Effect of nonlinear and noncollinear transformation strain pathways in phase-field modeling of nucleation and growth during martensite transformation

The phase-field microelasticity theory has exhibited great capacities in studying elasticity and its effects on microstructure evolution due to various structural and chemical non-uniformities (impurities and defects) in solids. However, the usually adopted linear and/or collinear coupling between eigen transformation strain tensors and order parameters in phase-field microelasticity have excluded many nonlinear transformation pathways that have been revealed in many atomistic calculations. Here we extend phase-field microelasticity by adopting general nonlinear and noncollinear eigen transformation strain paths, which allows for the incorporation of complex transformation pathways and provides a multiscale modeling scheme linking atomistic mechanisms with overall kinetics to better describe solid-state phase transformations. Our case study on a generic cubic to tetragonal martensitic transformation shows that nonlinear transformation pathways can significantly alter the nucleation and growth rates, as well as the configuration and activation energy of the critical nuclei. It is also found that for a pure-shear martensitic transformation, depending on the actual transformation pathway, the nuclei and austenite/martensite interfaces can have nonzero far-field hydrostatic stress and may thus interact with other crystalline defects such as point defects and/or background tension/compression field in a more profound way than what is expected from a linear transformation pathway. Further significance is discussed on the implication of vacancy clustering at austenite/martensite interfaces and segregation at coherent precipitate/matrix interfaces.National Science Foundation (U.S.). Division of Materials Research (DMR-1410322)National Science Foundation (U.S.). Division of Materials Research (DMR-1410636

Neuroinflammation and brain–peripheral interaction in ischemic stroke: A narrative review

Excessive immune activation within the lesion site can be observed after stroke onset. Such neuroinflammation within the brain parenchyma represents the innate immune response, as well as the result of the additional interactions between peripheral and resident immune cells. Accumulative studies have illustrated that the pathological process of ischemic stroke is associated with resident and peripheral immunity. The infiltration of peripheral immune cells within the brain parenchyma implicitly contributes to secondary brain injuries. Therefore, better understanding of the roles of resident and peripheral immune reactions toward ischemic insult is necessary. In this review, we summarized the interaction between peripheral and resident immunity on systemic immunity and the clinical outcomes after stroke onset and also discussed various potential immunotherapeutic strategies

Prevotella genus and its related NOD-like receptor signaling pathway in young males with stage III periodontitis

BackgroundAs periodontitis progresses, the oral microbiota community changes dynamically. In this study, we evaluated the dominant bacteria and their roles in the potential pathway in young males with stage III periodontitis.Methods16S rRNA sequencing was performed to evaluate variations in the composition of oral bacteria between males with stage I and III periodontitis and identify the dominant bacteria of each group. Function prediction was obtained based on 16S rRNA sequencing data. The inhibitor of the predominant pathway for stage III periodontitis was used to investigate the role of the dominant bacteria in periodontitis in vivo and in vitro.ResultsChao1 index, Observed Species and Phylogenetic Diversity (PD) whole tree values were significantly higher in the stage III periodontitis group. β-diversity suggested that samples could be divided according to the stages of periodontitis. The dominant bacteria in stage III periodontitis were Prevotella, Prevotella_7, and Dialister, whereas that in stage I periodontitis was Cardiobacterium. KEGG analysis predicted that variations in the oral microbiome may be related to the NOD-like receptor signaling pathway. The inhibitor of this pathway, NOD-IN-1, decreased P. intermedia -induced Tnf-α mRNA expression and increased P. intermedia -induced Il-6 mRNA expression, consistent with the ELISA results. Immunohistochemistry confirmed the down-regulation of TNF-α and IL-6 expressions by NOD-IN-1 in P. intermedia–induced periodontitis.ConclusionThe composition of the oral bacteria in young males varied according to the stage of periodontitis. The species richness of oral microtia was greater in young males with stage III periodontitis than those with stage I periodontitis. Prevotella was the dominant bacteria in young males with stage III periodontitis, and inhibition of the NOD-like receptor signaling pathway can decrease the periodontal inflammation induced by P. intermedia

SPTAN1/Numb Axis Senses Cell Density To Restrain Cell Growth and Oncogenesis Through Hippo Signaling

The loss of contact inhibition is a key step during carcinogenesis. The Hippo-Yes-associated protein (Hippo/YAP) pathway is an important regulator of cell growth in a cell density-dependent manner. However, how Hippo signaling senses cell density in this context remains elusive. Here, we report that high cell density induced the phosphorylation of spectrin α chain, nonerythrocytic 1 (SPTAN1), a plasma membrane-stabilizing protein, to recruit NUMB endocytic adaptor protein isoforms 1 and 2 (NUMB1/2), which further sequestered microtubule affinity-regulating kinases (MARKs) in the plasma membrane and rendered them inaccessible for phosphorylation and inhibition of the Hippo kinases sterile 20-like kinases MST1 and MST2 (MST1/2). WW45 interaction with MST1/2 was thereby enhanced, resulting in the activation of Hippo signaling to block YAP activity for cell contact inhibition. Importantly, low cell density led to SPTAN1 dephosphorylation and NUMB cytoplasmic location, along with MST1/2 inhibition and, consequently, YAP activation. Moreover, double KO of NUMB and WW45 in the liver led to appreciable organ enlargement and rapid tumorigenesis. Interestingly, NUMB isoforms 3 and 4, which have a truncated phosphotyrosine-binding (PTB) domain and are thus unable to interact with phosphorylated SPTAN1 and activate MST1/2, were selectively upregulated in liver cancer, which correlated with YAP activation. We have thus revealed a SPTAN1/NUMB1/2 axis that acts as a cell density sensor to restrain cell growth and oncogenesis by coupling external cell-cell contact signals to intracellular Hippo signaling

Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays.

Spatially resolved transcriptomic technologies are promising tools to study complex biological processes such as mammalian embryogenesis. However, the imbalance between resolution, gene capture, and field of view of current methodologies precludes their systematic application to analyze relatively large and three-dimensional mid- and late-gestation embryos. Here, we combined DNA nanoball (DNB)-patterned arrays and in situ RNA capture to create spatial enhanced resolution omics-sequencing (Stereo-seq). We applied Stereo-seq to generate the mouse organogenesis spatiotemporal transcriptomic atlas (MOSTA), which maps with single-cell resolution and high sensitivity the kinetics and directionality of transcriptional variation during mouse organogenesis. We used this information to gain insight into the molecular basis of spatial cell heterogeneity and cell fate specification in developing tissues such as the dorsal midbrain. Our panoramic atlas will facilitate in-depth investigation of longstanding questions concerning normal and abnormal mammalian development.This work is part of the ‘‘SpatioTemporal Omics Consortium’’ (STOC) paper package. A list of STOC members is available at: http://sto-consortium.org. We would like to thank the MOTIC China Group, Rongqin Ke (Huaqiao University, Xiamen, China), Jiazuan Ni (Shenzhen University, Shenzhen, China), Wei Huang (Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China), and Jonathan S. Weissman (Whitehead Institute, Boston, USA) for their help. This work was supported by the grant of Top Ten Foundamental Research Institutes of Shenzhen, the Shenzhen Key Laboratory of Single-Cell Omics (ZDSYS20190902093613831), and the Guangdong Provincial Key Laboratory of Genome Read and Write (2017B030301011); Longqi Liu was supported by the National Natural Science Foundation of China (31900466) and Miguel A. Esteban’s laboratory at the Guangzhou Institutes of Biomedicine and Health by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA16030502), National Natural Science Foundation of China (92068106), and the Guangdong Basic and Applied Basic Research Foundation (2021B1515120075).S

Fetal Brain Tissue Annotation and Segmentation Challenge Results

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, brainstem, deep grey matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.Comment: Results from FeTA Challenge 2021, held at MICCAI; Manuscript submitte