Recent advances in microscopy techniques, especially in electron microscopy, are transforming biomedical studies by acquiring large quantities of high-precision 3D cell image stacks. To examine cell morphology and connectivity in organs such as the brain, scientists need to conduct cell segmentation, which extracts individual cell regions of different shapes and sizes from a 3D image. This is challenging due to the indistinct images often encountered in real biomedical research: in many cases, automatic segmentation methods inevitably contain numerous mistakes in the segmentation results, even when using advanced deep learning methods. To analyze 3D cell images effectively, a semi-automated software solution is needed that combines powerful deep learning techniques with the ability to perform post-processing, generate accurate segmentations, and incorporate manual corrections. To address this gap, we developed Seg2Link, which takes deep learning predictions as inputs and use watershed 2D + cross-slice linking to generate more accurate automatic segmentations than previous methods. Additionally, it provides various manual correction tools essential for correcting mistakes in 3D segmentation results. Moreover, our software has been optimized for efficiently processing large 3D images in diverse organisms. Thus, Seg2Link offers an practical solution for scientists to study cell morphology and connectivity in 3D image stacks. These large 3D images, particularly those obtained through electron microscopy, offer a powerful tool for studying the structure and connectivity of cells in organs, such as brains8. However, one of the necessary procedures, segmenting cells into individual ones, remains a challenging and time-consuming task10.Recent advances in microscopy techniques have enabled scientists to efficiently acquire large-scale and/or high-resolution 3D images with optical and electron microscopy12, which has spurred the development of new automatic segmentation methods17. The automatic cell segmentation usually involves two main steps. The first step entails the differentiation of individual cells from the background, commonly referred to as semantic segmentation in computer vision literature18. The second step involves the discrimination of multiple cells when they are closely attached to each other, known as instance segmentation18. Over the years, various deep learning-based techniques have been developed to address these challenges. Some approaches, such as U-Net14, focus on semantic segmentation. Although they require other instance segmentation techniques like watershed19 to complete the segmentation, they have the advantage of needing less user effort to prepare the training data compared to other deep learning methods for instance segmentation such as StarDist15, which aim to solve both semantic and instance segmentation problems using deep learning-based techniques.Traditionally, segmenting large 3D cell images requires time-consuming manual annotations20. Secondly, many real images, such as those obtained through electron microscopy, may exhibit slice displacement along the z-axis, making automatic alignment challenging, particularly when non-rigid displacement occurs. Such displacements can cause incorrect linking across slices, leading to erroneous segmentation results that require manual corrections .Despite the development of these advanced methods, cell segmentation remains a challenging task, and mistakes in segmentation, referring to the differences between automatic segmentation algorithms and human expert judgment, are often unavoidable in real images due to several reasons. Firstly, the segmentation program need to detect cell boundaries to separate closely attached cells, which is often challenging in real images. For instance, fluorescence microscopy images may contain cells attached to each other without clear boundaries . On the other hand, although cell boundaries could be visible in electron microscopy images, they may be absent in some regions . Moreover, due to the specific nature of 3D segmentation tasks, mistakes in a single slice can easily propagate to other slices, necessitating a substantial amount of manual correction by users . The complexity of cell shapes, such as those of neurons, further exacerbates the challenge of detecting cell boundaries. As a result, even advanced methods are prone to mistakes and require manual corrections based on experts’ knowledge and experience, as demonstrated by a previous study24.For the reasons stated above, we consider that automated segmentation methods are unlikely to reach perfection and will continue to require manual corrections to reconcile the differences between automatic and human judgments for the foreseeable future. Therefore, if we can develop a semi-automatic software program that combines automatic segmentation and computer-assisted manual correction while maximizing operational efficiency, it will substantially help scientists to quickly and accurately segment 3D images. The following features would be ideal in such a software program. (1) The ability to automatically generate instance segmentation results, which is required for post-processing the semantic segmentation result by techniques such as U-Net. (2) Efficient correction of mistakes in the instance segmentation results on a slice-by-slice basis, with the corrected results used to generate more accurate segmentation in surrounding slices, thereby reducing the amount of manual correction required. (3) Optimized procedures for manual corrections, as well as optimized computational and storage efficiency to reduce user operation time and hardware requirements. Previous software programs for assisting 3D cell segmentation either could not utilize the semantic segmentation results as input, generate less accurate instance segmentation results, or lack key functions essential for manual corrections of the segmentation mistakesx–y plane) + cross-slice linking  which is more accurate than the watershed 3D used by other software, or it can directly take instance segmentation results as inputs. Seg2Link can segment entire images as well as user-defined regions. Additionally, our software program allows users to efficiently perform computer-assisted corrections of segmentation results through techniques such as utilizing corrected segmentation results in adjacent slices, quick localization of cells, multiple-step undo/redo functionality, various optimized tools for inspecting and correcting mistakes, and more, which are often not available in existing software. We consider Seg2Link successfully integrates many of the key functions required by semi-automatic 3D cell segmentation and can help scientists in analyzing the cell morphology and connectivity in organs more efficiently.Here we developed Seg2Link, a semi-automatic segmentation software that can take semantic segmentation results (cellular/non-cellular predictions) as inputs, performs automatic instance segmentation using watershed 2D .We created a set of graphical user interfaces (GUI) using the napari library19 to the cellular/non-cellular predictions by a deep neural network and generates segmented cell regions in each slice  information in a cache , so that the software can search for a cell in a much smaller subregion, and the time required for localization is primarily determined by cell size rather than the image size. During our tests on a larger dataset than the demo dataset see “”, we ran26. We chose the watershed 2D + Link approach because it is superior to watershed 3D in following aspects: First, our results on the EM demo dataset show that 3D watershed can produce more boundary mistakes than 2D watershed + overlap linking , especially when processing large image stacks with complex cell shapes.When users have obtained the semantic segmentation results (cell/non-cell regions) and need to transform them into individual cells, one popular way is watershed 3D which is available in other softwareing Fig. . Second,ing Fig. , which f27  We use watershed 2D + cross-slice linking for the automatic instance segmentation , which is a desired feature available in some other software for analyzing huge datasets such as entire brainsApart from our excellent design for performing quick and easy segmentation/correction, the advantages of Seg2Link are also drawn from existing data processing tools developed by the scientific computing/image processing communities. These tools allowed us to write a concise program implementing both the upper-level visualization and the underlying computation. The recently released napari library, for example, includes a GUI for viewing and editing various types of image stacks. It also allows us to add new widgets and hotkeys to perform custom functions. NumPy, SciPy, and scikit-image are array/image processing libraries that allow us to write image processing functions. Dask, Python's big data processing library, enables our software to dynamically load files on disk, reducing memory usage.31, studying the cell morphology and spatial distribution of embryonic27 . The dataset contains raw images as well as segmentation results with proofreading, with an x–y plane resolution of 8 nm/pixel and steps of 40 nm between slices. In 12 of the 1200 slices , we transformed the provided segmentations to cell/non-cell regions as the ground truth for training a 2D U-Net model. The trained 2D U-Net was then used to predict cell/non-cell regions across all 1200 slices of the 3D image. These predictions and raw images (stored as 2D TIFF image sequence) were then imported into Seg2Link to perform the semi-automatic segmentation. We also used a larger portion (2048 × 2048 × 1200 voxels) of the same dataset to test the localization time with and without using bbox.To demonstrate the functions of our software, we used a portion (1024 × 1024 × 1200 voxels) of a 3D EM image dataset (minnie65_8 × 8 × 40) of the visual cortex in mouse brain, which is publicly available (27. It is a 3D image (Emb1_t501.tif) taken by an inverted widefield microscope and is publicly available in the image set BBBC050 from the Broad Bioimage Benchmark Collection33 at https://bbbc.broadinstitute.org/BBBC050. This dataset has the size of 112 × 114 × 51 voxels, with an x–y plane resolution of 0.8 µm/pixel and steps of 2.0 µm between slices. The second dataset includes cells of C. elegans at the L1 stage28, which is a 3D image taken by a confocal microscope and is publicly available at https://doi.org/10.5281/zenodo.5942574 (C18G1_2L1_1.tif). This dataset has the size of 1244 × 140 × 64 voxels, with an x–y plane resolution of 0.116 µm/pixel and steps of 0.122 µm between slices.In addition, we used two other datasets for demonstrating the segmentation results of optical cell images using our software. The first dataset includes cells of mouse embryoThe automatic segmentation part of the Seg2D + Link module processes each slice of the 3D image one by one Fig. A. The pr19 to segment the image in the current working slice into individual cells , the program calculates the proportion of each segmented cell falling within the ROI. Cells with this proportion less than a user-specified threshold  are deleted automatically . When the overlap exceeds the threshold, the program will link X and Y to form a single cell Fig. B.Following the automatic segmentation and linking of each slice, the user can make the four types of corrections: 1. Merge multiple cells into a single cell; 2. Delete one or more cells; 3. Divide a cell in the current working slice into multiple cells; 4. Divide a cell in the current working slice and relink the results to the previous slices . If users quit the software, they can also easily restart from the point of interruption (but the previous states for undo/redo will be cleared).Seg2Link makes use of the napari viewer to let users view and edit segmentation results  that stores the original 2D segmentation results from the 2D watershed, and a label list (a 1D list) that originally stores labels in the 2D segmentation result and will be updated when labels change due to link/merge/delete, etc. . With this design, the program only needs to modify/cache/store a much smaller data structure: a 2D array + a group of 1D lists, rather than the large 3D array .The segmentation results from module 1 can be saved as a 3D array (npy format) and imported into module 2 to correct any remaining mistakes. The 3D segmentation results (2D TIFF image sequence) exported from other software can also be imported into module 2 for correction .In addition to the division and division-relink functions which are applied to a 2D subregion of a cell in a specific slice, we add a 3D division function in module 2 for dividing a cell in 3D space. We also add a function to insert a new cell that was not detected by deep learning, requiring users to manually paint the cell region with an automatically assigned new label .One critical issue about using a 3D array as data structure is that searching for a cell's location takes a long time due to the large search space. Seg2Link solved this problem by pre-calculating and storing the bounding boxes (bbox) of all cells in memory . Following each correction operation, the bbox of relevant cells are updated. In this way, the program can search for a cell in a much smaller sub-region and with much less time.Another issue is that caching the entire 3D array is very space-intensive, making the undo/redo functions impractical. Our program solved this problem by caching changes only in the sub-region with modifications, which requires much less memory .Supplementary Information.


We performed quantitative proteomics on 60 human-derived breast cancer cell line models to a depth of ~13,000 proteins. The resulting high-throughput datasets were assessed for quality and reproducibility. We used the datasets to identify and characterize the subtypes of breast cancer and showed that they conform to known transcriptional subtypes, revealing that molecular subtypes are preserved even in under-sampled protein feature sets. All datasets are freely available as public resources on the LINCS portal. We anticipate that these datasets, either in isolation or in combination with complimentary measurements such as genomics, transcriptomics and phosphoproteomics, can be mined for the purpose of predicting drug response, informing cell line specific context in models of signalling pathways, and identifying markers of sensitivity or resistance to therapeutics. It is therefore of considerable interest to identify recurrent changes in TNBCs that might be targeted to treat disease. Breast cancers can also be classified into molecular subtypes based on gene expression signatures. These include the luminal A/B and basal designations that generally encompass HR positive and TNBC disease respectively, with HER2 enriched cancers comprising a separate molecular subtype4.Targeted therapy relies on the identification of actionable changes in signal transduction, proliferation or cell death pathways that are drivers of transformed states. In some cases, these changes are associated with a recurrent mutation or overexpression of an oncogene. In other cases, the causes of differences in drug sensitivity are less well understood. Some breast cancer subtypes are particularly responsive to targeted therapy owing to high expression of one or more of the estrogen (ER), progesterone (PR), or HER2 receptors. Moreover, the presence or absence of these receptors, which is most commonly measured by immunohistochemistry (IHC), defines clinical breast cancer subtype and mode of first line therapy (expression of ER and/or PR defines the hormone receptor (HR) positive subtype and over-expression of HER2/ERBB2 defines the HER2 positive subtype). The third breast cancer subtype, triple negative breast cancer (TNBC), lacks high levels of ER, PR and HER2 expression, is genetically heterogeneous and is the least effectively treated6. However, transcript levels do not necessarily correlate with the abundance of proteins, which are the ultimate targets of small molecule and antibody therapies7. Moreover, in some tumor types, the effects of copy number alterations extend to mRNA abundance without necessarily propagating to changes in protein abundance8. It is therefore valuable to measure the levels of proteins across panels of cell lines to identify changes in cell state. Of particular interest are changes that might individually or in combination determine sensitivity to new or existing drugs for the treatment of breast cancer. It has been shown that computational models of drug sensitivity that are trained using protein expression data can complement or even outperform models trained on transcript or genomic data alone11. Thus, a standardized dataset on protein expression levels in breast cancer cell lines of all three clinical subtypes is expected to constitute a valuable resource for drug discovery and development of predictive biomarkers.Multiple studies have been performed in which panels of TNBC cell lines are subjected to transcript profiling to identify differences among themIn this data descriptor we describe systematic profiling of 60 widely used breast cancer cell lines using Tandem Mass Tag (TMT) liquid chromatography mass spectrometry (LC/MS). TMT LC/MS is a method for labelling multiple samples (up to 11 in the current work) with mass tags and then analyzing them in parallel on the mass spectrometer, thereby enabling direct comparison of protein levels. In this data descriptor we provide a technical summary of the TMT-based mass spectrometry approach and the resulting data. Quality metrics used to asses both technical and biological validity are explained and we highlight the use of bridge samples to enable normalization and comparison of samples collected in the face of technical and methodological advances. We discuss how the resulting data can be leveraged to characterize preclinical (cell line) models of breast cancer, generate testable hypotheses of resistance to therapy and discover novel biological insight.12.All cell lines used in this study were of human female origin and derived from breast cancers except the 184A1, 184B5, MCF 10A, MCF12A and HME1 cell lines which were derived from non-malignant human breast epithelia. Cell lines were maintained, free of mycoplasma, in their recommended growth conditions  followed by centrifugation at 300 g for 5 minutes at 4 °C. The supernatant was discarded, and pellets were snap frozen in liquid nitrogen and stored at −80 °C.Cells grown in their recommended growth medium to ~60% confluence were rinsed twice with phosphate-buffered saline (PBS) and then gently scraped from 15 cm dishes in PBS supplemented with protease and phosphatase inhibitors  or 2 M (sets 5–8) final urea concentration and digestion was then performed by overnight incubation at room temperature in the presence of Lys-C protease  at an enzyme-to-substrate ratio of 1:75. Following further dilution of the sample with 200 mM EPPS to a final urea concentration of 1.6 M (sets 1–4) or 0.5 M (sets 5–8), digestion was continued by incubation of the sample at 37 °C for 6 hours with trypsin  at an enzyme to substrate ratio of 1:75. Aliquots corresponding to 65 µg per sample (sets 1–4) or 150 µg per sample (sets 5–8) were withdrawn for TMT labelling.Cell pellets were solubilized in lysis buffer  supplemented with Halt™ Protease and Phosphatase Inhibitor Single-Use Cocktail, EDTA Free  with a hand-held tissue homogenizer. Disulfide reduction was performed by adding dithiothreitol (DTT) to a final concentration of 5 mM and heating to 37 °C for 1 hour, followed by alkylation of cysteine residues with iodoacetamide at a final concentration of 15 mM and incubation at room temperature in the dark for 30 minutes. Protein concentration was determined using a Micro BCA™ Protein Assay Kit  following the manufacturer’s protocol. Detergent was removed by methanol/chloroform protein precipitation as follows: ice cold methanol (3 parts lysis buffer volume), chloroform (2 parts lysis buffer volume) and water (2.5 parts lysis buffer volume) were added sequentially with vortexing after each addition followed by centrifugation at 4000 × g for 10 min. The top layer was aspirated while taking care not to disrupt the interface. Ice cold methanol (3 parts lysis buffer) was added, the samples were vortexed, centrifuged , and the supernatant aspirated leaving behind the protein pellet; this methanol wash procedure was repeated a total of three times14 (described below). The peptides were then dried and resuspended in 3% acetonitrile, 5% formic acid (FA) to a final concentration of ~2 µg/µl. The missed cleavage rate was measured by LC-MS/MS to evaluate the quality of the digest; a result under 15% of potential cleavage sites remaining uncleaved was deemed sufficient to proceed with labelling.Aliquots equivalent to 5–10 µg from two samples were pooled, desalted and peptides purified by reverse phase chromatography on stage tipsEqual amounts of protein were removed from each sample and labelled using a TMT10plex or TMT11plex Mass Tag Labelling Kit  (see Supplementary Table 16. MS scans were performed in the Orbitrap over a scan range of 400–1400 m/z with dynamic exclusion. Rapid rate (sets 1–4) and Turbo rate (sets 5–8) scans were performed in the Ion Trap with a collision energy of 35% and maximum injection times of 120 ms (sets 1–4) and 200 ms, (sets 5–8) respectively. TMT quantification was performed using SPS-MS3 in the Orbitrap with a scan range of 100–1000 m/z and an HCD collision energy of 55%. Orbitrap resolution was 50,000 (dimensionless units) with maximum injection times of 120 ms (sets 1–4) and 450 ms (sets 5–8), respectively. MS isolation windows were varied depending on the charge state. Additional reruns of fractions were performed for the later sets to achieve increased depth. Details are provided in Supplementary Table The first half of the dataset (sets 1–4) was recorded after peptide separation on 100 µm columns packed with 1.8 µm C18 beads with a pore size of 12 nm (Sepax Technologies Inc.). The second half of the data (sets 5–8) was obtained after peptide separation on 75 µm columns packed with 2.6 µm Accucore beads (Thermo Fisher Scientific). Peptides were injected onto 30–40 cm, 100 and 75 µm  columns, respectively, and separated using an EASY-nLC 1200 HPLC (ThermoFisher Scientific). The flow rate was 450 nl/min for the 100 µm columns and 300 nl/min for the 75 µm columns with a gradient of 6–28%B over 170 minutes with Buffer A comprising 3% acetonitrile, 0.4% FA and Buffer B comprising 100% acetonitrile, 0.4% FA for the 100 µm columns and 5–35%B over 240 minutes with Buffer A comprising 0.125% FA and Buffer B comprising 95% acetonitrile, 0.125% FA for the 75 µm columns. The columns were heated to 60 °C using a column heater (constructed in-house). Samples from the HPLC were injected into an Orbitrap Fusion Lumos Tribrid MS  using a multi-notch MS3 methodcorr, ΔCn, and precursor mass error) were computed by SEQUEST for target and decoy hits. Linear discriminant analysis that combines the 3 SEQUEST identification score parameters into an optimal discriminant score was performed18. For each set, the false discovery rate (FDR) was computed as twice the number of reverse peptides identified divided by the total number of peptide identifications above any given discriminant score threshold19. Peptides were filtered to achieve an FDR <1%. During peptide assignment for all data, oxidized methionine (+15.9949 Da) was searched dynamically. All peptide searches considered TMT modification (+229.1629 Da) on N-termini and lysine residues as static modifications. For each set, the FDR for protein identification was set to <1%17, and shared peptides were then collapsed into proteins using rules of parsimony, i.e if a peptide could be mapped to multiple proteins, it was assigned only to the largest protein17. Each peptide was assigned only to one protein. Peptides with a total TMT value of >200 and an isolation specificity of >0.7 were included in the final dataset.Mass spectrometric data (Thermo “.RAW” files) were converted to mzXML format, to correct monoisotopic m/z measurements, and to perform a post-search calibration. Peptide spectrum matches were assigned with SEQUEST (v.28 (rev. 12), (c) 1998–2007 Molecular Biotechnology, Univ. of Washington, J.Eng/S.Morgan/J.Yates licensed to Thermo Fisher Scientific Inc.) based software. The quality of peptide identifications by SEQUEST was determined with a target-decoy approach, where each peptide spectra was searched against a composite database of size-sorted forward and reverse protein sequences of the human proteome (Uniprot 02/2014) that also contained common contaminant proteins. For each peptide, identification scores , Level 2 data on peptide intensities normalized within and across sets (Synapse ID: syn32672825) and Level 3 data (Synapse ID: syn32672858) in which protein level data is derived from the mean peptide intensities23.Proteome datasets are available on Figshare: 10.6084/m9.figshare.c.6443633.v2Quality control checks for mass spectrometry were incorporated at multiple points in the workflow. To test for efficient digestion of samples, defined as <15% of potential proteolysis sites uncleaved, a “digest check” was performed using LC-MS/MS as described in the methods section. TMT labelling efficiency aims for modification of >95% of available sites and was determined by LC-MS analysis via dynamic searches for N-terminal peptide modification by TMT. A “ratio check” was also performed using LC-MS3 to determine relative amounts of labelled peptides in each of the multiplexed samples, as described in the methods section. The purpose of the ratio check is to ensure equal amounts of peptide across all samples in a set are pooled in the sample run through the mass spectrometer.73 samples  distributed across three breast cancer clinical subtypes Fig.  were ran24, we measured a total of ~13,000 unique proteins in our dataset. 7197 proteins were detected in all sets while the remaining proteins were observed to varying degrees in different sets  in set kAcross set normalization at the peptide level: Each within-set normalized peptide, j and set k was normalized by multiplying its intensity by the ratio of the summed intensities of bridge sample in the reference set (jbkr) and bridge sample in set k (jbk)yl in sample j and set k was calculated by summing across all normalized peptide intensities, reported for protein yl in sample j and set k and dividing by the number of peptides nlk reported for protein yl in set kDerivation of protein level data: Quantity of any protein, k was scaled to a value between 0 to 100 by dividing the normalized intensity of each peptide or protein xijk  was performed prior to data normalization and revealed a significant degree of clustering by batch (sets 1–4 versus sets 5–8). This was true despite the high overlap in proteins detected. We could identify two reasons for this batch effect: advances in instrumentation and analytical methods meant that later batches exhibited better signal to noise as assessed by an increase in mean intensity per protein. Samples in the second batch had a greater number of quantifiable total peptides per protein Fig. . This diets Fig. . In shotets Fig. , intensiAfter normalization, PCA-based clustering of the 74 samples at the peptide and protein levels showed that data for each cell line clustered by transcriptional subtype Fig. , suggest25 (these data levels are described in detail in the accompanying overview manuscript). Level 0 data for the current study are available for download from the Pride repository (PXD026581)21. Level 1 (available on Synapse: syn3267268423) data were generated using software provided by the Gygi Laboratory at HMS and comprise peptide level estimates. The peptide intensity estimates in Level 1 were normalized using bridge samples to make cross-set and cross batch comparisons possible, followed by calculation of protein level estimates . Level 1 datasets can also be generated by users from the Level 0 raw data using MaxQuant26. While Level 2 data can either be generated from Level 1 data as described using Eqs. , Level 1 (relative peptide level intensities reported for each set), Level 2 , Level 3  and Level 4 (signatures and markers of response)28. To assess if this is a reasonable assumption to make for this dataset, we compared the correlation across the 5 non-malignant MCF 10A replicates and the non-malignant hME1 cell line model using either normalized protein or peptide level measurements found in common across all 8 sets . Using the relative abundance for each of the 7197 proteins measured in all cell lines, we built univariate linear models to predict response (area over the GR curve) to palbociclib. The model included receptor status as a covariate to account for subtype specific differences in protein expression   and was positively correlated with increased sensitivity. In contrast, expression of CBX2 was correlated with resistance to palbociclib . Overexpression of CBX2, a protein involved in DNA damage repair and chromatin homeostasis34, has been associated with upregulation of genes involved in cell cycle progression and worse 5-year survival in breast cancer patients35. The association of CBX2 expression with resistance to palbociclib in cell lines provides a rationale for considering it as a potential biomarker in humans and a possible therapeutic target to overcome resistance to CDK4/6 inhibitors. This preliminary analysis suggests that baseline protein expression in untreated cell line models can be used to generate testable hypotheses about factors that influence drug sensitivity and resistance.Genomic and transcriptomic data have frequently been used to predict drug response and identify potential predictors or determinants of drug sensitivitySupplementary Table 1Supplementary Table 2Supplementary Table 3


This technical innovation assesses the adaptability of some common automated segmentation tools on abnormal pediatric magnetic resonance (MR) brain scans. We categorized 35 MR scans by pathologic features: (1) “normal”; (2) “atrophy”; (3) “cavity”; (4) “other.” The following three tools, (1) Computational Anatomy Toolbox version 12 (CAT12); (2) Statistical Parametic Mapping version 12 (SPM12); and (3) MRTool, were tested on each scan—with default and adjusted settings. Success was determined by radiologist consensus on the surface accuracy. Automated segmentation failed in scans demonstrating severe surface brain pathology. Segmentation of the “cavity” group was ineffective, with success rates of 23.1% (CAT12), 69.2% (SPM12) and 46.2% (MRTool), even with refined settings and manual edits. Further investigation is required to improve this workflow and automated segmentation methodology for complex surface pathology. Accurately printed 3-dimensional (D) models can be used to convey morphological information of the brain and especially the brain surface to non-radiologists , 2. To dThis study examined 35 abnormal MR brain scans in children with cerebral palsy due to prior perinatal hypoxic ischemic injury, who underwent delayed MRI. Some patterns of hypoxic ischemic injury only involve portions of the cortex, with related increased complexity of a 3-D mapping workflow to convert MR images into 3-D surface reconstructions. This has previously been documented in adults . The utiThe included image sets were distributed into four categories based on imaging-identified structural abnormalities: “normal”, demonstrating no visible structural brain abnormalities Fig. ; “atrophThe process, from scan to 3-D model, has four steps: (1) segmentation; (2) surface meshing; (3) standard tessellation language (STL) file (.stl) preparation; 4) 3-D printing. Digital Imaging and Communications in Medicine (DICOM) image sets of MRI brain scans are acquired and converted to Neuroimaging Informatics Technology Initiative (NIfTI) files—3-D arrays of the original MR scan intensities organized into 1 mm × 1 mm × 1 mm 3-D, voxels. The NIfTI files are segmented; the skull and cerebrospinal fluid are stripped to leave only brain tissue for 3-D modeling  “mesh” format and the internal volume is effectively discarded. Each component of the mesh is defined by three coordinates and outward-facing normal vectors. There were three methods used to generate the STL surface mesh files; all were accessed via the MatLab computing environment: (1) Iso2surf, (2) Isosurface, (3) MRIcroS.Meshing errors, though usual, require repair because surface “holes” cause printing errors. The rate of meshing errors acts as a proxy to gauge effectiveness of a segmentation method. Metrics used to analyze the accuracy of STL surface generation include volume of the generated STL file compared to the volume of the corresponding NIfTI file: (1) surface area to volume ratio for each volume generated was compared among the segmentation methods for a given scan and (2) visual assessment comparing STL files with a 3-D representation of the source NIfTI file. For scans not successfully segmented with default settings, the effects of specific default settings were inspected within each method. Manual modification of settings was performed to improve segmentation outcomes as below:Strength of Inhomogeneity Correction: Controls the level of bias regularization and bias Gaussian smoothness Full Width at Half Maximum (FWHM) cutoff and measures the strength of inhomogeneity bias correction applied prior to segmentation.Affine Pre-processing (APP): Additional pre-processing bias correction aiming to reduce failure in deviating anatomy.Strength of Local Adaptive Segmentation (LAS): Without bias, tissue intensity is variable. LAS is a region growing segmentation approach designed to overcome tissue variances and increase correct segmentation [entation .Strength of Skull Stripping: Attempts to isolate the brain by removing the skull before processing, leading to more accurate segmentation.Strength of Final Clean Up: Attempts to remove meninges and correct for the partial volume effect.For CAT12:Bias regularization: Determines the extent to which a non-uniformity correction field should be modelled by the algorithm. Lighter regularization results in a stronger correction field being modelled (hence stronger bias correction).Full Half Width at Maximum Cutoff: The non-uniformity intensity model used is Gaussian; this option determines the shape of the width of the curve. To model smooth low frequency fields a large cutoff should be used and vice versa.Tissue Probability Map: Template which represents the probability of a particular tissue type appearing at a particular spatial location—from previous  patients.For SPM12 and MRTool:The described workflow was applied to the 35 image sets with three segmentation methods applied to each. Initially, 22 models were successfully segmented across the three automated methods. This increased to 31 after various optimizations in segmentation. There were 4 MR brain scans, belonging to the “cavity” category, that failed all segmentation methods. The results of manual optimization and modification of default segmentation settings with recommendations (relative to scan class) are outlined in Tables Automated segmentation via CAT12, SPM12 and MRTool was almost universally successful in the “normal” and “other” classes of MRI brain scans. With default settings, there were success rates of 91.7%, 91.7% and 100%, for CAT12, SPM12 and MRTool, respectively for normal brains, and a 100% success rate for all methods for the “other” class. With respect to the “atrophy” and “cavity” classes, segmentation was less successful. In the “atrophic” group, CAT12 was successful in 60%, SPM12 in 100% and MRTool in 80%. In the “cavity” group, CAT12 was successful in 23.1%, SPM12 in 69.2% and MRTool in 46.2%. Unsuccessful segmentation is demonstrated in Fig. Manual segmentation of brain tissue by selecting regions of interest and setting tissue intensity thresholds is ineffective and time intensive . On the VBM techniques have been effective in detecting gray and white matter changes in adults suffering from motor neuron disease and in detecting levels of atrophic tissue in the brain . CompariAll three methods used for segmentation  adequately segmented nearly all the scans in the “normal” and “other” categories, albeit with different degrees of quality. The CAT12 method had a relatively low success rate with “cavity” and “atrophy” category images. Both SPM12 and MRTool methods were more successful with the “atrophy” and “cavity” categories. The successful segmentation rate was increased in both these methods by lowering the bias regularization setting (increasing bias correction). The segmentation volumes were also improved in both by using the pediatric TPM rather than the provided TPM . The “atThe efficiency benefits of automated segmentation were lost because various settings for each method required manual adjustment and even this did not guarantee successful segmentation. The difficulties in segmenting the “cavity” class of patients are highlighted in Fig. When proceeding through the workflow to produce a 3-D model of the surface from pediatric brain MRI, investigators should consider the feasibility of segmentation, weighing this up against the ultimate utility of an accurate 3-D model. We have shown that segmentation is unsuccessful when using recommended segmentation methods in default settings for MRI scans in children with atrophy of the cerebral gyri or cavities close to the surface of the brain.


The resulting domain pattern with aligned staggered magnetization is long term stable enabling memory applications. We achieve this switching with low heating of ≈20 K, which is promising regarding fast and efficient devices without the need for thermal activation. Current polarity dependent reversible domain wall motion demonstrates a Néel spin-orbit torque acting on the domain walls.Current pulse driven Néel vector rotation in metallic antiferromagnets is one of the most promising concepts in antiferromagnetic spintronics. We show microscopically that the Néel vector of epitaxial thin films of the prototypical compound Mn 2Au.Antiferromagnets have an inbuilt resilience to external magnetic fields and intrinsically fast dynamics, properties that have garnered interest in the hope that they could be used for antiferromagnet memories. Central to this are Neel spin-orbit torques, which can switch the individual sublattices of the antiferromagnet. Here, Reimers et al demonstrate complete and reversible current induced switching of the Neel vector in Mn This approach takes advantage of the intrinsically fast THz dynamics of AFMs5 and their stability against external magnetic fields (see Ref. 6). The interest in such concepts has grown strongly with the prediction of a so called Néel spin-orbit torque (NSOT) in metallic collinear AFMs with a specific symmetry, which is expected to rotate the spins of the AFM sublattices in a perpendicular orientation with respect to a driving bulk current7.Novel storage concepts in spintronics based on antiferromagnets (AFMs) propose to encode information by the direction of the alignment of the staggered magnetization or Néel vector2Au, current pulse induced reversible resistance modifications were reported as evidence for NSOT driven Néel vector rotation13. However, it later became clear that alternative mechanisms such as current pulse induced heat effects15, electromigration16, and rapid quenching induced structural and magnetic modifications17 can result in resistance modifications similar to the ones reported as well. Additionally, external strain supported Néel vector manipulation in Mn2Au was demonstrated19. Thus, microscopic investigations showing the intended current driven alignment of the staggered magnetization directly are essential. Up to now, for CuMnAs as well as for Mn2Au, only minor current induced modifications were observed, i.e., only a small fraction of the antiferromagnetic domains switched with limited reversibility and stability20. This is neither sufficient regarding the identification of the switching mechanism as local inhomogeneities are involved nor does it demonstrate the applicability of current induced Néel vector switching for memory devices.Correspondingly, within the first generation of experiments on both known compounds with the required structure and AFM ordering, CuMnAs and Mn21. This recently attracted renewed interest, as in NiO/Pt bilayers thermomagnetoelastic coupling effects were observed, which act in a very similar way as spin-orbit torques on the Néel vector22. Thus, the experimental demonstration of a current induced NSOT is important.Furthermore, microscopic imaging provides direct insights into the current driven mechanisms of Néel vector reorientation2Au(001) thin films patterned in cross-structures, which is compatible with spintronics applications such as magnetic random access memory (MRAM). A current polarity dependence demonstrates an NSOT acting on AFM domain walls. Studying different pulse lengths, we show that in contrast to related experiments on Mn2Au12, for our epitaxial thin films thermal activation is not necessary for switching, which is important for fast and energy efficient memory applications regarding potential ultrafast applications.Here we show current pulse induced complete, remanent, and reversible Néel vector switching of Mn2Au is a metallic antiferromagnet with a high Néel temperature above 1000 K23. The compound has a tetragonal crystal structure with two equivalent 〈110〉 easy axes in the (001)-plane and a strong out-of-plane magnetic anisotropy.Mn2Au(001) thin films with a thickness of 45 nm grown on an epitaxial double buffer layer of 13 nm of Ta(001) on 20 nm of Mo(001) on MgO(001) substrates  thin film in a cross structure oriented parallel to the easy 〈110〉 directions, which allows to send current pulses both parallel and perpendicular to the axis along which the Néel vector is aligned. We obtain a repeatable complete switching of the Néel vector orientation in the central area (10 μm × 10 μm) of the cross by applying current pulses alternating between the two orthogonal directions. Figure ∘ reorientation of the Néel vector shows up as alternating dark and white contrast of the central area.The largest NSOT is expected for the current directions parallel and antiparallel to the Néel vectora Mn2Au00 thin fil12, we compare current pulse trains with different numbers and lengths, ranging from a train of 100 pulses with a lengths of 1 ms down to a single 10 μs pulse. The panels a and b of Fig. c and d show very similar switching obtained after a single bipolar current pulse with a length of 10 μs only. Although only a single pulse instead of 100 pulses was used and the pulse length was 100 times shorter, the required current densities to obtain complete Néel vector rotation are very similar with J1ms ≈ 2.6 × 1011 A/m2 and Jμs10 ≈ 3.0 × 1011 A/m2 . These current densities and pulse lengths result in significantly different temperature increases of the Mn2Au thin film due to ohmic heating with ΔT(1 ms) ≈ 70 K, ΔT(100 μs) ≈ 45 K, and ΔT(10 μs) ≈ 20 K, as determined from ex-situ reference resistance measurements during application of current pulses  thin film26 and shorter current pulses (20 μs) very similar results were obtained, showing the robustness of the switching properties.The left panel of Fig. 24. Current pulses were applied along a hard [100] direction of a Mn2Au(001) thin film using a single patterned stripe aligned along this direction. In this geometry, no net alignment of the Néel vector was obtained in the central region of the patterned stripe. However, we observed a partial current polarity dependent reversible reorientation. Some of these regions which switch for positive current polarity from vertical to horizontal alignment are indicated by the red circles in Fig. f show regions, in which the current was flowing preferentially along an easy 〈110〉-direction resulting in a local alignment of the Néel vector corresponding to the behavior discussed above  is consistent with the larger longitudinal resistance in Fig. Ipulse1 (blue data points), if the Néel vector is aligned perpendicular to the current direction as expected for an NSOT acting.We obtained a maximum ΔFinally, consistent with the X-PEEM measurements, the electrical signal shows no sign of decay as shown in the 2Au(001) thin films are very suitable as the current pulse induced AFM domain configurations perfectly fulfill this requirement. We were able to demonstrate by XMLD-PEEM that even four months after the current pulse Néel vector alignment shown in Fig. For potential spintronics applications such as antiferromagnetic random access memory (MRAM), long term stability of the Néel vector aligned states is required. In this framework our Mn28, need to be considered. Furthermore, our small electrical read-out signals are clearly distinguishable from the current pulse induced large resistance modifications previously obtained investigating various non-NSOT related metals as mentioned in the introduction.For spintronics, the size of the electrical read-out signal associated with Néel vector reorientation is of major importance. Our work confirms that the maximum obtainable signal from AMR is relatively small so that in antiferromagnetic spintronics other read-out mechanisms, for example via an adjacent strongly exchange-coupled ferromagnetic layer as discussed in reference24. Fully consistent with this prediction, we observe a current induced Néel vector orientation perpendicular to the pulse direction, if the current flows parallel to an easy 〈110〉-axis of Mn2Au  thin films29 is the in-plane magnetocrystalline anisotropy ( ≈1.8 μeV per formula unit for Mn2Au(001)28). In this framework a key role of thermal activation was previously reported based on investigations of granular Mn2Au thin films12. However, as shown e. g. by the transmission electron microscopy image in the 2Au(001) thin films are highly epitaxial without morphological features on the typical length scale of the AFM domains, so that independent switching of morphological grains can be excluded. This strongly suggests that the reorientation occurs via domain wall motion.In contrast to domain wall pinning, the potential barrier for Néel vector reorientation which is typically considered in theoretical workIndependent of the physical origin of the potential barriers to be overcome, it is important for spintronics applications that the current induced Néel vector reorientation does not depend on slow thermal activation processes. Here, the required current density for full Néel vector reorientation using a single 10 μs current pulse was only ≈10% larger than required for a pulse train of 100 current pulses with 1 ms pulse length each. This indicates that the current pulse induced force is at least of the same order of magnitude as opposing forces e.g., due to domain wall pinning.2Au(001) thin films, are finally able to fulfill one of the major early promises of antiferromagnetic spintronics: it is possible to write long term stable information by single current pulses into macroscopic areas of device structures. Regarding the switching mechanism, while thermomagnetoelastic contributions might be present, we identify a Néel spin-orbit torque acting on the domain walls leading to reversible motion. Furthermore, we have shown that thermal activation processes are not essential for current induced Néel vector reorientation, thereby enabling fast and energy efficient switching. Thus, epitaxial Mn2Au(001) thin films fulfill all requirements for memory applications regarding the writing and storage of data using a single antiferromagnetic layer.We have shown that metallic antiferromagnets, specifically epitaxial Mn2Au(001)(45 nm) thin films grown epitaxially on Ta(001)(13 nm)/Mo(001)(20 nm) double buffer layers on MgO(100) substrates. All layers were deposited by magnetron sputtering by the process described in detail in ref. 28. The samples were capped with 2 nm of polycrystalline SiNx to protect them from oxidation. Optical lithography and ion beam etching were used to pattern the films into the cross and stripe structures shown in this manuscript.All experimental data shown in this manuscript were obtained investigating Mn2,3 absorption edge at the PEEM endstations at beamline MAXPEEM at MAX IV, and beamline I06 at Diamond Light Source. The XMLD effect at the Mn L2,3 absorption edge in Mn2Au was established in previous work. For x-ray polarisation along a Mn2Au 〈110〉 direction, the Mn L2,3 XMLD spectrum shows a minimum and a maximum located at the absorption edge Emax and at 0.8 eV below the edge. At MAXPEEM, the x-ray beam has normal incidence at the sample surface. The XMLD-PEEM images obtained at this beamline are the asymmetry images of images taken with two orthogonal x-ray polarisations along the 〈110〉 directions and energy of hν = Emax-0.8 eV, as this provides maximum contrast and minimum sensitivity to morphological features. At I06, the x-ray beam is incident at a grazing angle of 16∘. XMLD-PEEM images measured there are the asymmetry of images with photon energies of hν = Emax and hν = Emax – 0.8 eV for fixed in-plane polarisation along a 〈110〉 direction.Antiferromagnetic domain imaging was performed by combining photoemission electron microscopy with x-ray magnetic linear dichroism (XMLD-PEEM) at the Mn LIn-situ electrical manipulation was performed using the pulse functions of Keithley2601B-PULSE (at MAX IV) and Keithley 2461 (at Diamond) sourcemeters, integrated into the X-PEEM setup.The ex-situ resistance measurements . The maximum resistance obtained at the end of the current pulses with different lengths and amplitudes is shown in the R(T) obtained from a low current measurement in a cryostat, we can deduce the current pulse induced temperature raise. These measurements were performed outside the PEEM at ambient conditions using the same Keithley 2601B-PULSE Source Meter as for pulsing in the PEEM. The time dependent sample resistance during the current pulses was measured with an oscilloscope and using the SENSE inputs of the Source Meter obtaining consistent results.For the quantification of the current pulse induced sample heating, we used a lithographic pattern, which additional to the cross shown in Fig. Supplemantary InformationPeer Review File


Controversy exists as to the role of the amyloid-β (Aβ) peptide in the pathophysiology of Alzheimer’s disease (AD).To clarify the effect of age on Aβ deposition in sporadic AD by exploring the degree of amyloid burden in patients with sporadic young onset AD (YOAD).APOE genotyping between groups was calculated with Fisher’s Exact Test.Patients were diagnosed with YOAD with dementia starting before the age of 65 years . A cross-sectional analysis of amyloid binding using positron emission tomography (PET) imaging was performed using the C-Pittsburgh Compound B (PiB). The global standardized uptake value ratios (gSUVR) were examined using the Wilcoxon two-sample test, as were the cognitive scores between disease and healthy control populations. Differences in PiB retention in different anatomical areas were compared using the Kruskal-Wallis test. The contrast in APOE ɛ4 allele. Men had gSUVR = 2.37±0.54, with 80% having at least one APOE ɛ4 allele. The gSUVRs were significantly higher than the control populations for men and women and had significantly greater frequency of APOE ɛ4. Men and women analyzed together had significantly greater amyloid burden and APOE ɛ4 allele frequencies than controls, but no differences existed between them in gSUVR nor in the anatomical distribution of amyloid uptake.Women had a median gSUVR = 2.68±0.73 and 73% had at least one APOE ɛ4 alleles. Our findings suggest that the Aβ peptide is operational in young onset dementia and driven by the APOE ɛ4 allele.Men and women with YOAD have greater amyloid uptake than controls and have more  No preparation or testing was required for the patient prior to injection. The camera clock and dose calibrator were synchronized for precise timing. For accurate acquisition of scans, movement of patients during and between emission and transmission of scans was avoided. Preliminary emission-only scout view was performed to verify correct patient position. A 6-min transmission scan followed. The emission scan results were obtained in list mode and reconstructed into summed static and dynamic 6×5-min series [n < 30 in both groups), only those p values corrected for multiple comparisons are presented. Comparisons in PiB retention in different anatomical regions between male, female, and control populations were analyzed using the Kruskal-Wallis test. The difference in APOE genotyping by disease group was examined by Fisher’s exact test.An Allegro GSP whole body PET camera was used. The standard dose of 370 MBq (10 mCi) n series . ComputaAPOE ɛ4 allele (72.7%). APOE ɛ4 allele. Men and women with YOAD had a significantly higher amyloid burden than disease and healthy controls (p = 0.26–0.74). There were no significant differences in PiB retention in different brain regions . The frequency of APOE ɛ4 alleles for men and women was greater than the control group (controls . There wcontrols  (p = 0.2 regions . There wol group .p = 0.0003).There were no significant cognitive testing differences between both sexes with YOAD and the disease controls, but there were between YOAD and the healthy controls . The amyAPOE ɛ4 allele, without a major difference between men and women.We have found that amyloid uptake in the brains of people with YOAD is increased in comparison with the control groups. There were no differences between men and women with YOAD, both revealing elevated amyloid binding in contrast to controls. There was no difference in the amount of the amyloid binding between men and women and no dissimilarity in the extent of cognitive dysfunction. Both sexes showed a high prevalence of an APOE ɛ4 alleles. Other investigations have conveyed that the amyloid deposits in the brains of aging people such that by the age of 70 years 40–50% of cognitively normal people have amyloid accumulation, which increases with each decade [APOE ɛ4 is involved [APOE ɛ4 alleles being a useful diagnostic laboratory confirmation of the diseases. The novelty and added value of our study, in comparison to previous studies, is the emphasis on YOAD only in patients without evidence of vascular or other pathologies.In our previous studies we have found that YOAD has differences between old onset disease in relation to cerebrovascular risk factors and ethnicity providing support for the notion that YOAD may have a divergent pathophysiological origin . Our resh decade , limitinh decade . Additioh decade . The stuinvolved . FurtherWhether YOAD is truly divergent from late onset AD requires further investigation as some studies suggest differences , but othAPOE ɛ4 alleles, approximately 20% [in vivo represent part of the definitive pathophysiological pathway of AD, and not an epiphenomenon of aging. Our discovery redefines amyloid as center-stage of the pathological and chemical reactions resulting in AD and affirms the amyloid hypothesis [p = 0.010–0.031, with the high dose group achieving secondary endpoints, refuting the earlier futility analysis [APOE ɛ4 stratification allowed higher doses and longer treatment, explaining the findings. Lecanemab, a monoclonal antibody to Aβ soluble protofibrils, has been shown to reduce amyloid and result in less cognitive decline in a randomized control trial , strengthening our findings [Amyloid deposition along with the hyperphosphorylation of the microtubular associated protein tau and neuronal loss represent the pathological hallmarks of AD and these three variables contribute to the dementia syndrome . Approxitely 20% . The SUVpothesis . This hypothesis . Howeveranalysis . The patfindings .APOE ɛ4 alleles than controls, provides strong evidence that APOE ɛ4 is a driving force for amyloid deposition in YOAD, and is consistent with the observations that APOE ɛ4 lowers the age of onset of AD especially if there is a family history [Our data, which reveals that YOAD patients had greater amyloid burden and  history . It is pAPOE genotype, and may be especially useful in those who are APOE ɛ4 negative and have an early onset [Our observations support those of Zwan et al., that amyloid PET imaging is useful in the diagnosis of YOD, increasing diagnostic certainty and aiding management . The metly onset . That stly onset . These oly onset . Hellwigly onset . Our resThe major limitations of this study are the relatively small N numbers in each patient group. The ABIDE project provided additional evidence that amyloid positive and negative scans were associated with useful changes in diagnosis and treatment in those with and without dementia . In the APOE ɛ4 interact and are fundamental in its pathogenesis. Our findings raise the possibility that young onset AD may be driven more by Aβ than older onset disease, which might explain the heterogeneity in clinical trial data using anti-amyloid treatments.In conclusion, our survey suggests that in sporadic YOAD amyloid deposition and


Worldwide, more than half a billion women of reproductive age are anaemic. Each year, about 70 000 women who give birth die from postpartum haemorrhage. Almost all deaths are in low-income or middle-income countries. We examined the association between anaemia and the risk of postpartum haemorrhage.We did a prospective cohort analysis of data from the World Maternal Antifibrinolytic-2 (WOMAN-2) trial. This trial enrols women with moderate or severe anaemia giving birth vaginally in hospitals in Pakistan, Nigeria, Tanzania, and Zambia. Moderate anaemia was defined as a haemoglobin concentration of 70–99 g/L and severe anaemia as less than 70 g/L. Hospitals in each country where anaemia in pregnancy is common were identified from a network established during previous obstetric trials. Women who were younger than 18 years without permission provided by a guardian, had a known tranexamic acid allergy, or developed postpartum haemorrhage before the umbilical cord was cut or clamped were excluded from the study. Prebirth haemoglobin, the exposure, was measured after hospital arrival and just before giving birth. Postpartum haemorrhage, the outcome, was defined in three ways: (1) clinical postpartum haemorrhage (estimated blood loss ≥500 mL or any blood loss sufficient to compromise haemodynamic stability); (2) WHO-defined postpartum haemorrhage (estimated blood loss of at least 500 mL); and (3) calculated postpartum haemorrhage . Calculated postpartum haemorrhage was estimated from the peripartum change in haemoglobin concentration and bodyweight. We used multivariable logistic regression to examine the association between haemoglobin and postpartum haemorrhage, adjusting for confounding factors.Of the 10 620 women recruited to the WOMAN-2 trial between Aug 24, 2019, and Nov 1, 2022, 10 561 (99·4%) had complete outcome data. 8751 (82·9%) of 10 561 women were recruited from hospitals in Pakistan, 837 (7·9%) from hospitals in Nigeria, 525 (5·0%) from hospitals in Tanzania, and 448 (4·2%) from hospitals in Zambia. The mean age was 27·1 years (SD 5·5) and mean prebirth haemoglobin was 80·7 g/L (11·8). Mean estimated blood loss was 301 mL (SD 183) for the 8791 (83·2%) women with moderate anaemia and 340 mL (288) for the 1770 (16·8%) women with severe anaemia. 742 (7·0%) women had clinical postpartum haemorrhage. The risk of clinical postpartum haemorrhage was 6·2% in women with moderate anaemia and 11·2% in women with severe anaemia. A 10 g/L reduction in prebirth haemoglobin increased the odds of clinical postpartum haemorrhage (adjusted odds ratio [aOR] 1·29 [95% CI 1·21–1·38]), WHO-defined postpartum haemorrhage (aOR 1·25 [1·16–1·36]), and calculated postpartum haemorrhage (aOR 1·23 [1·14–1·32]). 14 women died and 68 either died or had a near miss. Severe anaemia was associated with seven times higher odds of death or near miss (OR 7·25 [95% CI 4·45–11·80]) than was moderate anaemia.Anaemia is strongly associated with postpartum haemorrhage and the risk of death or near miss. Attention should be given to the prevention and treatment of anaemia in women of reproductive age.The WOMAN-2 trial is funded by Wellcome and the Bill & Melinda Gates Foundation. Worldwide, about 500 million women of reproductive age are anaemic and 20 million are severely anaemic.5Postpartum haemorrhage, often defined as losing 500 mL or more of blood in the 24 h after giving birth,Evidence before this studyClinicalTrials.gov, PROSPERO, Embase, and the Cochrane Central Register of Controlled Trials, with the search terms “anaemia”, “haemoglobin”, “postpartum haemorrhage”, and “postpartum bleeding”. The review found that severe anaemia was associated with an increased risk of postpartum haemorrhage (odds ratio [OR] 3·54 95% CI [1·20–10·4]) but there was no statistical evidence of an association with moderate (OR 2·09 [0·40–11·1]) or mild (OR 0·60 [0·31–1·17]) anaemia. The included studies were small, of low methodological quality, and used a variety of different postpartum haemorrhage definitions. Anaemia was examined as a categorical variable with no dose–response analysis.A systematic review and meta-analysis of the association between prenatal anaemia and postpartum haemorrhage was published in 2019. The review reported the results of a search of MEDLINE, Scopus, Added value of this studyTo our knowledge, this is the first study to examine the association between prebirth haemoglobin and postpartum haemorrhage, with haemoglobin as a continuous variable. This was possible because of the large sample size (n=10 561). The study participants were from low-income and middle-income countries where almost all deaths from postpartum haemorrhage occur. We controlled for confounding factors and there were negligible missing data. We found that with decreasing maternal haemoglobin concentration, the risk of postpartum haemorrhage increases monotonically. A 10 g/L decrease in prebirth haemoglobin is associated with increased odds for clinician-diagnosed postpartum haemorrhage. Women with severe anaemia had seven-fold increased odds of death or near miss compared with women with moderate anaemia. Our results were robust to different definitions of postpartum haemorrhage and to correction for haemoglobin measurement error.Implications of all the available evidenceAnaemia is strongly associated with postpartum haemorrhage, with a dose–response relationship. Anaemia also increases the risk of death from postpartum haemorrhage and near miss. Attention should be given to preventing and treating anaemia in women of reproductive age., we used data from the World Maternal Antifibrinolytic-2 (WOMAN-2) trial, an ongoing randomised, placebo-controlled trial done in Pakistan, Nigeria, Tanzania, and Zambia.In our prospective cohort analysisThe exposure was prebirth haemoglobin concentration and the outcome was postpartum haemorrhage defined in one of three ways: (1) clinical postpartum haemorrhage, (2) WHO-defined postpartum haemorrhage, and (3) calculated postpartum haemorrhage. Firstly, clinical postpartum haemorrhage is diagnosed by a clinician usually because the patient has an estimated blood loss of at least 500 mL, or any blood loss sufficient to compromise haemodynamic stability within 24 h of the birth. Clinicians at each site were trained to estimate blood loss by the lead obstetrician and the trial team. We used pictorial guides, with photographs of soiled pads and bed sheets, to aid visual estimation of blood loss. Haemodynamic instability was based on signs such as low blood pressure, tachycardia, or reduced urine output. Secondly, we used the 1989 WHO working group definition for WHO-defined postpartum haemorrhage measurements, which is blood loss of at 500 mL or more from the genital tract within 24 h of a vaginal birth.Because anaemia reduces the oxygen-carrying capacity of blood, women with anaemia cannot tolerate the same volume of bleeding as healthy women and become shocked after a smaller blood loss. Given the lack of an established definition of postpartum haemorrhage in women with anaemia, before conducting this study, we examined different definitions of postpartum haemorrhage in terms of their specificity for substantial bleeding (defined as shock index ≥1·0) and their association with fatigue, physical endurance, and breathlessness.t test and Satterthwaite's approximation formula to test the hypothesis that estimated blood loss is independent of anaemia severity.We reported the cumulative incidence (risk) of postpartum haemorrhage over the observation period (24 h after birth). We then used multivariable logistic regression to examine the association between prebirth haemoglobin concentration and postpartum haemorrhage. The 10 g/L decrease in haemoglobin was obtained as an explanatory variable in the multivariable logistic regression model. To account for clustering, we included a random effect at the site level and a fixed effect for country. The same confounding factors were used in the models for each of the three definitions for postpartum haemorrhage. From the obstetric literature, we identified factors likely to be associated with maternal anaemia and the risk of postpartum haemorrhage. We described our causal assumptions using a directed acyclic graph . We examWe examined the association between haemoglobin concentration and postpartum haemorrhage stratified by the presence or absence of other risk factors for postpartum haemorrhage. We created an indicator variable for having one or more risk factors. We used crude logistic regression to estimate the association between haemoglobin and clinical postpartum haemorrhage. We added polynomial terms for haemoglobin and used likelihood ratio tests to see if this improved the model fit. We used a threshold of p<0·05. We then added interaction terms for the indicator variable and all haemoglobin terms to our model. We checked if interaction terms improved fit in a likelihood ratio test with a threshold of p<0·05.We also corrected for haemoglobin measurement error. The COMPARE study validated haemoglobin measurements obtained using capillary HemoCue samples against a laboratory gold standard in 5724 women who were aged 18 years or older and eligible to give blood through the UK's National Health Service donation service.The funders of the study had no role in study design, data collection, data analysis, data interpretation or writing of the report.A total of 10 620 women were recruited to the WOMAN-2 trial between Aug 24, 2019, and Nov 1, 2022, of whom 10 561 (99·4%) had complete outcome data. 8751 (82·9%) of 10 561 women were recruited from hospitals in Pakistan, 837 (7·9%) from hospitals in Nigeria, 525 (5·0%) from hospitals in Tanzania, and 448 (4·2%) from hospitals in Zambia. Baseline characteristics are presented in 742 (7·0%) women had a clinical postpartum haemorrhage. Mean prebirth haemoglobin for women with clinical postpartum haemorrhage was 76·4 g/L (SD 13·8). The risk of clinical postpartum haemorrhage was 6·2% (544 of 8791) in women with moderate anaemia and 11·2% (198 of 1770) in women with severe anaemia. The primary diagnosis for postpartum haemorrhage by anaemia status is shown in 14 women died (six with moderate anaemia and eight with severe anaemia), in whom the mean prebirth haemoglobin was 68·4 g/L (SD 19·1). Primary causes of death were bleeding (eight women), anaemic cardiac failure (two women), acute respiratory distress syndrome (two women), HELLP syndrome , and sepsis (one woman). Compared with moderate anaemia, severe anaemia was associated with an increased odds of death (OR 6·65 [95% CI 2·30–19·18]). For the nine women who died and had clinical postpartum haemorrhage, mean haemoglobin was 70·0 g/L (SD 17·4). For the eight women who died and had bleeding as their cause of death, mean haemoglobin was 66·6 g/L (SD 17·3). 68 women died or had a near miss, in whom mean haemoglobin was 65·0 g/L (SD 18·1). Severe anaemia was associated with a seven times higher increased odds of death or near miss (OR 7·25 [95% CI 4·45–11·80]) than was moderate anaemia.Estimated blood loss by anaemia severity is shown in Univariable and multivariable analyses of risk factors for postpartum haemorrhage are shown in Antepartum haemorrhage, placenta abnormalities, and current infection might be on the causal pathway between anaemia and postpartum haemorrhage in which case controlling for these factors would be inappropriate. When we conducted a post-hoc sensitivity analysis excluding these factors from the multivariable model , the resWe found no evidence of variable collinearity. The maximum variance inflation factor was 3·0 for abnormal placenta in the multivariable model with the outcome clinical postpartum haemorrhage. When we corrected for HemoCue inaccuracy, a 10 g/L reduction in prebirth haemoglobin increased both the unadjusted (OR 1·30 [95% CI 1·22–1·40]) and the adjusted (aOR 1·22 [1·14–1·32]) odds of clinical postpartum haemorrhage.The relationship between prebirth haemoglobin and postpartum haemorrhage is presented in The association between prebirth haemoglobin and clinical postpartum haemorrhage in women with (n=4564) and without (n=5997) one or more risk factors is presented in A low prebirth haemoglobin concentration greatly increases the probability of postpartum haemorrhage. The association between prebirth haemoglobin and postpartum haemorrhage is similar regardless of how postpartum haemorrhage is defined. Women with severe anaemia also have a greatly increased risk of death or near miss.To our knowledge, this is the largest cohort study of the association between prebirth haemoglobin and postpartum haemorrhage. The large sample size allowed us to examine maternal anaemia as a continuous rather than a categorical variable. Loss to follow-up was negligible and there were few missing data for any variable. Prebirth haemoglobin concentration was measured using capillary haemoglobinometry (HemoCue), which has been shown to be accurate and with the smallest bias in comparison with the reference standard. Postpartum haemorrhage was assessed clinically and objectively on the basis of measured haemoglobin change. The study dataset allowed us to control for many potential confounding factors for anaemia and postpartum haemorrhage risk.Several cohort studies have shown that maternal anaemia increases the risk of postpartum haemorrhage and death. Daru and colleaguesAlthough studies comparing haemoglobin concentration measured using capillary HemoCue and a reference standard laboratory measurement have shown that accuracy is high, some measurement error is inevitable.There are several mechanisms through which anaemia could worsen postpartum bleeding. First, the increased heart rate and cardiac output caused by anaemia could increase blood flow from bleeding vessels.27Although the OR for a 10 g/L reduction in prebirth haemoglobin is smaller than for some other known risk factors for postpartum haemorrhage, because anaemia is a strong risk factor that is also highly prevalent worldwide, a substantial proportion of postpartum haemorrhage cases are likely to be due to anaemia. Nonetheless, anaemia was not recorded as the primary cause of postpartum haemorrhage in any of the 10 561 women in our study. Our results endorse the recommendation that every effort should be made to correct anaemia before delivery.Following publication of the WOMAN-2 trial results, individual deidentified patient data, including a data dictionary, will be made available via our data sharing portal, The Free Bank of Injury and Emergency Research Data (freebird.lshtm.ac.uk) website, indefinitely.Correspondence to: The WOMAN-2 trial collaborators, Global Health Trials Group, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UKThis online publication has been corrected. The corrected version first appeared at thelancet.com/lancetgh on July 18, 2023We declare no competing interests.


Amyloid β (Aβ) is a hallmark of Alzheimer’s disease (AD). Insufficient sleep duration and poor sleep quality have been found to be a risk factor of developing AD because sleep may involve regulating Aβ. However, the magnitude of the relationship between sleep duration and Aβ is still unclear. This systematic review examines the relationship between sleep duration and Aβ in later-life adults.We screened 5,005 published articles searched from relevant electronic databases  and reviewed 14 articles for the qualitative synthesis and 7 articles for the quantitative synthesis.Mean ages of the samples ranged from 63 to 76. Studies measured Aβ using cerebrospinal fluid, serum, and positron emission tomography scans with two tracers: Carbone 11-labeled Pittsburgh compound B or fluorine 18–labeled. Sleep duration was subjectively measured using interviews, questionnaires, or using objective measures such as polysomnography or actigraphy. The studies accounted for demographic and lifestyle factors in their analyses. Five of the 14 studies reported a statistically significant association between sleep duration and Aβ. Using seven eligible articles, our quantitative synthesis demonstrated that the average association between sleep duration and Aβ was not statistically significant .This review suggests that caution should be taken when considering sleep duration as the primary factor for Aβ levels. More studies are needed using a longitudinal design, comprehensive sleep metrics, and larger sample sizes to advance our understanding of the optimal sleep duration and AD prevention. Alzheimer’s disease (AD) is a progressive neurodegenerative disease affecting one in ten adults over the age of 65 worldwide, which poses a considerable economic challenge . More thAlterations in sleep duration and efficiency can lead to wide-ranging consequences for health and well-being and increase the risk of AD . CurrentSleep maintains brain and neural homeostasis . During Prior studies speculates that shorter sleep duration can be associated with Aβ levels, because both total and partial sleep deprivation has been shown to increase Aβ levels in plasma , 23, CSFa priori with the International Prospective Register of Systematic Reviews .The purpose of this study is to conduct a systematic review to evaluate evidence on how sleep duration is associated with Aβ. The study was registered This review was conducted following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement for reporting systematic reviews and meta-analyses . Search 42, Aβ40, Aβ42/Aβ40) as the outcome, 4) a human study of adults aged ≥ 50 years old, and 5) recruited (or included) cognitively healthy individuals. An additional inclusion criterion for the quantitative synthesis was studies that reported sufficient data for examining the effect sizes, such as Pearson’s correlation (r), means, standard deviations, t, F, or X2 values. We excluded studies 1) not written in English, 2) interventional studies, 3) non-peer reviewed papers, proceedings, editorials, and reviews, and 4) the study sample focused only on neurological conditions or sleep disorders. For the quantitative synthesis, we excluded studies that lacked or had inadequate inferential statistical results for calculating the effect size.Inclusion criteria for the qualitative synthesis were as follows: 1) observational studies with a longitudinal or cross-sectional design, 2) includes exposure variables of sleep duration, 3) has Amyloid β plaques  screened the abstracts and titles of the 5,005 articles based on the eligibility criteria. Then, additional articles were removed leaving 61 full-text articles that were reviewed by four authors . A total of 14 articles met the inclusion criteria for the qualitative synthesis and studies met the criteria for quantitative synthesis . Disagrehttps://www.nhlbi.nih.gov/health-topics/study-quality-assessment-tools). The internal validity of the studies were assessed based on 14 domains: 1) bias due to an unclear purpose, 2) bias due to an unclear specification of the population, 3) bias due to ineligible participants, 4) bias due to recruitment from a different population, 5) bias due to unclear power justification, 6) bias due to measure timing, 7) bias due to time frame, 8) bias due to outcome level, 9) bias due to invalid exposure measure, 10) bias due to frequency of the assessments, 11) bias due to the outcome measure, 12) bias due to an unblinded assessment, 13) bias due to loss during follow up, and 14) bias due to statistical analysis and confounding. For each domain, we categorized the risk of bias as either low or high risk. We rated an item “unclear risk” if there was no information about the risk of bias.The risk bias of the selected papers was assessed independently by two reviewers using the National Institute of Health Study Quality Assessment Tool (2019) for Observational Cohort and Cross-Sectional Studies (I2 index using I2 = 100% × (Q − degree of freedom)/Q to identify how the variance in observed effects reflected the variance in true effects rather than by random error. The random-effects model was applied for the current study because we expect that the sampling distribution varied across the studies and parameters were drawn from random variables [We aggregated the effect sizes across the studies and calculated the publication bias, overall effect sizes, and Q statistics using Comprehensive Meta-Analysis (CMA) version 4 software . We also calculated the effect sizes using Pearson’s r as the effect size index after examining the available information on the correlation between sleep duration and Aβ. The r was extracted from each study and converted to Fisher’s Z = 0.5*Log(1 + Corr)/(1-Corr) . We usedariables –36. To cariables Thirteen articles were included in the qualitative synthesis portion of this review and six studies were included for the quantitative synthesis. The total number of participants of the 14 studies included in the qualitative synthesis was 11,295 individuals ranging from 13 to 4,712. The overall demographics of this review are presented in The mean age of the samples ranged from 61.6 to 75.7. Data from the studies included study subjects, and most studies had specific inclusion criteria for the mean age and cognitive status –50. All Both subjective and objective sleep measures were used in the reviewed studies . Five stEight of the reviewed studies used subjective measurements including the Pittsburgh Sleep Quality Index (n = 4) , 47–49, 40 and Aβ42 and assessed different combined ratios [The reviewed studies used a variety of Aβ measures . The 13 NFL/Aβ42), 47, 48 NFL/Aβ42) or by SiNFL/Aβ42).Nine studies measured Aβ using a PET scan , 49, 50.42 of 500pg/ml for the cut off.Quantitative assessment was done using standardized uptake value ratios (SUVr) in four studies –44, 46, 42  and Aβ42/Aβ40 ratio [ < 0.001). Fu et a < 0.001).2 statistic is 0%, indicating that 0% of the variance in observed effects reflect the variance in true effects rather than sampling error. Tau reflects the standard deviation of true effect size, which is 0.000 in Fisher’s Z units.Six eligible articles were used for the quantitative synthesis because the remaining studies were not included due to a lack of reporting Pearson’s correlation or not having a mean sleep duration for the amyloid beta categories. As shown in the standard error funnel plot by Fisher’s Z , the plot is slightly asymmetric, indicating that there could be minor publication bias from the included studies. This might be due to either our inability to identify studies with non-significant finings or failing to report non-significant findings . We ran Our review synthesized fourteen studies for the qualitative synthesis and seven studies for the quantitative synthesis focusing on sleep duration and Aβ. Using seven eligible articles, our quantitative synthesis demonstrated that the average association between sleep duration and Aβ was not statistically significant . This review suggests that caution should be taken when considering sleep duration as the primary factor for Aβ levels. The studies used subjective questionnaires or questions, PSG, or actigraphy to measure sleep duration. Aβ was measured using PET CT, CSF, or serum. There are quality concerns about a few studies due to the small sample size, timing of the measures, limited use of comprehensive measurements, and confounding variables.Although not all 14 qualitative studies we reviewed demonstrated statistically significant associations between sleep duration and Aβ, five studies showed that shorter sleep duration was associated with greater Aβ, but one study reported sleep great than 8 hours is associated with Aβ burden. The quantitative synthesis revealed an effect size of −0.006, but it was not statistically significant, indicating that sleep duration may not be a primary factor in Aβ accumulation. Alternatively, these results may be due to moderators , heterogenous outcome types of Aβ that may not provide consistent ideas, or publication bias due to insignificant results not being favorable for publication. Prior research has demonstrated that chronic sleep restriction or deprivation of slow wave sleep can alter the diurnal fluctuation of CSF Aβ levels , 51, 52.There was considerable heterogeneity in the methods used in the reviewed studies to conclude the relationship between sleep duration and Aβ burden. Although self-report sleep duration may not provide an accurate estimate of sleep duration, eight of thirteen reviewed studies used self-report measures, such as Pittsburgh sleep quality index (n = 6), medical outcomes study sleep scale (n = 1), or single question (n = 3) to assess sleep duration. The interview question in most studies directly asked participants about the average total number of hours of sleep, but these studies did not ask about the time window of habitual sleep or other dimensions. The time window of sleep duration assessments varied from 1 night with polysomnography to 4 weeks using Pittsburgh sleep quality index. Future studies are needed to capture habitual sleep duration using an actigraphy with a verification with sleep diary for 7 to 14 days . The use42 demonstrating lower Aβ42 values for shorter or longer sleep and the highest for 6.23 hours [Both the measures and the types of data were different in the studies. The majority of the studies used sleep duration as a continuous format using multivariate linear regression models. Winer et al. (2021) and Liu et al. (2021) used a categorial variable in the model and provided insights on the dose-dependent relationship between sleep duration and Aβ, 50. Bot23 hours . Future In addition to the considerable variability in sleep measurements, Aβ was also measured in different ways: PET to quantify the Aβ burden, CSF, and serum sample. Studies using PET used different tracers  as well as different quantification methods. Most studies focused on global Aβ burden in brain, but assessing both the overall levels of Aβ in PET as well as specific regional deposition could help us understand areas of the brain that may be affected more than other areas. This variability across the measurements prevented us from conducting a meta-analysis and drawing strong conclusions. However, it is promising in the current field of science to review data across different measures of Aβ accumulation. Although AD can be diagnosed at an autopsy , the US 42 level (≤ 500 pg/mL), which is indicative of amyloid deposition in the brain, had worse sleep efficiency than those with a normal CSF Aβ42 level , although sleep duration was not statistically significant. The reviewed studies also identified a positive link between Aβ and different sleep characteristics including less adequate sleep, more sleep problems, and greater somnolence based on participants’ self-reported perceptions [In addition to sleep duration, other sleep dimensions could be important factors for Aβ accumulation. First, reduced sleep efficiency could be a critical factor associated with Aβ accumulation. Of the reviewed studies, two reported that lower sleep efficiency was associated with Aβ burden. Ettore et al. (2019) found that an increase in sleep efficiency was associated with a 41% reduction of Aβ positivity . Ju et al. (2013) reported that individuals with a low CSF Aβceptions , frequenceptions , longer ceptions , greaterceptions , a higheceptions , 40. TheThe reviewed studies accounted for various demographic and clinical confounders in the multivariate models. Most of the reviewed studies accounted for age and sex, which are well-known confounders , 66–68. The strength of this review is that we examined current evidence related to sleep duration and amyloid burden. However, there are a few limitations. First, the study did not test for moderating effects of age, sex, APOE4 status, or sleep efficiency. Second, the current study only included publications written in English even though some important findings may have been published in different languages. Third, this review focused only on sleep duration even though other specific sleep characteristics could have more influence on Aβ pathology.The results of this systematic review suggest that previous studies that have demonstrated an association between sleep duration and Aβ accumulation should be understood cautiously. Researchers would greatly benefit from more studies using a longitudinal design, comprehensive sleep measure, broad range of biomarkers, and larger sample sizes to advance our scholarly understanding of the relationship between sleep and AD.


A hallmark of mechanical metamaterials has been the realization of negative Poisson’s ratios, associated with auxeticity. However, natural and engineered Poisson’s ratios obey fundamental bounds determined by stability, linearity and thermodynamics. Overcoming these limits may substantially extend the range of Poisson’s ratios realizable in mechanical systems, of great interest for medical stents and soft robots. Here, we demonstrate freeform self-bridging metamaterials that synthesize multi-mode microscale levers, realizing Poisson’s ratios surpassing the values allowed by thermodynamics in linear materials. Bridging slits between microstructures via self-contacts yields multiple rotation behaviors of microscale levers, which break the symmetry and invariance of the constitutive tensors under different load scenarios, enabling inaccessible deformation patterns. Based on these features, we unveil a bulk mode that breaks static reciprocity, providing an explicit and programmable way to manipulate the non-reciprocal transmission of displacement fields in static mechanics. Besides non-reciprocal Poisson’s ratios, we also realize ultra-large and step-like values, which make metamaterials exhibit orthogonally bidirectional displacement amplification, and expansion under both tension and compression, respectively. Natural and engineered Poisson’s ratios obey fundamental bounds determined by stability, linearity, and thermodynamics. Here, the authors design and realize self-bridging metamaterials with Poisson’s ratios surpassing the theoretical bounds. In terms of the fundamental metric determining mechanical deformations, Poisson’s ratio, metamaterials have been realized to support negative values based on auxetic deformation patterns, via bendable or buckled ligaments12, or rotatable nodes15. The range of admissible Poisson’s ratios for isotropic materials is . Anisotropy may expand this range, but based on the orthotropic constitutive law, thermodynamics predicts a general bound on Poisson’s ratios in the linear and stable elastic regime16, namely 0 ≤ vijvji < 1  and micropolar elasticity is not considered17. This bound includes positive Poisson’s ratios  only allows displacement amplification in one direction with a Poisson’s ratio larger than 1. However, a multi-mode lever with two detachable fulcrums can overcome this limit and realize bidirectional displacement amplification by changing rotation modes via different fulcrums  are predefined for the design of non-reciprocal Poisson’s ratios, while four slits  are predefined for the design of ultra-large and step-like Poisson’s ratios. The geometrical parameters of the slits are provided in Supplementary Fig. 11, which are built for different properties. Numerical simulations with 2 × 2 unit cells . Poisson’s ratios of these samples are evaluated by uniaxial compression and tension tests . In this framework, the design problem is described by the optimization problem in Eq. , where tell Fig. . The cellls Fig.  were perll Fig. c and c4 ac2 and c4 reconfigures the microstructural topology with a change in internal connectivity. Then, the microscale levers show two different rotation modes: (1) for transverse compression, the microstructure rotates about two fulcrums; (2) for longitudinal compression, the arms at c2 and c4 drive the rotation of the microstructure. These two different rotation behaviors result in positive and negative Poisson’s ratios, v21 > 0 and v12 < 0, respectively  that is derived from static reciprocity , stemming from static reciprocity. Hence, we can predict the emergence of a static non-reciprocal bulk mode (F1u2→1 > 0 > F2u1→2), different from the one in “fishbone” metamaterials designed by breaking geometrical symmetry and introducing geometrical nonlinearity25. The Poisson’s ratios v21 > 1 and 0 > v12 > −1 . In mechanical systems under small strains, general geometric and material nonlinearities may vanish25, but self-contacts can occur via zero initial distance between self-contact surfaces. The effective Poisson’s ratios and elastic moduli of the equivalent models agree well with those of the designed metamaterial under small strains , deins Fig. , which dF1 = − 60 N (F2 = − 60 N) was applied at its right (top) side. Under the same force, u1→2 > 0 > u2→1, and |u1→2| is an order of magnitude greater than |u2→1|, as shown in Fig. u1→2| is large than the input displacement |u1|, while the output displacement |u2→1| is smaller than the input displacement |u2|. Thus, there is a unidirectionally amplified non-reciprocal displacement field, and the amplification performance well fits the prediction of designed Poisson’s ratios. Poisson’s ratio is a fundamental property that directly quantifies the relation between input (ui) and output (ui→j) displacement. Hence, our designed self-bridging metamaterial may provide an explicit and programmable way to manipulate non-reciprocal transmission of the displacement field via designing Poisson’s ratios.The broken static reciprocity associated with Poisson’s ratios was tested in the designed metamaterial Fig. . Figure c1 and c3, while the locations change to c2 and c4 under longitudinal compression , thv12 > 0 and tensile v12 < 0, Fig. 15, and it realizes the expansibility under both tension and compression, different from the contractility induced by bending beams41. In the numerical simulation under compression, in terms of internal connectivity, the microstructural topology is reconfigured by self-bridging slits at c2 and c4 in the microstructure (Fig. c1 and c3 open while c2 and c4 close, and the microstructure rotates about fulcrums at c2 and c4. Only the first rotation mode activates the auxeticity, and thus the metamaterial exhibits step-like Poisson’s ratios. We found the simulated deformation patterns agree well with the experimental ones (Fig. The metamaterial in Fig. ure Fig. . Then, tnes Fig. .We built two equivalent models (Supplementary Fig. 17,These unusual Poisson ratios can be tailored by adjusting the target values in the inverse design framework. More designed metamaterials and corresponding deformation patterns are shown in Supplementary Fig. Mechanical metamaterials surpassing the thermodynamic limit of Poisson’s ratios were designed and realized through inverse design based on predefined slits for self-contacts and topology optimization. Symmetry and invariance of constitutive tensors are basic assumptions for the theoretical limit of Poisson’s ratios in the linear elastic regime. In terms of internal connectivity, the microstructural topologies of the designed mechanical metamaterials are reconfigured by the self-bridging slits, driving different rotation behaviors of microscale levers. Then, under different loads, the constitutive tensors of the self-bridging metamaterials no longer obey symmetry and invariance, enabling Poisson’s ratios that surpass the conventional limits.42 with oneway amplified displacement field, strain sensors43 with bidirectional ultra-large displacement amplification, civil protection equipment44, and biomedical stents45 with expansibility of tension and compression, and soft robots18 with flexible structural deformation.These metamaterials possess inaccessible deformation patterns, including one-way displacement amplification with broken static reciprocity, orthogonally bidirectional displacement amplification, and transverse expansion under both longitudinal tension and compression. These deformation patterns are active under both large and small strains by setting zero initial distance between self-contact surfaces, and can be tailored by the topological design of purely passive microstructure. Based on non-reciprocal Poisson’s ratios, we demonstrate the emergence of a bulk mode that breaks static reciprocity, which opens new avenues to manipulate non-reciprocal transmission of the displacement field via designing Poisson’s ratios. Our work may substantially extend the applications of Poisson’s ratios in modern devices, for example, mechanical energy harvestersThe strain energy density 24 and then projected using the tanh function25 via24 is used to establish the relation between the projected variables and material parameters:A design method with density-based topology optimization is built to design the unit cell, in which a set of element-wise design variables, entclass1pt{minima25 is used in which eroded, normal, and dilated manufacturing processes are mimicked with For accessible feature size in fabrication, a three-case robust formulation26To enhance numerical stability under large compression strain, we adopt the energy interpolation form2. s is the elemental area vector; the prescribed area fraction is 27 is the optimizer used to solve the model, where the gradient of the objective and constraints are calculated via the adjoint method28. The update processes of the microstructural topologies are presented in Supplementary Fig. The stiffness of the metamaterial is maximized using a formulated density-based topology optimization modela = 25 mm, as shown in Fig. A10 and A01 of the two-term Mooney–Rivlin model were −0.17 and 2.5 MPa, respectively, which were obtained by least-squares fitting of the relation between engineering strain and stress in a uniaxial tension test (Supplementary Fig. The samples with non-reciprocal, ultra-large, and step-like Poisson’s ratios consisted of 3 × 3, 6 × 6, and 6 × 8 unit cells with lattice constant Tension and compression tests were performed using a HANDPI machine with a 500 N loading cell. For non-reciprocal Poisson’s ratios, the sample in Fig. Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Movie 1Supplementary Movie 2Supplementary Movie 3