A thalamic-primary auditory cortex circuit mediates resilience to stress.

Resilience enables mental elasticity in individuals when rebounding from adversity. In this study, we identified a microcircuit and relevant molecular adaptations that play a role in natural resilience. We found that activation of parvalbumin (PV) interneurons in the primary auditory cortex (A1) by thalamic inputs from the ipsilateral medial geniculate body (MG) is essential for resilience in mice exposed to chronic social defeat stress. Early attacks during chronic social defeat stress induced short-term hyperpolarizations of MG neurons projecting to the A1 (MGA1 neurons) in resilient mice. In addition, this temporal neural plasticity of MGA1 neurons initiated synaptogenesis onto thalamic PV neurons via presynaptic BDNF-TrkB signaling in subsequent stress responses. Moreover, optogenetic mimicking of the short-term hyperpolarization of MGA1 neurons, rather than merely activating MGA1 neurons, elicited innate resilience mechanisms in response to stress and achieved sustained antidepressant-like effects in multiple animal models, representing a new strategy for targeted neuromodulation.

MicroRNA-antagonism regulates breast cancer stemness and metastasis via TET-family-dependent chromatin remodeling.

Tumor cells metastasize to distant organs through genetic and epigenetic alterations, including changes in microRNA (miR) expression. Here we find miR-22 triggers epithelial-mesenchymal transition (EMT), enhances invasiveness and promotes metastasis in mouse xenografts. In a conditional mammary gland-specific transgenic (TG) mouse model, we show that miR-22 enhances mammary gland side-branching, expands the stem cell compartment, and promotes tumor development. Critically, miR-22 promotes aggressive metastatic disease in MMTV-miR-22 TG mice, as well as compound MMTV-neu or -PyVT-miR-22 TG mice. We demonstrate that miR-22 exerts its metastatic potential by silencing antimetastatic miR-200 through direct targeting of the TET (Ten eleven translocation) family of methylcytosine dioxygenases, thereby inhibiting demethylation of the mir-200 promoter. Finally, we show that miR-22 overexpression correlates with poor clinical outcomes and silencing of the TET-miR-200 axis in patients. Taken together, our findings implicate miR-22 as a crucial epigenetic modifier and promoter of EMT and breast cancer stemness toward metastasis.

Depletion of a putatively druggable class of phosphatidylinositol kinases inhibits growth of p53-null tumors.

Here, we show that a subset of breast cancers express high levels of the type 2 phosphatidylinositol-5-phosphate 4-kinases α and/or ß (PI5P4Kα and ß) and provide evidence that these kinases are essential for growth in the absence of p53. Knocking down PI5P4Kα and ß in a breast cancer cell line bearing an amplification of the gene encoding PI5P4K ß and deficient for p53 impaired growth on plastic and in xenografts. This growth phenotype was accompanied by enhanced levels of reactive oxygen species (ROS) leading to senescence. Mice with homozygous deletion of both TP53 and PIP4K2B were not viable, indicating a synthetic lethality for loss of these two genes. Importantly however, PIP4K2A(-/-), PIP4K2B(+/-), and TP53(-/-) mice were viable and had a dramatic reduction in tumor formation compared to TP53(-/-) littermates. These results indicate that inhibitors of PI5P4Ks could be effective in preventing or treating cancers with mutations in TP53.

Mediation analysis in longitudinal study with high-dimensional methylation mediators.

Mediation analysis has been widely utilized to identify potential pathways connecting exposures and outcomes. However, there remains a lack of analytical methods for high-dimensional mediation analysis in longitudinal data. To tackle this concern, we proposed an effective and novel approach with variable selection and the indirect effect (IE) assessment based on both linear mixed-effect model and generalized estimating equation. Initially, we employ sure independence screening to reduce the dimension of candidate mediators. Subsequently, we implement the Sobel test with the Bonferroni correction for IE hypothesis testing. Through extensive simulation studies, we demonstrate the performance of our proposed procedure with a higher F$_{1}$ score (0.8056 and 0.9983 at sample sizes of 150 and 500, respectively) compared with the linear method (0.7779 and 0.9642 at the same sample sizes), along with more accurate parameter estimation and a significantly lower false discovery rate. Moreover, we apply our methodology to explore the mediation mechanisms involving over 730 000 DNA methylation sites with potential effects between the paternal body mass index (BMI) and offspring growing BMI in the Shanghai sleeping birth cohort data, leading to the identification of two previously undiscovered mediating CpG sites.

N6-Methyladenosine Guides mRNA Alternative Translation during Integrated Stress Response.

The integrated stress response (ISR) facilitates cellular adaptation to stress conditions via the common target eIF2α. During ISR, the selective translation of stress-related mRNAs often relies on alternative mechanisms, such as leaky scanning or reinitiation, but the underlying mechanism remains incompletely understood. Here we report that, in response to amino acid starvation, the reinitiation of ATF4 is not only governed by the eIF2α signaling pathway, but is also subjected to regulation by mRNA methylation in the form of N6-methyladenosine (m6A). While depleting m6A demethylases represses ATF4 reinitiation, knocking down m6A methyltransferases promotes ATF4 translation. We demonstrate that m6A in the 5' UTR controls ribosome scanning and subsequent start codon selection. Global profiling of initiating ribosomes reveals widespread alternative translation events influenced by dynamic mRNA methylation. Consistently, Fto transgenic mice manifest enhanced ATF4 expression, highlighting the critical role of m6A in translational regulation of ISR at cellular and organismal levels.

Learning locality-sensitive bucketing functions.

MOTIVATION: Many tasks in sequence analysis ask to identify biologically related sequences in a large set. The edit distance, being a sensible model for both evolution and sequencing error, is widely used in these tasks as a measure. The resulting computational problem-to recognize all pairs of sequences within a small edit distance-turns out to be exceedingly difficult, since the edit distance is known to be notoriously expensive to compute and that all-versus-all comparison is simply not acceptable with millions or billions of sequences. Among many attempts, we recently proposed the locality-sensitive bucketing (LSB) functions to meet this challenge. Formally, a (d1,d2)-LSB function sends sequences into multiple buckets with the guarantee that pairs of sequences of edit distance at most d1 can be found within a same bucket while those of edit distance at least d2 do not share any. LSB functions generalize the locality-sensitive hashing (LSH) functions and admit favorable properties, with a notable highlight being that optimal LSB functions for certain (d1,d2) exist. LSB functions hold the potential of solving above problems optimally, but the existence of LSB functions for more general (d1,d2) remains unclear, let alone constructing them for practical use. RESULTS: In this work, we aim to utilize machine learning techniques to train LSB functions. With the development of a novel loss function and insights in the neural network structures that can potentially extend beyond this specific task, we obtained LSB functions that exhibit nearly perfect accuracy for certain (d1,d2), matching our theoretical results, and high accuracy for many others. Comparing to the state-of-the-art LSH method Order Min Hash, the trained LSB functions achieve a 2- to 5-fold improvement on the sensitivity of recognizing similar sequences. An experiment on analyzing erroneous cell barcode data is also included to demonstrate the application of the trained LSB functions. AVAILABILITY AND IMPLEMENTATION: The code for the training process and the structure of trained models are freely available at https://github.com/Shao-Group/lsb-learn.

Structural and functional basis of low-affinity SAM/SAH-binding in the conserved MTase of the multi-segmented Alongshan virus distantly related to canonical unsegmented flaviviruses.

Alongshan virus (ALSV), a newly discovered member of unclassified Flaviviridae family, is able to infect humans. ALSV has a multi-segmented genome organization and is evolutionarily distant from canonical mono-segmented flaviviruses. The virus-encoded methyltransferase (MTase) plays an important role in viral replication. Here we show that ALSV MTase readily binds S-adenosyl-L-methionine (SAM) and S-adenosyl-L-homocysteine (SAH) but exhibits significantly lower affinities than canonical flaviviral MTases. Structures of ALSV MTase in the free and SAM/SAH-bound forms reveal that the viral enzyme possesses a unique loop-element lining side-wall of the SAM/SAH-binding pocket. While the equivalent loop in flaviviral MTases half-covers SAM/SAH, contributing multiple hydrogen-bond interactions; the pocket-lining loop of ALSV MTase is of short-length and high-flexibility, devoid of any physical contacts with SAM/SAH. Subsequent mutagenesis data further corroborate such structural difference affecting SAM/SAH-binding. Finally, we also report the structure of ALSV MTase bound with sinefungin, an SAM-analogue MTase inhibitor. These data have delineated the basis for the low-affinity interaction between ALSV MTase and SAM/SAH and should inform on antiviral drug design.

Development of a bispecific nanobody conjugate broadly neutralizes diverse SARS-CoV-2 variants and structural basis for its broad neutralization.

The continuous emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with increased transmissibility and profound immune-escape capacity makes it an urgent need to develop broad-spectrum therapeutics. Nanobodies have recently attracted extensive attentions due to their excellent biochemical and binding properties. Here, we report two high-affinity nanobodies (Nb-015 and Nb-021) that target non-overlapping epitopes in SARS-CoV-2 S-RBD. Both nanobodies could efficiently neutralize diverse viruses of SARS-CoV-2. The neutralizing mechanisms for the two nanobodies are further delineated by high-resolution nanobody/S-RBD complex structures. In addition, an Fc-based tetravalent nanobody format is constructed by combining Nb-015 and Nb-021. The resultant nanobody conjugate, designated as Nb-X2-Fc, exhibits significantly enhanced breadth and potency against all-tested SARS-CoV-2 variants, including Omicron sub-lineages. These data demonstrate that Nb-X2-Fc could serve as an effective drug candidate for the treatment of SARS-CoV-2 infection, deserving further in-vivo evaluations in the future.

Extracellular vesicle surface display enhances the therapeutic efficacy and safety profile of cancer immunotherapy.

Immunotherapy has emerged as a mainstay in cancer therapy, yet its efficacy is constrained by the risk of immune-related adverse events. In this study, we present a nanoparticle-based delivery system that enhances the therapeutic efficacy of immunomodulatory ligands while concurrently limiting systemic toxicity. We demonstrate that extracellular vesicles (EVs), lipid bilayer enclosed particles released by cells, can be efficiently engineered via inverse electron demand Diels-Alder (iEDDA)-mediated conjugation to display multiple immunomodulatory ligands on their surface. Display of immunomodulatory ligands on the EV surface conferred substantial enhancements in signaling efficacy, particularly for tumor necrosis factor receptor superfamily (TNFRSF) agonists, where the EV surface display served as an alternative FcγR-independent approach to induce ligand multimerization and efficient receptor crosslinking. EVs displaying a complementary combination of immunotherapeutic ligands were able to shift the tumor immune milieu toward an anti-tumorigenic phenotype and significantly suppress tumor burden and increase survival in multiple models of metastatic cancer to a greater extent than an equivalent dose of free ligands. In summary, we present an EV-based delivery platform for cancer immunotherapeutic ligands that facilitates superior anti-tumor responses at significantly lower doses with fewer side effects than is possible with conventional delivery approaches.

A novel diabetic foot ulcer diagnostic model: identification and analysis of genes related to glutamine metabolism and immune infiltration.

BACKGROUND: Diabetic foot ulcer (DFU) is one of the most common and severe complications of diabetes, with vascular changes, neuropathy, and infections being the primary pathological mechanisms. Glutamine (Gln) metabolism has been found to play a crucial role in diabetes complications. This study aims to identify and validate potential Gln metabolism biomarkers associated with DFU through bioinformatics and machine learning analysis. METHODS: We downloaded two microarray datasets related to DFU patients from the Gene Expression Omnibus (GEO) database, namely GSE134431, GSE68183, and GSE80178. From the GSE134431 dataset, we obtained differentially expressed Gln-metabolism related genes (deGlnMRGs) between DFU and normal controls. We analyzed the correlation between deGlnMRGs and immune cell infiltration status. We also explored the relationship between GlnMRGs molecular clusters and immune cell infiltration status. Notably, WGCNA to identify differentially expressed genes (DEGs) within specific clusters. Additionally, we conducted GSVA to annotate enriched genes. Subsequently, we constructed and screened the best machine learning model. Finally, we validated the predictions' accuracy using a nomogram, calibration curves, decision curve analysis (DCA), and the GSE134431, GSE68183, and GSE80178 dataset. RESULTS: In both the DFU and normal control groups, we confirmed the presence of deGlnMRGs and an activated immune response. From the GSE134431 dataset, we obtained 20 deGlnMRGs, including CTPS1, NAGS, SLC7A11, GGT1, GCLM, RIMKLA, ARG2, ASL, ASNS, ASNSD1, PPAT, GLS2, GLUD1, MECP2, ASS1, PRODH, CTPS2, ALDH5A1, DGLUCY, and SLC25A12. Furthermore, two clusters were identified in DFU. Immune infiltration analysis indicated the presence of immune heterogeneity in these two clusters. Additionally, we established a Support Vector Machine (SVM) model based on 5 genes (R3HCC1, ZNF562, MFN1, DRAM1, and PTGDS), which exhibited excellent performance on the external validation datasetGSE134431, GSE68183, and GSE80178 (AUC = 0.929). CONCLUSION: This study has identified five Gln metabolism genes associated with DFU, revealing potential novel biomarkers and therapeutic targets for DFU. Additionally, the infiltration of immune-inflammatory cells plays a crucial role in the progression of DFU.

The Impact of Electron Donating and Withdrawing Groups on Electrochemical Hydrogenolysis and Hydrogenation of Carbonyl Compounds.

The hydrogenolysis or hydrodeoxygenation of a carbonyl group, where the C═O group is converted to a CH2 group, is of significant interest in a variety of fields. A challenge in electrochemically achieving hydrogenolysis of a carbonyl group with high selectivity is that electrochemical hydrogenation of a carbonyl group, which converts the C═O group to an alcohol group (CH-OH), is demonstrated not to be the initial step of hydrogenolysis. Instead, hydrogenation and hydrogenolysis occur in parallel, and they are competing reactions. This means that although both hydrogenolysis and hydrogenation require adding H atoms to the carbonyl group, they involve different intermediates formed on the electrode surface. Thus, revealing the difference in intermediates, transition states, and kinetic barriers for hydrogenolysis and hydrogenation pathways is the key to understanding and controlling hydrogenolysis/hydrogenation selectivity of carbonyl compounds. In this study, we aimed to identify features of reactant molecules that can affect their hydrogenolysis/hydrogenation selectivity on a Zn electrode that was previously shown to promote hydrogenolysis over hydrogenation. In particular, we examined the electrochemical reduction of para-substituted benzaldehyde compounds with substituent groups having different electron donating/withdrawing abilities. Our results show a strikingly systematic impact of the substituent group where a stronger electron-donating group promotes hydrogenolysis and a stronger electron-withdrawing group promotes hydrogenation. These experimental results are presented with computational results explaining the substituent effects on the thermodynamics and kinetics of electrochemical hydrogenolysis and hydrogenation pathways, which also provide critically needed information and insights into the transition states involved with these pathways.

Discovery of vitexin as a novel VDR agonist that mitigates the transition from chronic intestinal inflammation to colorectal cancer.

Colitis-associated colorectal cancer (CAC) frequently develops in patients with inflammatory bowel disease (IBD) who have been exposed to a prolonged state of chronic inflammation. The investigation of pharmacological agents and their mechanisms to prevent precancerous lesions and inhibit their progression remains a significant focus and challenge in CAC research. Previous studies have demonstrated that vitexin effectively mitigates CAC, however, its precise mechanism of action warrants further exploration. This study reveals that the absence of the Vitamin D receptor (VDR) accelerates the progression from chronic colitis to colorectal cancer. Our findings indicate that vitexin can specifically target the VDR protein, facilitating its translocation into the cell nucleus to exert transcriptional activity. Additionally, through a co-culture model of macrophages and cancer cells, we observed that vitexin promotes the polarization of macrophages towards the M1 phenotype, a process that is dependent on VDR. Furthermore, ChIP-seq analysis revealed that vitexin regulates the transcriptional activation of phenazine biosynthesis-like domain protein (PBLD) via VDR. ChIP assays and dual luciferase reporter assays were employed to identify the functional PBLD regulatory region, confirming that the VDR/PBLD pathway is critical for vitexin-mediated regulation of macrophage polarization. Finally, in a mouse model with myeloid VDR gene knockout, we found that the protective effects of vitexin were abolished in mid-stage CAC. In summary, our study establishes that vitexin targets VDR and modulates macrophage polarization through the VDR/PBLD pathway, thereby alleviating the transition from chronic colitis to colorectal cancer.

Single-cell RNA sequencing reveals the transcriptional heterogeneity of Tbx18-positive cardiac cells during heart development.

The T-box family transcription factor 18 (Tbx18) has been found to play a critical role in regulating the development of the mammalian heart during the primary stages of embryonic development while the cellular heterogeneity and landscape of Tbx18-positive (Tbx18+) cardiac cells remain incompletely characterized. Here, we analyzed prior published single-cell RNA sequencing (scRNA-seq) mouse heart data to explore the heterogeneity of Tbx18+ cardiac cell subpopulations and provide a comprehensive transcriptional landscape of Tbx18+ cardiac cells during their development. Bioinformatic analysis methods were utilized to identify the heterogeneity between cell groups. Based on the gene expression characteristics, Tbx18+ cardiac cells can be classified into a minimum of two distinct cell populations, namely fibroblast-like cells and cardiomyocytes. In terms of temporal heterogeneity, these cells exhibit three developmental stages, namely the MEM stage, ML_P0 stage, and P stage Tbx18+ cardiac cells. Furthermore, Tbx18+ cardiac cells encompass several cell types, including cardiac progenitor-like cells, cardiomyocytes, and epicardial/stromal cells, as determined by specific transcriptional regulatory networks. The scRNA-seq results revealed the involvement of extracellular matrix (ECM) signals and epicardial epithelial-to-mesenchymal transition (EMT) in the development of Tbx18+ cardiac cells. The utilization of a lineage-tracing model served to validate the crucial function of Tbx18 in the differentiation of cardiac cells. Consequently, these findings offer a comprehensive depiction of the cellular heterogeneity within Tbx18+ cardiac cells.

Magnetic Microbead-Based Herringbone Chip for Sensitive Detection of Human Immunodeficiency Virus.

The microfluidic chip-based nucleic acid detection method significantly improves the sensitivity since it precisely controls the microfluidic flow in microchannels. Nonetheless, significant challenges still exist in improving the detection efficiency to meet the demand for rapid detection of trace substances. This work provides a novel magnetic herringbone (M-HB) structure in a microfluidic chip, and its advantage in rapid and sensitive detection is verified by taking complementary DNA (cDNA) sequences of human immunodeficiency virus (HIV) detection as an example. The M-HB structure is designed based on controlling the magnetic field distribution in the micrometer scale and is formed by accumulation of magnetic microbeads (MMBs). Hence, M-HB is similar to a nanopore microstructure, which has a higher contact area and probe density. All of the above is conducive to improving sensitivity in microfluidic chips. The M-HB chip is stable and easy to form, which can linearly detect cDNA sequences of HIV quantitatively ranging from 1 to 20 nM with a detection limit of 0.073 nM. Compared to the traditional herringbone structure, this structure is easier to form and release by controlling the magnetic field, which is flexible and helps in further study. Results show that this chip can sensitively detect the cDNA sequences of HIV in blood samples, demonstrating that it is a powerful platform to rapidly and sensitively detect multiple nucleic acid-related viruses of infectious diseases.

Integrated analysis reveals ceRNA network of cardiac remodeling by SGLT2 inhibitor in middle-aged hypertensive rats.

Sodium-glucose cotransporter 2 inhibitors (SGLT2i) represent an innovative class of antidiabetic agents that have demonstrated promise in mitigating cardiac remodeling. However, the transcriptional regulatory mechanisms underpinning their impact on blood pressure and the reversal of hypertension-induced cardiac remodeling remain largely unexplored. Given this context, our study concentrated on comparing the cardiac expression profiles of lncRNAs and mRNAs between Wistar-Kyoto (WKY) rats and spontaneously hypertensive rats (SHR). To validate our results, we performed blood pressure measurements, tissue staining, and qRT-PCR. The treatment led to a significant reduction in systolic blood pressure and improved cardiac remodeling by reducing myocardial fibrosis and regulating the inflammatory response. Our examination disclosed that ventricular tissue mRNA, regulated by hypertension, was primarily concentrated in the complement and coagulation cascades and cytokine-cytokine receptor interactions. Compared with SHR, the SGLT2i treatment group was associated with myocardial contraction. Investigation into the lncRNA-mRNA regulatory network and competing endogenous RNA (ceRNA) network suggested that the potential roles of these differentially expressed (DE) lncRNAs and mRNAs were tied to processes such as collagen fibril organization, inflammatory response, and extracellular matrix (ECM) modifications. We found that the expression of Col3a1, C1qa, and lncRNA NONRATT007139.2 were altered in the SHR group and that SGLT2i treatment reversed these changes. Our results suggest that dapagliflozin effectively reverses hypertension-induced myocardial remodeling through a lncRNA-mRNA transcriptional regulatory network, with immune cell-mediated ECM deposition as a potential regulatory target. This underlines the potentiality of SGLT2i and genes related to immunity as promising targets for the treatment of hypertension.

Predicting long-term progression of Alzheimer's disease using a multimodal deep learning model incorporating interaction effects.

BACKGROUND: Identifying individuals with mild cognitive impairment (MCI) at risk of progressing to Alzheimer's disease (AD) provides a unique opportunity for early interventions. Therefore, accurate and long-term prediction of the conversion from MCI to AD is desired but, to date, remains challenging. Here, we developed an interpretable deep learning model featuring a novel design that incorporates interaction effects and multimodality to improve the prediction accuracy and horizon for MCI-to-AD progression. METHODS: This multi-center, multi-cohort retrospective study collected structural magnetic resonance imaging (sMRI), clinical assessments, and genetic polymorphism data of 252 patients with MCI at baseline from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our deep learning model was cross-validated on the ADNI-1 and ADNI-2/GO cohorts and further generalized in the ongoing ADNI-3 cohort. We evaluated the model performance using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and F1 score. RESULTS: On the cross-validation set, our model achieved superior results for predicting MCI conversion within 4 years (AUC, 0.962; accuracy, 92.92%; sensitivity, 88.89%; specificity, 95.33%) compared to all existing studies. In the independent test, our model exhibited consistent performance with an AUC of 0.939 and an accuracy of 92.86%. Integrating interaction effects and multimodal data into the model significantly increased prediction accuracy by 4.76% (P = 0.01) and 4.29% (P = 0.03), respectively. Furthermore, our model demonstrated robustness to inter-center and inter-scanner variability, while generating interpretable predictions by quantifying the contribution of multimodal biomarkers. CONCLUSIONS: The proposed deep learning model presents a novel perspective by combining interaction effects and multimodality, leading to more accurate and longer-term predictions of AD progression, which promises to improve pre-dementia patient care.

Roles of vaporization and thermal decomposition in the dynamic evolution of laser-induced bubble on the surface of a submerged metal plate.

This paper aims to explain when the vaporization or thermal decomposition prevails during laser-induced bubble growth and how they influence bubble morphology. Bubbles were generated by irradiating a 304 stainless steel plate submerged in degassed water using millisecond lasers with a pulse width of 0.4 ms and powers of 1.6 kW and 3.2 kW, respectively. The dynamic evolution of bubbles was recorded by a high-speed camera. Moreover, the numerical models were developed to obtain a vaporization model and a decomposition model by incorporating the source terms due to the vaporization and decomposition mass fluxes into the governing equations, respectively. The simulated dynamic bubble evolution is consistent with the experimental results. When the laser power is 1.6 kW, a thin-layer bubble is formed, which gradually shrinks and eventually disappears after the laser stops irradiating. When the laser power is 3.2 kW, a spherical bubble is formed, and its volume decreases significantly after the laser stops irradiating. Subsequently, it remains relatively stable during the observation period. The fundamental reason for the difference between the bubble morphologies obtained from the vaporization model and the decomposition model lies in the presence of a condensation zone in the gas phase. When water vaporization or thermal decomposition dominates, the temperatures obtained from the models align with the decomposition ratios at varying temperatures reported in the literature. Our findings are significant for understanding the dynamic behavior of bubbles, with implications for various laser processing underwater.

Temporal structured illumination and vision-transformer enables large field-of-view binary snapshot ptychography.

Ptychography, a widely used computational imaging method, generates images by processing coherent interference patterns scattered from an object of interest. In order to capture scenes with large field-of-view (FoV) and high spatial resolution simultaneously in a single shot, we propose a temporal-compressive structured-light Ptychography system. A novel three-step reconstruction algorithm composed of multi-frame spectra reconstruction, phase retrieval, and multi-frame image stitching is developed, where we employ the emerging Transformer-based network in the first step. Experimental results demonstrate that our system can expand the FoV by 20× without losing spatial resolution. Our results offer huge potential for enabling lensless imaging of molecules with large FoV as well as high spatial-temporal resolutions. We also notice that due to the loss of low-intensity information caused by the compressed sensing process, our method so far is only applicable to binary targets.

Snapshot compressive structured illumination microscopy.

We propose a snapshot compressive structured illumination microscopy (SoSIM) system to increase the number of reconstructed resolution-enhanced (RE) images per second and reduce the data bandwidth by capturing compressed measurements. In this system, multiple low-resolution images are encoded by a high-speed digital micro-mirror device with random binary masks. These images are then captured by a low-speed camera as a snapshot compressed measurement. Following this, we adopt an efficient deep neural network to reconstruct nine images with different structured illumination patterns from a single measurement. The reconstructed images are then combined into a single-frame RE image using the method of spectral synthesis in the frequency domain. When the camera operates at 100 frames per second (fps), we can eventually recover dynamic RE videos at the same speed with 100 fps.

Video snapshot compressive imaging using adaptive progressive coding for high-quality reconstruction under different illumination circumstances.

We consider capturing high-speed color video under different illumination conditions using a video snapshot compressive imaging system (video SCI). An adaptive progressive coding method is proposed, and we conduct an integrated design of the imaging system in terms of optics, mechanics, and control. Compared to previous video SCI systems, this adaptive progressive coding method mitigates the image stability issues in various illumination conditions, ensuring high-quality imaging while greatly improving the light throughput of the system. Based on the analysis of both simulation and real experimental results, we found that this imaging system can achieve color video shooting under an illumination range of 2 lux to 60 lux.