Moreover, accurate high-throughput analysis requires a uniform droplet size distribution and high production rates. Although the previously reported microfluidic coflow step-emulsification method can be used to generate highly monodispersed droplets, the droplet diameter ( Similarly, bubbles and perfluorocarbon droplets are used as contrast agents in ultrasound detection and therapy4. Depending on the application, the chemical composition of particles or droplets varies from solutes to macromolecules, such as proteins, polymers, and DNA1. Likewise, the viscosity of the disperse phase fluid ranges from 1 cP for aqueous biofluids, including blood plasma and serum5, to 1000 cP or higher for concentrated polymer solutions. Existing methods for making nanodroplets and nanoparticles include high-pressure homogenization and sonication1, low-energy methods such as self-emulsification, which rely on the extraction of solvent from the dispersed phase to the continuous phase to shrink the droplets to the submicron scale, and the phase inversion temperature or component (PIT or PIC), which involves the inversion of curvature of the interface at a critical temperature or by the addition of an auxiliary chemical component6. However, the existing methods are selective in the chemical composition of the phases, and it is challenging to form droplets with a uniform size distribution. The latter problem can be solved by using microfluidic techniques, where the droplet size is tightly controlled by the geometry of the T-junction microchannel7, Y-junction microchannel8, flow focusing or coflow method9. In these techniques, the droplet size is regulated by the microchannel dimension11, and, therefore, generating submicron-sized droplets requires microchannels of submicron width and height, which is technically challenging. For example, Toprakcioglu et al. reported a T-junction nanochannel geometry to generate nanodroplets of controllable size7. Other methods were proposed to downscale the droplet size. For instance, small capillaries assembled into PDMS channels can generate droplets and bubbles whose sizes are controlled by the capillary inner diameter, and such hybrid devices are realized in a flow-focusing geometry4.Droplets and particles of micron and submicron size are widely used in targeted drug delivery and controlled release9. A robust tip-streaming method can be realized in a hybrid device with a flame-shaped capillary assembled with a PDMS channel that allows for 3D focusing of the dispersed thread4. A similar device was used for emulsification of water as well as highly viscous polyethylene glycol at the submicron scale11. Tip streaming provides good conditions for the formation of micron and submicron droplets; however, it is restricted to a specific range of surfactant concentrations and to low-viscosity ratio fluids, i.e., 12.Tip-streaming is an effective way to generate monodispersed micron- and submicron-sized droplets when the interface is subjected to shear or elongation flow that drags the surfactants at the interface toward the tip of the disperse phase stream, resulting in a considerable reduction in the local surface tension. Then, a thin thread is drawn out of the stagnant tip, breaking up the stream into tiny droplets13. Alternative strategies have been proposed; for instance, low-viscosity droplets can be first generated in a high-viscosity continuous phase by flow focusing before the system undergoes a phase inversion by switching the surface wettability to form high-viscosity droplets suspended in a low-viscosity continuous phase14.Emulsification of high-viscosity fluids is challenging when using standard microfluidic methods since the interfacial deformation required for subsequent thread breakup is suppressed by viscosity17. The dispersed phase does not wet the channel walls, and a convex interface with the continuous phase  is formed, resulting in an extra Laplace (capillary) pressure within the dispersed phase thread. Theory based on quasistatic equilibrium explains the reason for thread necking as the curvature in the thread balancing the curvature of the growing drop18. An alternative theory of coflow step-emulsification considers that the competition of viscous and surface tension forces can be used to compute the interfacial profile based on Hele–Shaw hydrodynamics21. Experimentally, improvements in the local geometry near the step were proposed, such as the addition of a constriction and bypass channel23 and a shunt channel that facilitates adverse flow24 and promotes thread pinching, increasing the droplet production rate. When the dispersed fluid flowing in the Hele-Shaw channel partially wetted the walls , it was found that the contact angle of the dispersed fluid forming with the channel plays an essential role in determining the droplet size25. The viscous shear effect has a marginal effect on the droplet size, rendering it insensitive to fluctuating flow rates, while it is mainly controlled by the Hele-Shaw channel height, b26. To generate micron- and submicron-sized droplets produced with the coflow step-emulsification technique, ultrashallow channels with heights <1 µm could be used27, which require sophisticated nanofabrication and high operating pressures due to high hydraulic resistance. The triphasic coflow SE was recently proposed to generate double emulsions with ultrathin shells of thickness ~1.6% of the droplet diameter28.Microfluidic step-emulsification (SE) is an emerging technology used for the generation of highly monodisperse microdroplets at high throughput29. Single bubbles in a solution may shrink due to the dissolution of the gas into the unsaturated liquid phase30. Microbubbles suspended in lipid solution can dissolve into the aqueous phase to form nanobubbles that can be used as ultrasound contrast agents (UCAs)31. A mixture of perfluorocarbon (PFC) with another component that is soluble in the continuous phase can be emulsified into drops32 or bubbles33 and subsequently reduced to smaller-sized PFC. This idea is similar to methods based on solvent evaporation or extraction34.Hollow-core structures can be generated in a microfluidic device by encapsulating gas bubbles within liquid drops. A gas-core triple emulsion was shown to be able to release encapsulated fluid triggered by ultrasound35. Another method used laser excitation to create a bubble near the free surface on the liquid side, and bubble expansion caused a shock wave to propagate toward the interface, triggering a jet of liquid droplets36. Air can also be used as the focusing fluid , which drives liquid thread breakup and droplet formation37. To the best of our knowledge, the generation of micron- and submicron-sized droplets  remains a challenging undertaking for conventional microfluidic methods with passive flow control.Droplet generation methods involving gas were previously explored in various microfluidic geometries; for example, in a T-junction device, bubbles were used to trigger pinching of the thread of the disperse phaseIn the present work, we propose a gas-assisted coflow step-emulsification (SE) method to first generate precursor hollow-core double emulsions, which subsequently produce small, single emulsions of the disperse phase upon dissolution of the encapsulated gas cores. Smaller micron-sized droplets at higher throughput can be generated compared with the standard biphasic SE methods. Therefore, single emulsions of micron- and submicron-sized droplets can be readily generated using the auxiliary coflow of air.b and w are the Hele-Shaw channel height and width, respectively. It should be noted that the air flow velocity in the Hele-Shaw channel is on the order of 10 cm/s, so the effects of compressibility can be safely neglected, as the Mach number is well below 0.3. In this situation, the pressure contributes to the flow of the air instead of compressing the air. Figure As sketched in Fig. 38, where 30. In addition, Ostwald ripening, which corresponds to air diffusing from a smaller bubble to a larger bubble driven by the difference in the Laplace pressure, is apparently operative as the mechanism of the bubble shrinkage39. It can be readily seen in Fig. b. Previous research on biphasic coflow step-emulsification theoretically predicted that the diameter of the droplet cannot be smaller than ~3b19. In this work, the gas-assisted step-emulsification of the oil droplet overcomes this theoretical limitation. We note that during the generation of hollow-core double emulsions, the shell encapsulating the innermost air bubble can be made ultrathin by tuning the oil flowrate, so the oil shell cannot be detected using an optical microscope. However, based on the diameter of the final droplet that can be measured, the thickness of the shell of the precursor hollow-core double emulsions can be easily estimated. In the following subsections, we study various dynamic regimes of droplet formation depending on the relevant dimensionless parameters and investigate the variation in the droplet size and generation rate.We observe that the compound (containing the air and oil phases) disperse phase thread breaks synchronously at the step, forming hollow-core double emulsions (Supplementary Movie P) and the flowrates of the outer dispersed FC40 oil ” regime at smaller 28 In the case of triphasic coflow liquid step-emulsification, 28 is not readily accessible. Converting the air pressure into the air flow rate 28. Hence, the phase diagram of the air-assisted step-emulsification is in qualitative agreement with the theory developed for the triphasic liquid step-emulsification.In the phase diagram Fig. , the SE 42. However, the droplet production rate in this regime is relatively low. Moreover, this regime is restricted to a very narrow range of parameters, limiting its practical applicability for generating hollow-core double emulsions.Starting at low values of At low pressure , which is different in the two cases  droplets, f can be compared with the experimentally measured rate e is the shell thickness, being equal to the resulting oil droplet volume D of the hollow-core double emulsion. We next compare 28:28. As shown in Fig. 13 of oil at the step during the SE, whose volume is not accounted for in the shell volume. Some satellite drops can be observed in Supplementary Fig. As shown in Fig. ray Fig. , which mb/b Fig.  with the26. In this work, we use devices with three different channel heights  to generate hollow-core double emulsions at the step and subsequently form smaller single oil droplets due to air dissolution. We measure the droplet production rate and size at various flow rates of the outer disperse oil P. The absolute inlet air pressure P varies with the channel height b, which is 100–160 mbar for channels with b = 20 μm, 550–800 mbar for b = 5 μm, and 1100–1600 mbar for b = 1.8 μm. It is also shown that for both biphasic (air/water) SE and triphasic (air/oil/water) SE, there is only marginal dependence of droplet size on P  channels to generate micron-sized droplets. Submicron channels possess high hydraulic resistance  was left for sedimentation for 2 days, and most micron-sized beads sediment due to gravity  affected by this limitation.The air-ETPTA compound thread is subject to fast oscillations during step-emulsification, resulting in strong elongational stretching of the ETPTA filament beyond the step  due to high hydraulic resistance, rendering microfabrication complex and expensive in comparison to PDMS-based soft lithography. Conventional microfluidic settings (such as flow-focusing) can theoretically generate micron-sized droplets by reducing the channel dimension, but they are subjected to the same technical difficulties described above.The high-throughput production of highly monodisperse micron- and submicron-sized droplets is required in many biomedical applications, such as ultrasound imaging and drug delivery. Conventional coflow step-emulsification methodsIn coflow step-emulsification, generation of small droplets requires the capillary number not to exceed some critical value, beyond which only large “balloon” droplets (of the size much larger than the channel height) can be generated. Since the capillary number is proportional to the flow rate and viscosity, it limits the single channel throughout, particularly for high-viscosity fluids. Some biological and chemical fluids do have high viscosity and cannot be easily emulsified.We propose an alternative method of gas-assisted coflow step-emulsification, which is able to generate gas-in-oil-in-water hollow-core double emulsions, which act as precursors for micron-sized oil-in-water emulsions. Following Ostwald ripening and dissolution of the air cores, monodisperse micron-sized oil droplets are formed, with a minimal diameter reaching ~1.7 times the channel height, twice as small as in the standard biphasic step-emulsification. Due to the low viscosity of the air, the production rate is increased by an order of magnitude in comparison with the standard step-emulsification method. At the same time, this method can be performed using microchannels  dissolved in deionized water (from Greagent) at two different concentrations. A 2.5% (w/w) SDS aqueous solution was used as the continuous phase in the experiment of generating hollow-core double emulsions with Fluorinert FC40 oil (purchased from 3 M). A 0.2% (w/w) SDS aqueous solution was used as the continuous phase in the experiments of generating the trimethylolpropane ethoxylate triacrylate (ETPTA) droplets. ETPTA (from Aladdin) was mixed with 0.5% (w/w) of photoinitiator 2-hydroxy-2-methyl-1-phenyl-1-propanone (from Sigma‒Aldrich). The surface tension between the liquids was measured using the pendent drop method with an optical contact angle meter Dataphysics OCA20.Microfluidic step-emulsification devices were fabricated with the standard soft lithography method. The silicon wafer was prepared with SU-8 photoresist under UV exposure and possessed two different heights realized by a mask aligner in the lithography machine. The microchip was prepared from polydimethylsiloxane , and a mixture of PDMS and the curing agent at a 10:1 ratio was poured onto the silicon wafer and then heated in an oven at 80 °C for 2 h. The PDMS block was sealed with a glass slide with plasma (Harrick PDC-32G-2) for 35 s, which rendered the channel walls hydrophilic. The experiments were conducted immediately after the plasma treatment, which preserved the hydrophilicity of the PDMS channel walls. A stable generation of droplets for 1 h and 20 min is demonstrated in Supplementary Movie The device has three inlets for the three fluid phases, two junctions, and a shallow (Hele-Shaw) microchannel, which is connected with a wide and deep reservoir Fig. . We usedThe liquids were injected by syringe pumps (Longerpump), and the air was injected by a pressure pump (Fluigent). We first injected air to prevent liquids from entering the air inlet channel and then the SDS aqueous solution to keep the hydrophilicity of the channel intact and the oil. The flow was observed under an inverted transmission microscope (Nikon Ti2-U) and filmed by a high-speed camera (Photron Fastcam mini).The liquids were injected by controlling the flowrates of the oil and aqueous phase, whereas the injected air was controlled by pressure. The flow rate  was calculated based on measuring the size of the hollow-core double emulsion and production frequency measured in the experiments. The droplet sizes of the hollow-core double emulsions and of oil droplets were measured by ImageJ software. In the case of ETPTA droplet formation, we collected the formed mixture of bubbles and droplets at the exit of the channel and exposed it to UV for 20 min for solidification. Then, we left it for 2 days for the beads to sediment due to gravity. We measured the supernatant by dynamic light scattering (DLS) and microscopy. For microscopic observation, we placed 20 μL of the supernatant onto a glass slide and observed the particles at ×1500 magnification. The water evaporated quickly, leaving the ETPTA beads fixed to the glass. In addition, we observed the ETPTA beads by scanning electron microscopy (SEM).Supplementary DocumentSupplementary Movie S1Supplementary Movie S2Supplementary Movie S3Supplementary Movie S4


Fluorescence lifetime imaging microscopy (FLIM) is a powerful imaging technique that enables the visualization of biological samples at the molecular level by measuring the fluorescence decay rate of fluorescent probes. This provides critical information about molecular interactions, environmental changes, and localization within biological systems. However, creating high-resolution lifetime maps using conventional FLIM systems can be challenging, as it often requires extensive scanning that can significantly lengthen acquisition times. This issue is further compounded in three-dimensional (3D) imaging because it demands additional scanning along the depth axis. To tackle this challenge, we developed a novel computational imaging technique called light field tomographic FLIM (LIFT-FLIM). Our approach allows for the acquisition of volumetric fluorescence lifetime images in a highly data-efficient manner, significantly reducing the number of scanning steps required compared to conventional point-scanning or line-scanning FLIM imagers. Moreover, LIFT-FLIM enables the measurement of high-dimensional data using low-dimensional detectors, which are typically low-cost and feature a higher temporal bandwidth. We demonstrated LIFT-FLIM using a linear single-photon avalanche diode array on various biological systems, showcasing unparalleled single-photon detection sensitivity. Additionally, we expanded the functionality of our method to spectral FLIM and demonstrated its application in high-content multiplexed imaging of lung organoids. LIFT-FLIM has the potential to open up new avenues in both basic and translational biomedical research. Fluorescence lifetime imaging microscopy (FLIM) ,2 has beThe FLIM techniques are generally stratified into two categories: frequency-domain FLIM and time-domain FLIM. Frequency-domain FLIM utilizes frequency-modulated light to illuminate the sample, inducing fluorescence that oscillates at the same frequency but with a reduced modulation depth and a phase shift due to the non-instantaneous fluorescence decay . To extrTo acquire a two-dimensional (2D) lifetime map, most time-domain FLIM systems scan the sample either in the spatial domain, such as a confocal FLIM microscope ,16, or iThe use of single-photon avalanche diode (SPAD) arrays in time-domain FLIM provides a solution to this long-standing problem by enabling parallel measurement of fluorescence decays at multiple image pixels. Moreover, SPAD arrays offer considerably greater sensitivity than conventional gated cameras, making them an excellent choice for low-light imaging applications. A SPAD imager can operate in either time gate or time-correlated single photon counting (TCSPC) mode, with TCSPC being the preferred detection method for its higher precision, faster speed, and greater sensitivity. However, the native fill factor of 2D SPAD arrays operating in the TCSPC mode is generally low (<10%)  due to t0%) [50% , which rTo tackle the aforementioned challenges and streamline the acquisition of 3D FLIM data using a linear SPAD array, we devised a novel computational imaging technique called LIght-Field Tomographic Fluorescence Lifetime Imaging Microscopy (LIFT-FLIM). Our approach has been only recently made possible by an emerging technique, light field tomography (LIFT) ,22, whicAside from its impressive 3D imaging capability, LIFT also possesses inherent compatibility with spectral imaging . CapitalBased on computational imaging, LIFT-FLIM operates in two steps: data acquisition and image reconstruction. We show a LIFT-FLIM system in To compute a 3D image, conventional light field cameras require the acquisition of a comprehensive set of spatial and angular information of a light field, resulting in a significant data load. However, our previous work demonstrated that this acquisition method is inefficient and generates a substantial amount of redundant data , which cWe formulate the image formation of LIFT-FLIM using a linear model. For a given perspective image Rather than capturing a complete set of i.e. the sub-aperture image Here The overall image forward operator For direct fluorescence lifetime measurement using a linear SPAD array with TCSPC , the imaFor spectral FLIM measurement using a gated ultrafast camera , the imaThe image reconstruction of LIFT-FLIM and -sFLIM involves solving the inverse problems of Akin to conventional light field cameras, LIFT-FLIM and -sFLIM divide the aperture to extract the depth information. Therefore, they have a reduced lateral resolution  network –31 H and FAD, enabling its application in cancer diagnosis and intraoperative surgical guidance ,39. PartWe excited the sample at boundary  reveals boundary . The obsin vivo tissue, have gained growing interest for modeling tissue development and disease [We demonstrated LIFT-sFLIM in 3D multiplex imaging of lung organoids. Organoids, 3D multicellular stem-cell-derived constructs that mimic  disease –48. Part disease –53.Supplementary Table 1 and labeled epithelialmesenchymal transition by INK4A (p16) expression.One primary challenge for high-content phenotypic screening of organoids is extraction of multivariate information from organoids labeled with multiple biomarkers –56. HereUsing LIFT-sFLIM, we acquired a five-dimensional (5D) dataset Using LIFT-FLIM for 3D lifetime imaging offers a crucial benefit of reducing the number of scanning steps required compared to traditional point- or line-scanning time-domain FLIM techniques. To produce a 3D image of For non-compressive measurement, we set Alternatively, like sparse-view computed tomography , we can To quantify the dependence of the reconstructed image quality on the The imaging speed of LIFT-FLIM is determined by the total number of projections The spatial resolution of LIFT-FLIM is fundamentally limited by optical diffraction when performing non-compressive measurements. Due to the division of the aperture, LIFT-FLIM has a lateral resolution of LIFT-FLIM images can be reconstructed and analyzed in real-time. For instance, when processing uncompressed measurement data, a simple inverse Radon transform takes about 0.13 seconds per time bin on an Nvidia RTX3080Ti GPU with CUDA. Subsequently, deep learning enhancement and phasor analysis require 0.079 and 0.024 seconds, respectively. Parallel computing reduces the total post-processing time to less than 0.3 seconds.The light throughout of LIFT-FLIM depends on the sub-aperture size of a perspective image, the ratio of projection line image width to the detector pixel’s size, and the fill factor of the image sensor. As detailed in Supplementary Materials, the LIFT-FLIM is built on an unfocused light field imaging configuration, where the projection line width at the image sensor equals to the sub-aperture diameter, i.e., a shorter focal length of the cylindrical lens in Here nstrated . HoweverOn the other hand, for LIFT-sFLIM using a gated ultrafast camera, the light throughput is determined by the sub-aperture size of a perspective image, the diffraction efficiency of the grating, Since trometer ,60.i.e., more photons) can lead to a higher PSNR. For high-quality image reconstruction  as the front-end optics and excited the sample with a pulsed laser source . The emitted fluorescence is collected by a microscope objective lens , and an intermediate fluorescence image is formed at the side image port of the microscope.To split the light, we employed a beam splitter , which transmits The fluorescence is then directed through a The subsequent system is split into two arms, namely the LIFT-FLIM and LIFT-sFLIM arms. The former employs a linear SPAD array , while tWhen the mirror is positioned at 1, the fluorescence is directed towards the LIFT-FLIM sub-system through a camera lens  and directly measured by the linear SPAD camera. The linear SPAD camera comprises 256 effective CMOS SPAD pixels with a pitch of oseconds . It is cIn position 2 of the flip mirror, the emitted fluorescence is directed to the LIFT-sFLIM sub-system. The line image is relayed to the image sensor plane by a pair of camera lenses . To disperse the line image along the To tune the illumination wavelength from the supercontinuum laser, we built a wavelength-selecting module  using a digital micromirror device (DMD). The collimated white laser beam is first dispersed by a transmission diffraction grating  and line focused onto the surface of the DMD  through a cylindrical lens . The broadband illumination has a line dispersion of When imaging the mixed fluorescence beads and mouse kidney tissue section, we used multiband excitation with two different wavelengths  isgorithms . Besidesgorithms ,28, inveA light field acquired by conventional light field cameras can be parameterized by the aperture plane e images . Unlike For sub-aperture For numerical refocusing, we applied the correspondent shearing factor to each projection image and updated sinogram for reconstructing the depth image (Supplementary Note 1).Extending the depth of field can be achieved through a similar approach to conventional light field imaging, which involves refocusing onto different depths, extracting the sharpest feature for each pixel, and assembling an all-in-focus image .To ensure that the scanning range of sub-apertures fully utilizes the entire aperture of the objective lens, we calibrated the scanning mirror’s horizontal and vertical tilt angles. Additionally, to optimize the light throughput of each sub-aperture, we employed a rectangular iris instead of a round one at the aperture stop. The rectangular shape reduces the gaps between adjacent scanned pupil positions and allows more light to pass through a sub-aperture.To calibrate the central position of each projection line image at the sensor plane, we imaged a pinhole  positioned at the center of the FOV on the sample stage. We captured images of the pinhole at every projection angle To calibrate the spectral response, we positioned a pinhole  at the sample stage and illuminated it with monochromatic light at varied wavelengths. The resulting pixel locations of the projections were recorded and fitted with a linear polynomial, as illustrated in Fig. S7a. The slope of the line determines the spectral sampling of the system, which was calculated to be puted as :(12)wTo quantify the spatial resolution, we imaged a fluorescence bead with a diameter of 4 lated as :(13)wWe also imaged a group of bars from a USAF resolution target (Group 7 element 3-6) along both horizontal and vertical directions and plotted the intensities along the dashed line. The image visibility, defined as e.g., sum of modified Laplacian) for each pixel in the image [To calibrate the depth, we utilized a fluorescence bead  and translated it along the depth axis from he image . The besTo register the LIFT-FLIM and -sFLIM image with the reference camera, we imaged a 7 by 13 grid pattern. We then extracted the point locations from the reconstructed LIFT-FLIM and -sFLIM images and the reference image and calculated the homography matrix to establish a pixel-to-pixel correspondence between the two cameras. The reprojection error using the homography is less than one pixel, ensuring an accurate registration. This homography matrix is then used to register the LIFT-FLIM and -sFLIM images with reference images for deep learning reconstruction.To generate the ground truth depth image stack, we translated the sample stage along the depth axis while capturing images using the reference camera. The resulting image stack was then transformed to the LIFT-FLIM and -sFLIM camera coordinates using a homography matrix obtained through camera registration. We subtracted a background image from the warped image stack.The LIFT-FLIM and -sFLIM images were captured at depth The PixelCNN++ architecture  was adopHere it (ELU)  and SigmThe training loss of our model is a linear combination of Fourier domain mean absolute error (FDMAE) ,71, meanThe perceptual loss is defined as the sum of MSE losses between the feature maps of  network :(17)LOur models were implemented using PyTorch framework  on a macWe used a feature-based image stitching algorithm to create a panorama view of the human lung cancer pathology slide from multiple scanned FOVs with overlapping regions. This process involved detecting and matching image features, estimating the geometric transformation between images, and computing the transformation mapped each image onto the panorama. Moreover, to correct the artifacts in the stitched image caused by connecting the individual images, we applied an intensity averaging technique to the neighboring pixels at the artifact’s coordinates.To facilitate fast and accurate analysis of LIFT-FLIM and LIFT-sFLIM data, we utilized a phasor approach to unmix the underlying chromophores. In our experiments involving mouse kidney tissue sections and human lung cancer pathology slides, we reconstructed a multidimensional array lgorithm ,37 to deXYTS at each depth In LIFT-sFLIM, the image is represented by a multidimensional array Using this equation, we can calculate the amplitude fractions of the components in the mixture.Processing the raw histograms from SPAD involves three steps: background subtraction, delay correction, and nonlinear correction . First, in the background subtraction step, we collected the background signal under the same condition as experiments, which results from the dark count of the SPAD camera and stray light from the environment. The resulting background histogram was then subtracted from the raw histogram. Figure S8b shows the histograms after subtraction at two representative pixel locations, indicating that the zero references of histograms of the pixels are not aligned due to the delays in the FPGA from the input to the delay line not being matched . Delay cistogram . A correTo show the ground truth locations of individual biomarkers in our lung organoid experiment, we cultured another four sets of lung organoids under the same condition and labeled them with individual fluorophores. We then imaged the organoids using a standard confocal fluorescence microscope . Figure S9 presents the color-coded images, which display a similar appearance to our LIFT-sFLIM results.To create a mixed fluorescent bead sample, we embedded three types of fluorescent beads  into agarose gels. First, we diluted the bead suspensions and sonicated them. Then, we pipetted 10uL of the 4um bead suspension We prepared the agarose gel by making a We used a hydrostatic droplet generator to fabricate alginate microbead scaffolds with an average diameter of found in .+ population) were isolated from the distal tissue and used in this study.Human primary adult normal lung fibroblasts were isolated from distal lung tissue from a de-identified healthy donor  procured from the International Institute for the Advancement of Medicine (IIAM). Human lung tissue was procured under the UCLA-approved IRB protocol #16-000742. The fibroblast (crawled out population) and epithelial  bioreactor vessel  of https://github.com/iOpticsLab/LIFT-FLIMThe data of this study is available at https://github.com/iOpticsLab/LIFT-FLIMThe data of this study is available at


The ideal approach for calculating effective coverage of health services using ecological linking requires accounting for variability in facility readiness to provide health services and patient volume by incorporating adjustments for facility type into estimates of facility readiness and weighting facility readiness estimates by service-specific caseload. The aim of this study is to compare the ideal caseload-weighted facility readiness approach to two alternative approaches: (1) facility-weighted readiness and (2) observation-weighted readiness to assess the suitability of each as a proxy for caseload-weighted facility readiness. We utilised the 2014–2015 Tanzania Service Provision Assessment along with routine health information system data to calculate facility readiness estimates using the three approaches. We then conducted equivalence testing, using the caseload-weighted estimates as the ideal approach and comparing with the facility-weighted estimates and observation-weighted estimates to test for equivalence. Comparing the facility-weighted readiness estimates to the caseload-weighted readiness estimates, we found that 58% of the estimates met the requirements for equivalence. In addition, the facility-weighted readiness estimates consistently underestimated, by a small percentage, facility readiness as compared to the caseload-weighted readiness estimates. Comparing the observation-weighted readiness estimates to the caseload-weighted readiness estimates, we found that 64% of the estimates met the requirements for equivalence. We found that, in this setting, both facility-weighted readiness and observation-weighted readiness may be reasonable proxies for caseload-weighted readiness. However, in a setting with more variability in facility readiness or larger differences in facility readiness between low caseload and high caseload facilities, the observation-weighted approach would be a better option than the facility-weighted approach. While the methods compared showed equivalence, our results suggest that selecting the best method for weighting readiness estimates will require assessing data availability alongside knowledge of the country context. In low- and middle-income countries (LMICs), effective coverage estimates of health service provision are often generated by linking data from household surveys and health facility assessments (HFAs) . The ideHealth facilities within an administrative area are often of varying types  and as such they are often not equally ready to deliver care and they do not care for equal volumes of patients. It is important to account for this variability when generating linked estimates as it reduces bias and results in a closer approximation to an exact-match link. This can be accomplished by incorporating adjustments for facility type into estimates of facility readiness, where readiness refers to the capability of health facilities to provide a service of minimum acceptable standards, and is measured by the availability of both physical resources and human resources, and by weighting facility readiness estimates by service-specific caseload .Data on facility type is readily available in HFAs and incorporating adjustments for facility type into estimates of facility readiness is a recommended best practice for ecological linking. However, caseload data is not collected in standard HFA tools. It can also be difficult to obtain caseload data and link it to HFA data. The aim of this study is to use data from Tanzania to compare the ideal caseload-weighted facility readiness approach to two alternative approaches: (1) facility-weighted readiness and (2) HFA observation-weighted readiness, to assess the suitability of each approach as a proxy for caseload-weighted facility readiness.Box 1: Facility types in Tanzania Dispensary – Dispensaries are generally the first point of contact with the health care system and are staffed by a clinical assistant, often with the support of a nurse. Services provided at dispensaries include maternal and child health care, assistance with uncomplicated deliveries, and basic outpatient curative care.Health centre – Health centres supervise the dispensaries and are staffed by a clinical officer often with the assistance of a clinical assistant, maternal and child health aide, health aide, and a health assistant. Services provided at health centres include preventative care as well as curative care for common diseases and minor surgery.Clinic – Clinics are private primary-level health facilities that provide mostly outpatient curative services. They employ nurses/midwives, clinical officers, doctors, and pharmaceutical technicians.Hospital – This includes national referral hospitals, regional hospitals, district hospitals, and private hospitals. Secondary care is provided by district hospitals, which are staffed by medical doctors and assistant medical officers supported by clinical officers and nurses. District hospitals offer both inpatient and outpatient services not available at lower-level facilities including laboratory, imaging, and surgical services. Tertiary care is provided at regional hospitals and national referral hospitals, which are staffed by surgeons and medical doctors, as well as general and specialised nurses and midwives. They offer similar services as district hospitals; however, they are larger, employ specialists in various fields, and offer additional advanced services.Facility types in Tanzania include the following:The 2014–2015 TSPA was a health facility assessment that included a standard set of survey instruments: a facility inventory questionnaire, health worker interviews, observation of ANC consultations, and exit interviews with ANC clients. The survey was sampled to be nationally representative with health facilities selected using stratified systematic probability sampling with stratification by region and facility type  with oversampling of hospitals  project implementation.https://hfrportal.moh.go.tz/.The Health Facility Registry (HFR) is a web-based system used to provide public access to a database of information about all health facilities in mainland Tanzania. The HFR registry contains the name, region, and facility ID for each health facility and can be accessed at Data on the number of ANC4+ visits  was extracted from the Tanzania DHIS-2 portal per month and per facility for 2015.The data on ANC4+ visits was exported from DHIS-2 for each region to a .csv file. All non-facility-level entries were dropped , and duplicate entries were dropped. We then assessed the extent of missingness of ANC4+ data across the 12 months and created a binary variable with 0 indicating ≤5 months of missing data and 1 indicating >5 months of missing data. Facilities with more than five months of missing data were excluded from the analysis. For remaining facilities, missing data was imputed with the facility-specific mean value of ANC4+ visits . ANC4+ caseload was calculated as the total number of ANC4+ visits across the 12-month period (January–December 2015).We defined facility readiness as the human resources, equipment and supplies, diagnostics, medicines, and basic amenities essential to deliver a high-quality ANC service. A total of 19 items from the TSPA were selected to include in the facility readiness index. Details on item selection have been previously published [We used the HFR dataset to link the TSPA and DHIS-2 datasets. We merged the 2015 TSPA and DHIS-2 datasets using the HFR registry to identify facilities by both their name (TSPA) and facility identification code (DHIS-2).We aggregated estimates of facility quality over select categories of facility type, managing authority, and geographic unit aligning with different levels of aggregation that could be employed in ecological linking. Specifically, we generated aggregate quality scores by facility type, managing authority, urbanicity, and region. We limited the analysis to facilities that offered ANC services, had complete data for all facility readiness variables, and had at least 7 months of routine data available to calculate caseload. We weighted our aggregate facility readiness estimates using three approaches.The TSPA survey dataset includes survey weights  that are calculated by the DHS Program. The health facility weight is calculated based on the health facility selection probability, adjusted for non-response at the sampling stratum level. The complex sampling design employed by the TSPA results in an unequal probability of facility selection. The use of sampling weights addresses the difference between the survey design and simple random sampling and ensures that the contribution of facilities to the total is proportionate to their existence in the country. Sampling weights do not account for facility use or caseload. The facility-weighted approach used only the TSPA health facility weight to generate the weighted facility readiness estimate, scaling each facility’s contribution to align with the national distribution of facilities.For the observation-weighted approach, we approximated differentials in client volume by multiplying each client’s weight by the number of client observations. The client weight is calculated by taking the health facility weight multiplied by the inverse selection probability of clients within each of the sampling strata, adjusted for client non-response. By scaling the client weight by the number of observations, the contribution of each facility’s readiness is adjusted to approximate the client load on the day of the survey. In addition, we limited the client observation dataset to women for whom facility-level data was included in the analysis .For the caseload-weighted approach, we multiplied the health facility weight by the ANC4+ caseload estimate to generate a caseload weight. We applied these caseload weights when calculating mean facility readiness by category.All estimates were generated using the R ‘survey’ package . In addiThe 2014–2015 TSPA data set contained data from 1078 health facilities in mainland Tanzania, of which 949 provided ANC services. All 949 facilities offering ANC services had complete data for all facility readiness variables, while 860 facilities had at least 7 months of routine data available to calculate caseload. On average, each facility had an annual caseload of 45 ANC4+ clients. The annual health facility ANC4+ caseload ranged from 2 to 891. There was a total of 3466 ANC client consultations from 689 facilities observed in the 2014–2015 TSPA. On average, each facility had 5 ANC client observations. The number of ANC client observations at a health facility ranged from 1 to 15.Caseload was variable both across and within facility type. Clinics and dispensaries tended to have lower caseload, while health centres and hospitals tended to have comparatively higher caseload. However, within health centres and hospitals, there were facilities with both very low caseload and very high caseload .Figure R = 0.32) between caseload and the number of ANC observations at a health facility (R = 0.35).There was a weakly positive correlation  was not statistically significantly different and equivalent, which is what we would expect to see if the method was an exact approximation of the ideal caseload approach. Comparing the facility-weighted readiness estimates to the caseload-weighted readiness estimates disaggregated by managing authority, we found two out of four managing authority estimates were statistically significantly different, but equivalent with a difference between caseload-weighted and facility-weighted estimates of 3.7% to 3.8%. One estimate (private-for-profit) was not statistically significantly different and equivalent. One estimate  was statistically significantly different and not equivalent with a difference between caseload-weighted and facility-weighted estimates of 4.3%. This finding  demonstrates this is not a reasonable approximation of the ideal caseload approach. Comparing the observation-weighted readiness estimates to the caseload-weighted readiness estimates, we found two out of four estimates were statistically significantly different, but equivalent with a difference between caseload-weighted and observation-weighted estimates of 1.2 to 1.6%. Two estimates (government/public and private-for-profit) were not statistically significantly different and equivalent. All urban/rural estimates comparing the facility-weighted readiness estimates to the caseload-weighted readiness estimates and the observation-weighted readiness estimates to the caseload-weighted readiness estimates were statistically significantly different, but equivalent with a difference between caseload-weighted and facility-weighted estimates of 1.5 to 4.0% and a difference between caseload-weighted and observation-weighted estimates of −0.6% to 0.3%. Comparing the facility-weighted readiness estimates to the caseload-weighted readiness estimates disaggregated by region, we found that 11 out of 25 regional estimates were statistically significantly different, but equivalent with a difference between caseload-weighted and facility-weighted estimates of 1.7% to 3.6%. Four estimates were not statistically significantly different and equivalent. Fourteen estimates were statistically significantly different and not equivalent with a difference between caseload-weighted and facility-weighted estimates of −6.9% to 9.7%. Comparing the observation-weighted readiness estimates to the caseload-weighted readiness estimates, we found 11 out of 25 regional estimates were statistically significantly different, but equivalent with a difference between caseload-weighted and observation-weighted estimates of −3.7% to 3.8%. One estimate was not statistically significantly different and equivalent. Thirteen estimates were statistically significantly different and not equivalent with a difference between caseload-weighted and facility-weighted estimates of −14.3% to 7.9%.Looking across all estimates, comparing the facility-weighted readiness estimates to the caseload-weighted readiness estimates, we found 14% of the estimates were not statistically significantly different and were equivalent, which is what we would expect to see if the method was an exact approximation of the ideal caseload approach. An additional 44% of the estimates were statistically significantly different but were equivalent, which can be considered reasonable approximations of the ideal caseload approach. Combined, 58% of the estimates met the requirements for equivalence, while 42% of the estimates were statistically significantly different and not equivalent. The non-equivalent facility-weighted estimates were all regional estimates plus one managing authority estimate . The facility-weighted readiness estimates consistently underestimated, by a small percentage, facility readiness as compared to the caseload-weighted readiness estimates with 33 out of 36 (92%) of the facility-weighted readiness estimates being lower than the caseload-weighted readiness estimates.Looking across all estimates, comparing the observation-weighted readiness estimates to the caseload-weighted readiness estimates, 11% of the estimates were not statistically significantly different and were equivalent. An additional 53% of the estimates were statistically significantly different but were equivalent, for a total of 64% of the estimates meeting the requirements for equivalence. Thirty-six per cent of the estimates were statistically significantly different and not equivalent. The non-equivalent observation-weighted estimates were all regional estimates. There was no consistent trend in overestimation or underestimation when comparing the observation-weighted readiness estimates to the caseload-weighted readiness estimates.We found that, in this setting, both facility-weighted readiness and observation-weighted readiness may be reasonable proxies for caseload-weighted readiness, as the national readiness estimates and readiness estimates disaggregated by facility level, urbanicity, and managing authority all met the criteria for equivalency .However, we found that the facility-weighted estimates consistently underestimated facility readiness, albeit by a small percentage, which occurred because facilities with higher readiness scores (which tended to have higher caseloads) were consistently upweighted in the caseload-weighted approach. In a setting with more variability in facility readiness or larger differences in facility readiness between low caseload and high caseload facilities, this underestimation could be more pronounced, and the observation-weighted approach would be a better option than the facility-weighted approach.This study had a number of limitations. Data quality challenges with the routine data utilised for calculating caseload created an imperfect measure for our ‘ideal’ approach. We addressed some of the data quality issues by imputing missing data and excluding facilities with more than 40% missing data. Data quality is often a challenge for routinely collected data in LMICs, so this analysis likely reflects what is possible to achieve in other similar contexts ,14. ThisThis study has provided an important contribution to the growing evidence around best practices for generating effective coverage estimates. While the methods compared showed equivalence, our results suggest that selecting the best method for weighting readiness estimates will require assessing data availability alongside knowledge of the country context.


Medical image segmentation provides various effective methods for accuracy and robustness of organ segmentation, lesion detection, and classification. Medical images have fixed structures, simple semantics, and diverse details, and thus fusing rich multi-scale features can augment segmentation accuracy. Given that the density of diseased tissue may be comparable to that of surrounding normal tissue, both global and local information are critical for segmentation results. Therefore, considering the importance of multi-scale, global, and local information, in this paper, we propose the dynamic hierarchical multi-scale fusion network with axial mlp (multilayer perceptron) (DHMF-MLP), which integrates the proposed hierarchical multi-scale fusion (HMSF) module. Specifically, HMSF not only reduces the loss of detail information by integrating the features of each stage of the encoder, but also has different receptive fields, thereby improving the segmentation results for small lesions and multi-lesion regions. In HMSF, we not only propose the adaptive attention mechanism (ASAM) to adaptively adjust the semantic conflicts arising during the fusion process but also introduce Axial-mlp to improve the global modeling capability of the network. Extensive experiments on public datasets confirm the excellent performance of our proposed DHMF-MLP. In particular, on the BUSI, ISIC 2018, and GlaS datasets, IoU reaches 70.65%, 83.46%, and 87.04%, respectively. Because medical images are affected by equipment, the partial volume effect, and patient position movement, they inevitably have noise and artifacts. At the same time, the lesion areas are complex and diverse, which all present certain obstacles to the physician's diagnosis. As a result, the efficiency and accuracy of diagnosis have increased as doctors are assisted by computers.1 has played a huge role in the development of medical image segmentation. CNNs perform well in many segmentation tasks, such as multi-organ segmentation through abdominal CT images4, lesion detection7, cell segmentation10, heart segmentation13, etc. Unfortunately, for the segmentation of high-level networks, the feature maps contain less detail information due to their low resolution. For the low-level networks of segmentation, the feature maps have more noise. The low-level networks also have the characteristics of a small receptive field and weak semantic information representation abilities. However, both high-level semantic information and low-level features are extremely important to the final segmentation result. Effective multi-scale feature fusion contributes to identifying network segment targets more accurately, which is an important way to improve segmentation performance. In order to guide the segmentation of small lesions and multi-lesion regions and increase prediction accuracy, many CNNs have been proposed that fuse low-level features with high-level semantic information. For example, the pure convolutional network U-Net14 fuses low-level features into the up-sampling through skip connections. U-Net14 has become the baseline for most medical image segmentation tasks and has inspired a large number of researchers to think about U-shaped semantic segmentation networks. V-Net15, which is used for 3D image segmentation, also uses skip connections to transmit low-level features. However, these simple skip connections do not achieve cross-scale interaction. Later, it is proposed that U-Net++16 indirectly fuses features of several different levels through short skip connections and up-down sampling. MDU-Net17 extracts rich semantic information through multi-scale dense connection encoders, decoders, and skip connections. With the deepening of the network, the features of the deep network are greatly offset from the features of the shallow network, and direct feature fusion will lead to semantic conflicts. These conflicts inhibit the learning of detail information, which is not conducive to the establishment of context information for multi-scale features and has negative impacts on segmentation results.With the development of deep learning, the emergence of convolutional neural networks (CNNs)18 and Coordinate Attention19 use the generated weight sequence to explicitly build the dependency relationship between channels, so as to increase the sensitivity of the model to channel information and make channel information contribute more to the final decision. CBAM20 further combines channel attention with spatial attention and has better performance. However, these networks ignore the different proportions of foreground and background information for each feature map at different sampling stages.For the reasons outlined above, many researchers have proposed a variety of attention mechanisms to make networks focus on features of greater interest. SE-Net21 . In the ASAM module, the semantic conflicts between multi-scale features can be adjusted adaptively by learning parameters, and the noise inhibiting segmentation performance can be filtered out to enhance the attention of important features. Axial-mlp21 addresses the baseline's (UNeXt22) shortcoming in global information modeling with linear computational complexity.Based on the above analysis, we propose dynamic hierarchical multi-scale fusion network with axial mlp (DHMF-MLP) for medical image segmentation, in which we integrate the hierarchical multi-scale fusion (HMSF) module. We generate features with rich semantic and spatial information by fusing features from each stage of the encoder several times. To alleviate semantic conflicts in multi-scale feature fusion and enhance the ability to model the network globally, we propose dynamic spatial linear attention module (DSLA) as a component of HMSF. DSLA includes two parts: the adaptive spatial attention mechanism (ASAM) and the global branching in multi- gated MLPWe design the HMSF module, which achieves cross-level information interaction. HMSF not only improves the segmentation accuracy of small lesions and multi-lesion areas but also reduces the loss of information caused by pooling structures, fully improving the lack of up-sampling information.21, which enhances the global modeling capabilities of the network with less computation.We propose the DSLA module and apply it to the HMSF module. One part of the DSLA is the ASAM module, which adaptively adjusts the semantic conflict of multi-scale features with learnable parameters, filters out background noise that inhibits detail learning, and highlights foreground information appropriately. Another part of the DSLA is Axial-mlp22. Further advancements in global modeling capabilities allow for even better network segmentation performance.We achieve interaction between different layers, enriching the semantic information and reducing the conflict of fusing different features when compared to UNeXtThe effectiveness of our proposed network has been proven by experiments on three datasets. Compared with other networks, our network is highly competitive.The contribution of this paper can be summarized as follows:The remainder of this paper is organized as follows: “23 is a common fusion technique for medical imaging based on the spatial domain. Nevertheless, spectral and spatial distortion of the merged images are produced by spatial domain approaches. Researchers have moved their attention to the transform domain in an effort to improve the results of fusion. The contour transform24, discrete wavelet transform25, and pyramid transform26 are common examples. Although the transform domain-based approaches produce noise during the fusion process, they have the advantages of excellent structure and distortion avoidance. Better fusion results are obtained when the two procedures are combined. However, based on the traditional fusion methods, on the one hand, they are compelled to employ the same transform for various source images to extract features in order to guarantee the viability of subsequent feature fusion. The fact that this process disregards the variations in the source images' characteristics could result in a subpar representation of the extracted features. On the other hand, the performance of the conventional feature fusion technique is insufficient and too coarse. The technique for integrating deep learning into image fusion gets over these drawbacks of conventional approaches.Spatial domain, transform domain, and their combination make up traditional medical image fusion algorithms. Principal component analysis27 is the pioneer of CNNs for image segmentation, opening up a new era of encoder–decoder structure for image segmentation. Subsequently, U-Net14 combines encoder features from different levels to reduce information loss from pooling structures, achieve more accurate pixel boundary localization, and generate a plethora of efficient U-shaped segmentation network architectures29. Some researchers have further improved the structure of CNN-based networks, like Dilated Convolution31, RefineNet32, and PSPNet33.These networks are widely used in the field of image segmentation. However, due to the inherent characteristics of convolution, it lacks the ability to perform global context modeling.The emergence of CNNs has led to rapid development in the field of image segmentation. FCN18, ECANet34, and FcaNet35. The spatial attention mechanism is weighted along the spatial dimension so that the network can weaken background noise and pay more attention to the foreground information. For example, GE-Net36, RA-Net37, and SPA-Net38 make full use of spatial context information to make the network more efficient in mining regions of interest. However, these attention mechanisms do not take into account the dynamic relationship between foreground and background information for different scale features. Self-attention is a variant of the attention mechanism that aims to reduce dependence on external information and to use information inherent within features for the interaction of attention whenever possible. Like non-local39, the global context is modeled to effectively capture feature dependencies over long distances. OCR-Net40 models from a category perspective, which uses the results of coarse segmentation as the object of modeling and finally assigns weights to each query point. The bad news is that these calculations are relatively large.The attention mechanism is designed to focus the network on more important features. Channel attention is weighted by channel direction to automatically obtain the contribution of each channel to the segmentation result. The representative networks are SE-Net41 uses multilayer perceptron (MLP) to replace the convolution operation of CNN and the self-attention mechanism in Transformer44. MLP-Mixer41 builds contextual and inter-channel correlations between tokens through cross-position and per-position operations, respectively. gMLP45 achieves better performance than MLP-Mixer40 with fewer parameters and simpler gating mechanism. AS-MLP46 aligns different features to the same channel by parallel axial shift operations, and the full connection along the channel position achieves a cross-shaped field of view. AS-MLP46 is the first MLP architecture to migrate to downstream tasks. The above network does not balance model performance and computational redundancy. MAXIM21 employs multi-axis gated MLP to extract local and global information simultaneously, with cross-gated to achieve information interaction.MLP-MixerCompared to the above network, each layer of features in the DHMF-MLP encoder section interacts with its upper layer of features, reducing deviations between features. DHMF-MLP can not only adaptively adjust semantic conflicts in multi-scale feature fusion according to image properties but also extract perfect global context information.This section describes our proposed DHMF-MLP network as well as the research motivation. We will briefly introduce the overall architecture of the network in “22 performs well in local feature extraction, it underperforms in global contextual information. We cite the Axial-mlp21 to improve the overall network global modelling capability. Therefore, we propose the DHMF-MLP with the above issues fully in mind.As down-sampling proceeds, image information is lost and feature offsets can occur. By fusing encoder features layer by layer, interaction between higher-level features and their relative lower-level features can be achieved, and bias between features can be reduced. Semantic conflicts arise during the fusion process, and by adaptively adjusting the conflicts, consistent multi-scale feature sequences can be generated, facilitating the learning of important features. The fused features contain rich semantic information and are up-sampled from the decoder's bottom, reducing the semantic gap in the skip connections part and improving prediction accuracy. Furthermore, human tissues are highly similar, and both global and local information are critical. While UNeXtThe overall architecture of DHMF-MLP is shown in Fig. Tokenized to obtain ρ and The channel is divided into h parts for the input feature T, then axially shifted along the w-dimension and mentclass2pt{minimMLP along the channel dimension to map the number of channels into 768 dimensions, followed by 3 × 3 DWConv and GLEU to obtain entclass1pt{minima formula  and 4)  \documenIt is well known that the low-level features of the segmentation network contain more fine-grained information, which is helpful for the segmentation of small lesions. The deep segmentation network is able to extract more high-level semantic information, which can improve the accuracy of segmentation. Moreover, the rich multi-scale information, which fuses features with different receptive fields, facilitates the segmentation of multi-lesion regions.In this paper, we propose the HMSF module. The structure of the HMSF module is shown in Fig. 21 is performed to create global context to obtain , the formula :11:11\docum47: ultrasound images and corresponding segmentation images of normal, benign, and malignant breast cancer cases were collected. We use only benign and malignant images (647 images) and resize all images to 256 × 256. (2) International Skin Imaging Collaboration (ISIC 2018)48: the dataset consists of skin images containing cases and corresponding segmentation images of skin lesions, including a total of 2594 images. We resize all images to 512 × 512. (3) GlaS49: the dataset consists of 165 microscopic images of hematoxylin and eosin-stained slides, all of which are resized to 256 × 256.(1) Breast UltraSound Images (BUSI)22 loss function scaling, we adopt a combination of binary cross entropy (BCE) and dice loss (Dice) for training. The total loss L between prediction We utilize the Pytorch framework to develop DHMF-MLP. Consistent with the UNeXtWe use Adam optimizer to train the model with the initial learning rate of We exploit the IoU, Dice, SE, Acc, PPV, and SP segmentation metrics to quantify the segmentation ability of DHMF-MLP. For instance, IoU is used to assess the degree of similarity between prediction and ground truth. SE is a measure of the ability to correctly identify pixels that are not in the region of interest in a segmentation experiment. The formula is shown below:Figure 14, U-Net++ 16, U-Net3+ 28, Att-Unet29, and transformer-based network architectures, such as TransUnet42 and MedT50. We also make comparisons with UNeXt22, the network based on MLP. In the following, we will conduct the quantitative and qualitative analysis of the comparative test results. Moreover, the number of Parameters in each network is maintained to two decimal places.In order to further measure the effectiveness of the proposed DHMF-MLP network for lesion segmentation, we conduct comparative tests on the BUSI, ISIC 2018, and GlaS datasets. The network architectures used in our comparative experiments include the most advanced CNN-based networks, such as U-Net28 even outperforms the MedT50 network based on the transformer method, with the best PPV and SP. However, there is a big gap between the overall performance of the CNN-based methods and DHMF-MLP. IoU, Dice, SE, and ACC are 8.14%, 5.76%, 10.95%, and 0.59% higher than U-Net3+ 28, respectively. We note that the 4.54 M parameters of DHMF-MLP are also relatively low compared to the 26.97 M parameters of U-Net3+ 28. It shows that DHMF-MLP is efficient in its segmentation performance.The quantitative comparison results of the BUSI dataset on different methods are depicted in Table The qualitative comparison results of the BUSI dataset using different methods are presented in Fig. 42, based on the Transformer method, has the best PPV and SP. However, the number of DHMF-MLP parameters is very low compared to TransUnet42, which is also a relatively lightweight model. These experiments have verified the consistency of the foregoing.Results of the quantitative comparison of the ISIC 2018 dataset on different methods according to Table Figure 42 improves its performance by using CNN and Transformer to extract local and global contextual information, respectively. UNeXt22 achieves great competitive advantages by extracting excellent local features through CNN and shifted MLP. DHMF-MLP considers both local features and global feature extraction. Further better segmentation results from the medical image's own characteristics. Our proposed network (DHMF-MLP) has the best IoU, Dice, ACC, PPV and SP, which is 3.87%, 2.10%, 2.10%, 3.42%, 3.46% higher than UNeXt22. It should be noted that the proposed DHMF-MLP is also a relatively lightweight model, which is more feasible in clinical scenarios. There are huge advantages to these advanced methods.Table 16 and DHMF-MLP enable cross-scale interaction to reduce redundant information interference compared to U-Net14. From the second row of results, TransUnet42 and MedT49 combine the CNN with the Transformer and give better segmentation results of the junction of the lesion area compared to U-Net++ 16. DHMF-MLP further considers the feature conflict during fusion compared to TransUnet42 and proposes ASAM. As shown in Fig. Based on the above analysis of the characteristics of the GlaS dataset and the results of the qualitative analysis of the GlaS dataset in the first row of results in Fig. 22). (1) UNeXt22 framework; (2) DHMF-MLP without DSLA: our propose DHMF-MLP framework does not include DSLA module in its HMSF framework; (3) DHMF-MLP without ASAM: our propose DHMF-MLP framework with DSLA module without ASAM block; (4) DHMF-MLP without  Our proposed DHMF-MLP framework with DSLA modules does not have Axial-mlp21 blocks or learnable parameters. (5) DHMF-MLP without Axial-mlp21: our propose DHMF-MLP framework with DSLA module without Axial-mlp21 block; (6) DHMF-MLP: the DHMF-MLP framework is proposed by us. Tables To verify the individual contribution of each module in DHMF-MLP, we perform ablation experiments on three datasets and compare them with the baseline model  indicates the importance of the learnable parameters. In addition, DHMF-MLP without Axial-mlp21 and DHMF-MLP without ASAM improve the segmentation metrics essentially without increasing the number of parameters and by increasing the number of parameters by less, respectively. We conclude the lightness of ASAM and Axial-mlp and the necessity of applying them to the feature fusion process. When they are all applied to the network, the IoU (%) of BUSI, ISIC 2018, and GlaS increases by 4.76%, 1.10%, and 3.87%, respectively.From the quantitative results in Tables 21 enhances the boundary segmentation effect, validating the module's ability to improve the network's ability to extract global contextual information. Compared with DHMF-MLP without , DHMF-MLP without Axial-mlp21 takes into account the difference of foreground and background information of different scale features and automatically adjusts itself by using the learnable parameters. From the segmentation results of the two columns in Fig. Taking the first row of Fig. 22. That is, by fusing multi-scale features, rich semantic information is extracted, improving the segmentation effect. As shown in the second row of Fig. 21 achieve better boundary preservation results than DHMF-MLP without DSLA by utilizing global contextual information and adjusting for semantic conflicts that arise during the fusion process, respectively. The segmentation results from Fig. As is vividly depicted in the third line of Fig. 22. This is due to the better identification of lesion regions through the interactive learning of low-level and high-level features. According to the red box position in the first row shown in Fig. 21), DHMF-MLP without Axial-mlp21.According to the location of the red box in the second row shown in Fig. 21 to retrieve global contextual information. When they are all applied to the network, as shown in the last column of the qualitative analysis results, they compensate for each other's flaws, resulting in significant improvements in the segmentation effect.Through the above analysis, it can be seen that the quantitative results are consistent with the qualitative results. These experiments demonstrate the efficacy of our proposed method, which is exploited to extract rich multi-scale information for improving the accuracy of segmentation of small lesions and multi-lesion regions. Simultaneously, determine the feasibility of ASAM for adaptive learning of important features, as well as the necessity of Axial-mlp21, which is introduced to establish the global contextual information, fully compensates for the lack of global information at baseline and allows the fused feature information to be propagated so as to improve the overall performance of the network. Extensive experiments on three medical segmentation datasets have revealed that our proposed DHMF-MLP is competitive with current state-of-the-art methods. In the future, we will investigate the merits of the proposed DHMF-MLP on a wider range of datasets to improve the generalisation capability of the model.We propose a new medical image segmentation framework called DHMF-MLP. HMSF is proposed as part of the encoder, which contains three functions. First, the accuracy of small lesion and multi-locus region segmentation is improved by fusing features from each stage of the encoder to obtain rich semantic information and reduce the deviation between features. Second, lightweight ASAM is constructed by applying learnable parameters to calculate feature weights based on the foreground and background information of the feature map to adjust the semantic conflicts arising from feature fusion. Third, Axial-mlp


It is important for healthy aging to understand resilience in depth. This study aims to examine the dimensional structure underlying the Connor–Davidson Resilience Scale (CD-RISC) among Chinese older adults. Exploratory Graph Analysis (EGA) was used to evaluate the dimensional structure of CD-RISC in two large samples: training sample  and cross-validation sample (n = 7662). Then, Confirmatory Factor Analysis (CFA) was used to compare the fit of the theoretical dimensions with the EGA dimensions. Finially, Generalized Linear Model was used to examine the association between resilience scores and self-rated health (SRH) after controlling other covariates in order to evaluate the predictive value of the EGA dimensions. The EGA indicated two demensions(named foresight and self-adjustment) of the 25-item CD-RISC. The CFA comparison found that the two-demension structure of CD-RISC fit significantly better than the theoretical three-demension structure. After controlling for sociodemographic characteristics, generalized linear model showed that the EGA dimensions has better protective value with SRH. Compared with older adults with lowest quartile of foresight, those with second , third  and fourth quartile  of foresight had lower odds ratio of poor SRH. Similarly, older adults with the second  and fourth  quartile of self-adjustment also had lower OR of poor SRH than those with lowest quartile of self-adjustment. These findings show that EGA outperforms the traditional methods, which may be helpful to understand resilience deeply. CD-RISC should be interpreted into two aspects among community-dwelling older adults in China, highlighting the significance of the practical value and cultural context of resilience. American Psychological Association defined resilience as the process of adapting well and even growing in the face of adversity, stress, or trauma2. Research has shown that resilience functions as a buffer when people encountering with adversity or stressors, thus improving the ability to adapt to the environment and the efforts to exert control in front of obstacles3. During aging process, older adults may more frequently encounter with adversity or stressors including decline of cognitive and physical ability, retirement, death of a loved one, or changes of social network4. A better understanding of resilience across the life-course and how it manifests has arisen5. According to the healthy aging framework raised by World Health Organization (WHO), a higher level of resilience indicates better intrinsic capacity among older adults thus permiting them to apapt to environment and boost their functional capacity6. Previous studies indicated the negative association between resilience and adverse health outcomes, such as hospitalization8, frailty10, depression12, and chronic diseases15.In the past decade, research on resilience has drawn attention from a growing number of experts in psychology, psychopathology, sociology, biology, and cognitive neuroscience16, which captures different aspects of resilience. Such as, the Brief Resilience Scale (BRS) directly measures one's ability to bounce back or be resilient17, and the Resilience Scale for Adults (RSA) contains more intrapersonal protective factors18. CD-RISC focuses on resources that can help individuals to recover from and adapt to disruptions or stressful events. Previous research using CD-RISC-25 as a measurement found that in Chinese population, resilience was associated with quality of life22, depression25, frailty26, physical activity and sedentary behaviour27. CD-RISC-25 scale was prvoved to have sound psychometric properties among different populations, i.e., adolescents28, soldiers29, cancer30 and depressive patients31. However, the CD-RISC is not a stable multidimensional instrument for measuring resilience across the cultures and contexts of countries33 and lacked of robust replication34. The Chinese version of CD-RISC provide theoretically-based and psychometrically sound assessments of strength, optimism and tenacity34. Despite the preliminary promising findings, several methodological issues suggest that a reanalysis is warranted35. Firstly, cultural differences in item interpretation as well as differences in test settings and analytic strategy may have played a part in the varying factor structures found to date36. Different structures of CD-RISC were demonstrated due to the cultural context: a five-factor(25-item) and a one-factor (10-item) solution using a U.S. sample37, a three-factor solution using a Chinese sample34, and a four-factor solution using an Indian sample38. Also, items of CD-RISC are highly correlated, and the factors that emerged were in several cases difficult to interpret because they contained items with disparate themes35.Although aging research increasingly incorporates resilience, there is a considerable heterogeneity in the measurement of resilience and its scale structure39. Compared with the former, one of the data-driven methods, network analysis is advantageous in its capacity to accommodate a range of variable types and investigate the associations between behaviors and symptoms instead of constructs or domains40, which means network analysis may show more specific implications of a concept and explore the association with other related aspects. In 2017, Golino developed an innovative approach in exploratory factor analysis titled Exploratory Graph Analysis (EGA) and compared it with other traditional methods. From simulation of thousands of data sets, researchers found EGA outperforms traditional factor analytical and/or eigenvalue-based methods when there exist 2 factors of the scale and factors are highly related42. With a graphical display, EGA is robust especially in identifying the correct dimensionality when evaluating the instruments with multiple strongly correlated factors (as is the case of CD-RISC) or in large samples44.Research using psychometric-driven (administration of established questionnaires aimed at quantifying resilience) and data-driven method  are raised to operationalize resilience. However, none of the psychometric evaluations of resilience scales in older adults conducted to date are properly comprehensive, for example: there is no consensus as to the dimensionality of these scales’ latent structureThe emphasis of the present study is to verify the original dimensional structure of the CD-RISC-25 among a large sample of community-dwelling Chinese older adults and advance the knowledge regarding the factor structure underlying the CD-RISC structure by conducting a cutting-edge psychometric technique. Specifically, the objectives of this study are to (a) establish the factor structure of the CD-RISC with EGA, and (b) compare the results of the EGA method with its original structure both in Confirmatory Factor Analysis (CFA) and generalized linear models (GLM).10. 19,970 people were recruited from five cities in China: Shanghai (east of China), Zhuhai(south of China), Panzhihua(west of China), Ordos(north of China) and Hangzhou(east of China). After excluding participants with incomplete questionnaires, a total of 19,155 participants were included. Data was collected from face-to-face surveys using a self-administered questionnaire by trained interviewers. The written informed consent was obtained from all participants and study protocol was approved by the Ethics Committee for Medical Research at the School of Public Health, Fudan University (IRB00002408 & FWA00002399).This cross-sectional study was conducted among community-dwelling Chinese older adults during June 2020 and July 2022 . Sampling method and exclusion criterion has been reported in our previous published research articleWe measured demographic variables of age, gender, marital status and education level using a self-reported demographic checklist. Additionally, we measured the self-rated health (SRH) by asking the participant: "Recently, how do you feel about your health generally?", and the answer is a 5-point Likert response, from 1 (very bad) to 5 (very good). From this item, we merged "very bad" and "bad" into one category for very few participants (141) rating health as "very bad". We created a dichotomous outcome measure of SRH  for comparing the predictive value in 2 structures.34 was used in the current study, which measures 25 items of three dimensions of resilience: tenacity, strength and optimism. Tenacity refers to equanimity, promptness, perseverance, and sense of control when facing situations of hardship and challenge, and items are "When things look hopeless, I don't give up" and "Under pressure, I focus and think clearly" etc. Strength focuses on the individual's capacity of recovering and becoming strong after a setback and past experiences . Optimism reflects the individual's tendency to look on the positive sides of things and trust in one's personal and social resources. Participants were asked to respond to each item on a 5-point Likert scale, from 0  to 4 . The higher the score, the higher the level of psychological resilience.The Chinese version of the CD-RISC46. Despite the large sample size, here we used a novel method to explore the scale structure therefore remained training set as 70% of total participants to train the statistical model. According to 70:30, the total sample was randomly divided into two samples: the scale development sample (sample 1), consisting of 11,493 participants  and the validation sample (sample 2), consisting of 7662 participants . Descriptive statistics were obtained for both the total and the divided sample. We applied EGA in the sample 1 and sample 2 separately. Research has found bootEGA is a robust approach for identifying the stability and robustness of dimensionality in multivariate data which allows for the consistency of dimensions and items to be evaluated across bootstrapped EGA results, providing information about whether the data are consistently organized in coherent dimensions or fluctuate between dimensional configurations47. Here we use bootEGA to assess the stability of the EGA dimensionality estimates and item factor assignments across 1000 bootstrap samples. Third, EFA was adopted in sample 1. Finally, confirmatory factor analysis (CFA) was then adopted in sample 2 to compare the indices of both EGA and EFA dimensionalities. Good fit was determined by values of a comparative fit index (CFI) ≥ 0.95, standardized root mean residual (SRMR) ≤ 0.08, Tucker-Lewis index > 0.90 and root mean square error of approximation (RMSEA) ≤ 0.0548. The traditional χ2 difference test was not used because it is typically used to compare the fit of two nested models and sensitive to sample size, thus tends to give significant results with moderate-to-large sample sizes49. Network estimation and resampling was performed with the EGAnet package. To examine the practical value of new structure, we used generalized linear model (GLM) to examine the association between resilience dimensions from two structures with self-rated health as the dependent variable. The dimension scores of both structures were divided into quartiles to examine their associations with self-rated health.We firstly adopted exploratory factor analysis (EFA) on the total sample and deleted items with less than 0.3 item discrimination or standardized loading matrix < 0.4 if anyThe demographic distributions of the total and two separate sample are shown in Table χ2(300) = 431,501.5, P < 0.001) and parallel analysis suggested that the two factors structure was the model that best fit with the Chinese version of CD-RISC. After deleting any item in the scale, Cronbach’s α for the remaining items are all 0.97, which does not exceed 0.972. The mean inter-item-correlation was 0.591 and Cronbach’s α was 0.972 indicating items were internally consistent. Item discrimination criterion was set to 0.3 to check problematic items 50. Results showed all the 25 items were above the threshold of 0.3 as shown in Table For the total sample, we first adopted EFA (Kaiser–Meyer–Olkin (KMO): MSA = 0.98; Bartlett's sphericity test: We then calculated the correlation coefficients matrix and remove the item with coefficients lower than 0.4. In Model 1, we firstly put all of the 25 items and the results showed that for item 3 (d503), for the loading for both factor 1 and 2 were lower than 0.4. The loading pattern matrix in Model 2 found that after removal of the item 3, all the left 24 items showed proper loadings on the two factors. Factor 1 explained 54% variance and Factor 2 accounted for 46%. The results are shown in Table EGA was conducted respectively among sample 1 and sample 2 to check the consistency. As shown in Figs. The EGA analyses revealed two dimensions in the Chinese version of CD-RISC, we named as foresight and self-adjustment respectively. Notably, the item contents of 2 dimensions were identical in both samples. For the dimension of foresight, item 1, 2, 4–14, and 19 were largely connected to focus on reactions the individual adults imagine when encountering difficulties. The other dimension self-adjustment describes feelings of purpose and future orientation, consisting of item 15–18 and 20–25. The two dimensions are shown in different colors in Figs. bootEGA function in EGAnet package. The network structural results showed good stability in both samples. Bootstrapped results are shown in Figs. We bootstrapped results over 1000 samples using the After drawing the 2-factor structure from EGA, we compared the results with the original 3-factor structure by conducting CFA. As shown in Table To assess the predictive value of the two dimensional structures, GLM was also conducted between the resilience scores and self-rated health. According to previous findings, GLM models controlled the following covariates: age, gender, marital status, educational level, as shown in Table We adopted a novel method to explore the scale structure of Chinese version of CD-RISC in a large and representative sample and found that EGA produced a 2-factor structure (foresight and self-adjustment) that appeared preferably relevant for measuring the multidimensional nature of resilience and provided better data fit in indices of CFI, TLI, SRMR and RMSEA 95%CI). GLM results found that in the original structure, only strength and the 2 highest quantiles of optimism showed statistically significant value to predict SRH. In the dimension of tenacity, only the 4th quantile showed preventive effect on participants with lower SRH status. In the results of EGA structure, only the 3rd quantile of self-adjustment didn’t show predictive effect on older adults with poor SRH  and after  distress may explain the resilience distribution in aging population. Additionally, previous validation in industry population and patient sample considered the number of items and volunteering bias, both of the above mentioned studies have reduced item numbers for making it much easier and less time consuming for respondents to answer the questions. Our study was based on a representative aging sample in China and the sample size may be efficient to genrelize resilience traits in Chinese older adults.In 2003, Connor and Davidson firstly developed a 5-factor scale named as Connor-Davidson Resilience Scale (CD-RISC-25)58, human motivation and action are regulated by foresight, and this cognitive control of behavior is based on the self-efficacy expectations, which are the individuals’ beliefs in their capabilities to perform a course of action to attain a desired outcome. Several studies revealed that self-efficacy was a significant factor explaining resilience62, and resilience-building interventions targeted at older adults appeared effective in improving self-efficacy in at-risk older people63. The other 3 items in this dimension  described the buffing roles of resources. For example, item 2 "close and secure relationship" and item 13 "know where to turn for help" are related to social support, which has been discussed in other findings66, and item 5 "past success gives confidence for new challenge" concerned about the self confidence which may provide coping straregies. Zapater etc. found that under stressful condition, active coping strategies moderated a conditional indirect effect of resilience and cortisol reactivity among the older participants67. The other studies also implied that when facing the crisis of COVID-19, those older adults who better understood and promoted late-life coping  were more resilient and less suffered from loneliness and sleep problems70. A review of resilience concept found older people who have the ability to use personal resources and see the world beyond their own concerns are more likely to be resilient71. The protective model of resilience was supported for goal orientation and self-confidence. Based on it, Askeland etc. found that goal orientation and self-confidence showed small moderating effects between negative life events and depressive symptoms72.Herein we name the two dimensions as foresight and self-adjustment. As we found in EGA results, one dimension includes more items related closely with foresight. For example, item 1, 4, 6–12,14 and 19 depicted the foresight of an individual when encountering a difficult situation. According to Social Cognitive Theory73. When people start doing something that may be difficult, their self-adjustment ability is affected by differences in stress ability, emotional response, and efforts to avoid negative behavior74. Herein we found the items in self-adjustment relates to self-realization and future orientation, which has been discussed elsewhere77. Self-realization and resilience were both aspects of well-being, found to correlate with disease self-management or difficulties among the older adults78. Studies elsewhere found self-realization was associated with quality of life positively and depression negatively79. As part of the psychological needs, when self-realization is not met, psychological state of the older people may change, which in turn affects their mental health80. As a self-initiated ability, future orientation (will to live) may help the development of resilience among maltreated youth81, children and older adults aged above 50 affected by HIV83 and the youth facing disasters84. A systematic review suggested that as transition to older age can challenge people's sense of self and their role in life85. One study foucused on older people in post-labor period of life found that older adults with higher self-realization and a moreoptimistic attitude to the future tend to enjoy beteer mental health status. As one study pointed that the resilience in older age was tied to an existential drive to create meaning in life and move toward a sense of self-fulfillment86, future interventions on how to help regain feelings of purpose and a fulfilling older age may help improve their resilience.The other dimension self-adjustment includes item 15–18 and 20–25, which emphasized self-realization  and the facet of future orientation . Self-adjustment generally equals to self-reinforcing, referring the process of reinforcing and maintaining one’s behavior with rewards that can be controlled when people achieve their own standardsFinally, two limitations should be noted. First, we only used samples of older adults from China. Whether the findings can be generalized to samples from other countries should be further examined. Second, we only included measurement of subjective well-being as the outcome. Future studies should investigate whether the CD-RISC also has unique predictive effects on other well-being or health-related outcomes.In this study we conducted an in‐depth psychometric investigation of the Chinese version of CD-RISC using traditional (EFA and CFA) and contemporary (EGA) exploratory techniques. Although the scale was developed to assess three categories of resilience, our findings do not support the hypothesis that the scale delivers a psychometrically consistent measure of adult attachment styles. The results of the present study support the assumption that a two‐dimension approach  to assess resilience among older adults is optimal.The Ethics Committee for Medical Research at a university approved the study (2019-04-0741). Written informed consent was obtained from all participants in the study. All methods were carried out in accordance with relevant guidelines and regulations.Supplementary Information.