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Alzheimer's Disease (AD) is an irreversible neurodegenerative disease common in the elderly. The application of artificial intelligence technology to the early diagnosis of AD can not only improve the accuracy of prediction compared with traditional methods, but also save the complicated manual feature extraction of traditional methods and speed up the diagnosis. This paper reviews various applications of artificial intelligence algorithms in AD diagnosis, including machine learning, convolutional neural network, graph convolutional neural network, cyclic neural network and other mainstream deep learning technologies. The advantages and disadvantages of each approach are discussed, and finally, we discuss limitations and future prospects.

The research in this paper provides generalization and new ideas for research topics in computer-assisted medicine. The main improvement efforts in deep learning-based multimodal fusion schemes, which provide alternative directions and robust feature fitting performance for fusion schemes, are building complex structures, migrating knowledge or experience, processing and enhancing data, and targeting features for semantic correction based on contextual features. At the application level, the brain, liver, and lungs are the main targets of scientific research, so this paper surveys related work and analyzes the reasons for performance gains. Taken together, deep learning-based image fusion schemes can assist physicians in understanding information about lesion sites, lesion types, and sizes, providing an important basis for developing personalized treatment plans, which is important for improving diagnosis and specifying precise treatment plans. Therefore, the investigation of medical image fusion schemes is promising and beneficial.

Controlling the perovskite morphology and defects at the buried perovskite-substrate interface is challenging for inverted perovskite solar cells. In this work, we report an amphiphilic molecular hole transporter, (2-(4-(bis(4-methoxyphenyl)amino)phenyl)-1-cyanovinyl)phosphonic acid, that features a multifunctional cyanovinyl phosphonic acid group and forms a superwetting underlayer for perovskite deposition, which enables high-quality perovskite films with minimized defects at the buried interface. The resulting perovskite film has a photoluminescence quantum yield of 17% and a Shockley-Read-Hall lifetime of nearly 7 microseconds and achieved a certified power conversion efficiency (PCE) of 25.4% with an open-circuit voltage of 1.21 volts and a fill factor of 84.7%. In addition, 1–square centimeter cells and 10–square centimeter minimodules show PCEs of 23.4 and 22.0%, respectively. Encapsulated modules exhibited high stability under both operational and damp heat test conditions.

Background: Acute myocardial infarction (AMI) is a leading cause of death and disability. Diabetes is an important risk factor and a common comorbidity in AMI patients. The higher mortality risk of diabetes-AMI relative to nondiabetes-AMI indicates a need for specific treatment to improve clinical outcome. However, the global metabolic dysregulation of AMI complicated with diabetes is still unclear. We aim to systematically interrogate changes in the metabolic microenvironment immediate to AMI episodes in the absence or presence of diabetes. Methods: In this work, quantitative metabolomics was used to investigate plasma metabolic differences between diabetes-AMI (n=59) and nondiabetes-AMI (n=59) patients. A diverse array of perturbed metabolic pathways involving carbohydrate metabolism, lipid metabolism, glycolysis, tricarboxylic acid cycle, and amino acid metabolism emerged. Results: In all, our omics-oriented approach defined a metabolic signature of afflicted mitochondrial function aggravated by concurrent diabetes in AMI patients. In particular, our analyses uncovered N-lactoyl-phenylalanine and lysophosphatidylcholines as key functional metabolites that skewed the metabolic picture of diabetes-AMI relative to nondiabetes-AMI. N-lactoyl-phenylalanine was strongly associated with metabolic indicators reflective of mitochondrial overload and negatively correlated with HbA1c (glycosylated hemoglobin, type A1C) specifically in hyperglycemic AMI, suggestive of its central role in glucose utilization and mitochondrial energy production instrumental to the clinical outcome of diabetes-AMI. Reductions in lysophosphatidylcholines, which were negatively correlated with blood glucose and inflammatory markers, might further compromise glucose expenditure and aggravate inflammation leading to poorer prognosis in diabetes-AMI. Conclusions: As circulating metabolite levels are amenable to therapeutic intervention, such shifts in metabolic signatures provide new clues and potential therapeutic targets specific to the treatment of diabetes-AMI.

The development of cloud computing has brought many conveniences to life, its advantages of low cost and high computing power bring convenience to our life, but the main problem is how to protect user data on untrustworthy cloud servers. To solve this problem, we have proposed a new private set intersection protocol, denoted as. The protocol uses a representation of sets with polynomial point-value pairs. All elements should be transmitted and computed in the protocol as ciphertext by introducing random polynomials to re-randomize the encryption elements. To reduce the computational burden of each party. The parties send the processed polynomials to the cloud server, which calculates the intersection. In addition, our protocol achieves fairness in the case of collusion between parties. Finally, the security of the protocol is demonstrated under the UC model. Comparing the performance of this protocol with other related protocols show that the protocol is better than the other associated protocols in terms of low computational overhead.

Acute myeloid leukemia (AML) is fueled by leukemic stem cells (LSC) whose determinants are challenging to discern from hematopoietic stem cells (HSC) or uncover by approaches focused on general cell properties. We have identified a set of RNA-binding proteins (RBP) selectively enriched in human AML LSCs. Using an in vivo two-step CRISPR-Cas9 screen to assay stem cell functionality, we found 32 RBPs essential for LSCs in MLL-AF9;NrasG12D AML. Loss-of-function approaches targeting key hit RBP ELAVL1 compromised LSC-driven in vivo leukemic reconstitution, and selectively depleted primitive malignant versus healthy cells. Integrative multiomics revealed differentiation, splicing, and mitochondrial metabolism as key features defining the leukemic ELAVL1–mRNA interactome with mitochondrial import protein, TOMM34, being a direct ELAVL1-stabilized target whose repression impairs AML propagation. Altogether, using a stem cell–adapted in vivo CRISPR screen, this work demonstrates pervasive reliance on RBPs as regulators of LSCs and highlights their potential as therapeutic targets in AML. Significance: LSC-targeted therapies remain a significant unmet need in AML. We developed a stem-cell–adapted in vivo CRISPR screen to identify key LSC drivers. We uncover widespread RNA-binding protein dependencies in LSCs, including ELAVL1, which we identify as a novel therapeutic vulnerability through its regulation of mitochondrial metabolism. This article is highlighted in the In This Issue feature, p. 171

Hydrofractures, or hydraulic fractures, are fractures where a significantly elevated fluid pressure played a role in their formation. Natural hydrofractures are abundant in rocks and are often preserved as magmatic dykes or sills, and mineral-filled fractures or mineral veins. However, we focus on the formation and evolution of non-igneous hydrofractures. Here we review the basic theory of the role of fluid pressure in rock failure, showing that both Terzaghi’s and Biot’s theories can be reconciled if the appropriate boundary conditions are considered. We next discuss the propagation of hydrofractures after initial failure, where networks of hydrofractures may form or hydrofractures may ascend through the crust as mobile hydrofractures. As fractures can form as a result of both tectonic stresses and an elevated fluid pressure, we address the question of how to ascertain whether a fracture is a hydrofracture. We argue that extensional or dilational fractures that formed below c. 2–3 km depth are, under normal circumstances, hydrofractures, but at shallower depth they may, but must not be hydrofractures. Since veins and breccias are often the products of hydrofractures that are left in the geological record, we discuss these and critically assess which vein structures can, and which do not necessarily, indicate hydrofracturing. Hydrofracturing can suddenly and locally change the permeability in a rock by providing new fluid pathways. This can lead to highly dynamic self-organization of crustal-scale fluid flow.

Crystalline materials are often considered to have rigid periodic lattices, while soft materials are associated with flexibility and nonperiodicity. The continuous evolution of metal-organic frameworks (MOFs) has erased the boundaries between these two distinct conceptions. Flexibility, disorder, and defects have been found to be abundant in MOF materials with imperfect crystallinity, and their intricate interplay is poorly understood because of the limited strategies for characterizing disordered structures. Here, we apply advanced nuclear magnetic resonance spectroscopy to elucidate the mesoscale structures in a defective MOF with a semicrystalline lattice. We show that engineered defects can tune the degree of lattice flexibility by combining both ordered and disordered compartments. The one-dimensional alignment of correlated defects is the key for the reversible topological transition. The unique matrix is featured with both rigid framework of nanoporosity and flexible linkage of high swellability.

The flexible strain sensors based on the textile substrate have natural flexibility, high sensitivity, and wide-range tensile response. However, the textile's complex and anisotropic substructure leads to a negative differential resistance (NDR) response, lacking a deeper understanding of the mechanism. Therefore, we examined a graphene textile strain sensor with a conspicuous NDR tensile response, providing a requisite research platform for mechanism investigation. The pioneering measurement of single fiber bundles confirmed the existence of the NDR effect on the subgeometry scale. Based on the in situ characterization of tensile morphology and measurement, we conducted quantitative behavior analyses to reveal the origin of tensile electrical responses in the full range comprehensively. The results showed that the dominant factor in generating the NDR effect is the relative displacement of fibers within the textile bundles. Based on the neural spiking-like tensile response, we further demonstrated the application potential of the textile strain sensor in threshold detection and near-sensor signal processing. The proposed NDR behavior model would provide a reference for the design and application of wearable intelligent textiles.

Hematopoietic stem cell (HSC) dormancy is understood as supportive of HSC function and its long-term integrity. Although regulation of stress responses incurred as a result of HSC activation is recognized as important in maintaining stem cell function, little is understood of the preventive machinery present in human HSCs that may serve to resist their activation and promote HSC self-renewal. We demonstrate that the transcription factor PLAG1 is essential for long-term HSC function and, when overexpressed, endows a 15.6-fold enhancement in the frequency of functional HSCs in stimulatory conditions. Genome-wide measures of chromatin occupancy and PLAG1-directed gene expression changes combined with functional measures reveal that PLAG1 dampens protein synthesis, restrains cell growth and division, and enhances survival, with the primitive cell advantages it imparts being attenuated by addition of the potent translation activator, c-MYC. We find PLAG1 capitalizes on multiple regulatory factors to ensure protective diminished protein synthesis including 4EBP1 and translation-targeting miR-127 and does so independently of stress response signaling. Overall, our study identifies PLAG1 as an enforcer of human HSC dormancy and self-renewal through its highly context-specific regulation of protein biosynthesis and classifies PLAG1 among a rare set of bona fide regulators of messenger RNA translation in these cells. Our findings showcase the importance of regulated translation control underlying human HSC physiology, its dysregulation under activating demands, and the potential if its targeting for therapeutic benefit.

Carbon dots (C-Dots), with unique properties from tunable photoluminescence to biocompatibility, show wide applications in biotechnology, optoelectronic device and catalysis. Chiral C-Dots are expected to have strongly chirality-dependent biological and catalytic properties. For chiral C-Dots, a clear structure and quantitative structure-property relationship need to be clarified. Here, chiral C-Dots were fabricated by electrooxidation polymerization from serine enantiomers. The oxidized serine has a reversed chiral configuration to serine, which leads to the chiral C-Dots possessing inverse handedness compared with their raw materials. Electron circular dichroism spectrum, together with other diverse characterization techniques and theoretical calculations, confirmed that these chiral C-Dots (2-7 nm) have a well-defined primary structure of polycyclic dipeptide and possess a spatial structure with a

A series of vibratile π-extended dihydrophenazines (BPs) and a tetrahydrodiphenazine (TP) were synthesized via direct C–N coupling reactions. Structural alterations of the fused acene wings lead to...

A tool to map changes in synaptic strength during a defined time window could provide powerful insights into the mechanisms governing learning and memory. We developed a technique, Extracellular Protein Surface Labeling in Neurons (EPSILON), to map alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) insertion in vivo by pulse-chase labeling of surface AMPARs with membrane-impermeable dyes. This approach allows for single-synapse resolution maps of plasticity in genetically targeted neurons during memory formation. We investigated the relationship between synapse-level and cell-level memory encodings by mapping synaptic plasticity and cFos expression in hippocampal CA1 pyramidal cells upon contextual fear conditioning (CFC). We observed a strong correlation between synaptic plasticity and cFos expression, suggesting a synaptic mechanism for the association of cFos expression with memory engrams. The EPSILON technique is a useful tool for mapping synaptic plasticity and may be extended to investigate trafficking of other transmembrane proteins.

Lung squamous cell carcinoma (LUSC) is the second most common lung cancer worldwide, leading to millions of deaths annually. Although immunotherapy has expanded the therapeutic choices for LUSC and achieved considerable efficacy in a subset of patients, many patients could not benefit, and resistance was pervasive. Therefore, it is significant to investigate the mechanisms leading to patients’ poor response to immunotherapies and explore novel therapeutic targets. Using multiple public LUSC datasets, we found that Kallikrein-8 (KLK8) expression was higher in tumor samples and was correlated with inferior survival. Using a LUSC cohort (n = 190) from our center, we validated the bioinformatic findings about KLK8 and identified high KLK8 expression as an independent risk factor for LUSC. Function enrichment showed that several immune signaling pathways were upregulated in the KLK8 low-expression group and downregulated in the KLK8 high-expression group. For patients with low KLK8 expression, they were with a more active TME, which was both observed in the TCGA database and immune marker immunohistochemistry, and they had extensive positive relations with immune cells with tumor-eliminating functions. This study identified KLK8 as a risk factor in LUSC and illustrated the associations between KLK8 and cancer immunity, suggesting the potentiality of KLK8 as a novel immune target in LUSC.

The design and regulation of phosphor are attractive but challenging due to the spin-forbidden intersystem crossing (ISC) process. Here, a new perspective on the enhancement of ISC was proposed and demonstrated. Different from current strategies, the ISC yield (ΦISC) was enhanced by decreasing the fluorescence radiative transition rate constant (kF) via rational molecular designing, rather than boosting the spin-orbit coupling by decorating molecular skeleton with heavy atom, heteroatom, or carbonyl. The kF of the designed molecule in this case was associated with the substituent position of the methoxy group, which alter the distribution of the front orbitals. The S0→S1 transition of these compounds evolved from bright state to dark state gradually with the variation of substituent position, accompanied by the decrease of kF and increase of ΦISC. The fluorescence emission was switched to phosphorescence emission successfully by regulating the kF. This work provided an alternative strategy to design efficient room-temperature phosphorescence material.

In this paper, we investigate the application of Hybrid Representation in Wide-Angle Synthetic Aperture Radar (WASAR) imaging, addressing the challenges of achieving sparse representation in the presence of complex electromagnetic scattering characteristics and highly anisotropic targets. We utilize a Convolutional Neural Network (CNN) to represent two-dimensional data within the same subaperture, while employing dictionary learning for sparse representation across different subapertures. Convolutional Neural Networks (CNNs) excel at learning spatial hierarchies and local dependencies in two-dimensional data, but require a large amount of training data. Isotropic targets within subapertures can be used for training with conventional SAR data, whereas anisotropic targets present challenges in obtaining training samples. To address this, a dictionary for different subapertures is generated from measurements using dictionary learning, eliminating the need for additional training data. By integrating these methods, we propose a novel approach, Hybrid-WASAR, which incorporates two regularization terms into WASAR imaging and employs the Alternating Direction Method of Multipliers (ADMM) to iteratively solve the imaging model. Compared to traditional WASAR imaging techniques, Hybrid-WASAR not only enhances the accuracy of the reconstructed target backscatter coefficients, but also effectively reduces sidelobes and noise, resulting in a significant improvement in overall imaging quality.

Cellular automata is an important tool to study the emergent properties of complex systems based on its well-known parallel, bio-inspired, computational characteristics. However, running cellular automata on conventional chips suffer from low parallelism, and high hardware cost. Establish dedicated hardware for cellular automata remains elusive. Here, we propose a recirculate logic operations scheme (RLOS) based on memristive hardware combined with 2D transistors to realize cellular automata evolution. The scheme utilizes the storage and calculation characteristics of memristive devices, which greatly reduces hardware complexity. The versatility of the RLOS scheme allows implementing multiple different cellular automata algorithms on the same circuitry. The entire rule (rule 1-254) of elementary cellular automata and more complicated 1D CA model majority classification algorithm have been verified to be applicable to this circuitry. Further, the edge detection algorithm based on 2D cellular automata has been authenticated through RLOS. The experimental and evaluation results reveal that the scheme reduces the hardware cost up to 79 times comparing to the Field Programmable Gate Array (FPGA) approach. To our best knowledge, RLOS has the lowest hardware cost (6 components/per cell) among state-of-art hardware implementations. This work can pave the road towards high-efficiency and low-cost cellular automata hardware realization, and also facilitates the exploration of memristive applications.

Backgroud The role of epigenetic modifications in tumorigenesis has been widely reported. However, the role and mechanism of H3K4me3 modification in lung adenocarcinoma (LUAD) are rarely reported systematically. We, therefore, sought to analyze the characteristics of LUAD associated with H3K4me3 modification, build an H3K4me3-lncRNAs score model to predict the prognosis of patients with LUAD and clarify the potential value of H3K4me3 in immunotherapy of LUAD. Methods We evaluated H3K4me3-lncRNA patterns and H3K4me3-lncRNA scores of 477 LUAD samples based on 53 lncRNAs closely correlated to H3K4me3 regulators and comprehensive analyzed the role of these patterns in tumorigenesis and tumor immunity. Using Gene set variation analysis (GSVA), we systematically evaluated the H3K4me3 level of every sample and deeply analyzed the effect of H3K4me3 on the prognosis of LUAD. In addition, we included two independent immunotherapy cohorts to study the impact of high H3K4me3 score on the prognosis of patients. We also used an independent cohort with 52 matched paraffin specimens of LUAD to verify the impact of high H3K3me3 expression on the prognosis of patients. Results We identified three H3K4me3-lncRNA patterns that exhibited specific immune characteristics. Characterized by immunosuppressive and increased TGFβ-mediated epithelial-mesenchymal transition (EMT), patients with high H3K4me3-lncRNA score had a poor overall survival and decreased H3K4me3 score. H3K4me3 score was significantly positively correlated with CD4+T-cell and CD8+T-cell activation, programmed cell death and immune checkpoints (ICs) expression, and was negatively correlated with MYC pathway, TP53 pathway, and cell proliferation. Patients with high H3K4me3 score showed elevated expression of ICs, potentiated CD4 T-cell and CD8 T-cell activation, increased programmed cell death, and suppressed cell proliferation and TGFβ-mediated EMT. Patients with high H3K4me3 score and high expression of CTLA4, ICOS, TIGIT, PDCD1LG2, IDO1, CD274, PDCD1, LAG3, or HAVCR2 had the best survival advantage. Two independent immunotherapy cohorts verified that patients with high H3K4me3 score showed an increased inflamed tumor microenvironment (TME) phenotype and enhanced anti-PD-1/L1 immunotherapy response. Immunohistochemistry (IHC) data from 52 matched paraffin specimens of LUAD confirmed that the protein level of H3K4me3 in tumor was significantly lower than that of paracancerous tissues and H3K4me3 brought significant survival benefits to patients with LUAD. Conclusions We build an H3K4me3-lncRNAs score model to predict the prognosis of patients with LUAD. More importantly, this study revealed characteristics of H3K4me3 modification in LUAD and clarified the important potential role of H3K4me3 on tumor immunotherapy and patients’ survival.

Table S5 shows the log2 fold change of differentially expressed transcripts from the ELAVL1 knockout RN2c RNA-sequencing data set.

Table S7 shows (a) reproducible peaks and (b) GO analysis of ELAVL1-bound transcripts identified in the eCLIP-sequencing data set. The negatively enriched (c) and positively-enriched (d) gene sets identified by GSEA of ELAVL1-bound and differentially expressed transcripts from the RN2c RNA-sequencing data set are shown.

Table S1 shows expression of RBPs in an RNA-sequencing data set of human bone marrow hematopoietic stem and progenitor subpopulations.

Supplemental Figure S4 shows the validation of ELAVL1 knockdown by western blot and effects of ELAVL1 loss on human primary AML cells in vitro and in vivo.

Table S8 shows transcripts from the ELAVL1 knockout RNA-sequencing data set that are (a) alternatively spliced and (b) alternatively spliced as well as bound by ELAVL1 as identified by eCLIP-sequencing.