation of COF-derived single-atom catalysts to boost CO photoreduction efficiency.

Article	Year
Effective automated pipeline for 3D reconstruction of synapses based on deep learning. BMC bioinformatics\| PubMed ID: 30005590	2018
A fast forward 3D connection algorithm for mitochondria and synapse segmentations from serial EM images. BioData mining\| PubMed ID: 30410581	2018
Automatic Mitochondria Segmentation for EM Data Using a 3D Supervised Convolutional Network. Frontiers in neuroanatomy\| PubMed ID: 30450040	2018
Brain activity regulates loose coupling between mitochondrial and cytosolic Ca transients. Nature communications\| PubMed ID: 31754099	2019
Elucidating the Nature of the Cu(I) Active Site in CuO/TiO for Excellent Low-Temperature CO Oxidation. ACS applied materials & interfaces\| PubMed ID: 31931575	2020
Virus identification in electron microscopy images by residual mixed attention network. Computer methods and programs in biomedicine\| PubMed ID: 33059061	2021
Large-Scale Epitaxial Growth of Ultralong Stripe BiFeO Films and Anisotropic Optical Properties. ACS applied materials & interfaces\| PubMed ID: 35129325	2022
Deep residual contextual and subpixel convolution network for automated neuronal structure segmentation in micro-connectomics. Computer methods and programs in biomedicine\| PubMed ID: 35338886	2022
Formation of halonitromethanes from methylamine in the presence of bromide during UV/Cl disinfection. Journal of environmental sciences (China)\| PubMed ID: 35725080	2022
Modulation of Osteogenesis and Angiogenesis Activities Based on Ionic Release from Zn-Mg Alloys. Materials (Basel, Switzerland)\| PubMed ID: 36295204	2022
VISN: virus instance segmentation network for TEM images using deep attention transformer. Briefings in bioinformatics\| PubMed ID: 37903415	2023
Covalent Organic Framework Stabilized Single CoNCl Site Boosts Photocatalytic CO Reduction into Tunable Syngas. Angewandte Chemie (International ed. in English)\| PubMed ID: 39193917	2025

Chi Xiao

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