Fast clustering and cell-type annotation of scATAC data using pre-trained embeddings.

Data from the single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) are now widely available. One major computational challenge is dealing with high dimensionality and inherent sparsity, which is typically addressed by producing lower dimensional representations of single cells for downstream clustering tasks. Current approaches produce such individual cell embeddings directly through a one-step learning process. Here, we propose an alternative approach by building embedding models pre-trained on reference data. We argue that this provides a more flexible analysis workflow that also has computational performance advantages through transfer learning. We implemented our approach in scEmbed, an unsupervised machine-learning framework that learns low-dimensional embeddings of genomic regulatory regions to represent and analyze scATAC-seq data. scEmbed performs well in terms of clustering ability and has the key advantage of learning patterns of region co-occurrence that can be transferred to other, unseen datasets. Moreover, models pre-trained on reference data can be exploited to build fast and accurate cell-type annotation systems without the need for other data modalities. scEmbed is implemented in Python and it is available to download from GitHub. We also make our pre-trained models available on huggingface for public use. scEmbed is open source and available at https://github.com/databio/geniml. Pre-trained models from this work can be obtained on huggingface: https://huggingface.co/databio.

DeepGSEA: explainable deep gene set enrichment analysis for single-cell transcriptomic data.

MOTIVATION: Gene set enrichment (GSE) analysis allows for an interpretation of gene expression through pre-defined gene set databases and is a critical step in understanding different phenotypes. With the rapid development of single-cell RNA sequencing (scRNA-seq) technology, GSE analysis can be performed on fine-grained gene expression data to gain a nuanced understanding of phenotypes of interest. However, with the cellular heterogeneity in single-cell gene profiles, current statistical GSE analysis methods sometimes fail to identify enriched gene sets. Meanwhile, deep learning has gained traction in applications like clustering and trajectory inference in single-cell studies due to its prowess in capturing complex data patterns. However, its use in GSE analysis remains limited, due to interpretability challenges. RESULTS: In this paper, we present DeepGSEA, an explainable deep gene set enrichment analysis approach which leverages the expressiveness of interpretable, prototype-based neural networks to provide an in-depth analysis of GSE. DeepGSEA learns the ability to capture GSE information through our designed classification tasks, and significance tests can be performed on each gene set, enabling the identification of enriched sets. The underlying distribution of a gene set learned by DeepGSEA can be explicitly visualized using the encoded cell and cellular prototype embeddings. We demonstrate the performance of DeepGSEA over commonly used GSE analysis methods by examining their sensitivity and specificity with four simulation studies. In addition, we test our model on three real scRNA-seq datasets and illustrate the interpretability of DeepGSEA by showing how its results can be explained. AVAILABILITY AND IMPLEMENTATION: https://github.com/Teddy-XiongGZ/DeepGSEA.

Turbidity-tolerant underwater wireless optical communications using dense blue-green wavelength division multiplexing.

Underwater wireless optical communication (UWOC) has demonstrated high-speed and low-latency properties in clear and coastal ocean water because of the relatively low attenuation 'window' for blue-green wavelengths from 450 nm to 550 nm. However, there are different attenuation coefficients for transmission in ocean water at different wavelengths, and the light transmission more seriously deteriorates with fluctuations in the water turbidity. Therefore, traditional UWOC using a single wavelength or coarse blue-green wavelengths has difficulty tolerating variations in water turbidity. Dense wavelength division multiplexing (WDM) technology provides sufficient communication channels with a narrow wavelength spacing and minimal channel crosstalk. Here, we improve the UWOC in clear and coastal ocean water using dense blue-green WDM. A cost-effective WDM emitter is proposed with directly modulated blue-green laser diodes. Dense wavelength beam combination and collimation are demonstrated in a 20-metre underwater channel from 490 nm to 520 nm. Demultiplexing with a minimum channel spacing of 2 nm is realized by an optical grating. Remarkably, our WDM results demonstrate an aggregate data rate exceeding 10 Gbit/s under diverse water turbidity conditions, with negligible crosstalk observed for each channel. This is the densest WDM implementation with a record channel spacing of 2 nm and the highest channel count for underwater blue-green light communications, providing turbidity-tolerant signal transmission in clear and coastal ocean water.

Fine Mapping of Candidate Gene Controlling Anthocyanin Biosynthesis for Purple Peel in Solanum melongena L.

Fruit color is an intuitive quality of horticultural crops that can be used as an evaluation criterion for fruit ripening and is an important factor affecting consumers' purchase choices. In this study, a genetic population from the cross of green peel 'Qidong' and purple peel '8 guo' revealed that the purple to green color of eggplant peel is dominant and controlled by a pair of alleles. Bulked segregant analysis (BSA), SNP haplotyping, and fine genetic mapping delimited candidate genes to a 350 kb region of eggplant chromosome 10 flanked by markers KA2381 and CA8828. One ANS gene (EGP22363) was predicted to be a candidate gene based on gene annotation and sequence alignment of the 350-kb region. Sequence analysis revealed that a single base mutation of 'T' to 'C' on the exon green peel, which caused hydrophobicity to become hydrophilic serine, led to a change in the three-level spatial structure. Additionally, EGP22363 was more highly expressed in purple peels than in green peels. Collectively, EGP22363 is a strong candidate gene for anthocyanin biosynthesis in purple eggplant peels. These results provide important information for molecular marker-assisted selection in eggplants, and a basis for analyzing the regulatory pathways responsible for anthocyanin biosynthesis in eggplants.

Hydroxamate-Containing Bisphosphonates as Fosmidomycin Analogues: Design, Synthesis, and Proherbicide Activity.

Fosmidomycin (FOS) is a natural product inhibiting the DXR enzyme in the MEP pathway and has stimulated interest for finding more suitable FOS analogues. Herein, two series of FOS analogue hydroxamate-containing bisphosphonates as proherbicides were designed, with bisphosphonate replacing the phosphonic unit in FOS while retaining the hydroxamate (BPF series) or replacing it with retro-hydroxamate (BPRF series). The BPF series were synthesized through a three-step reaction sequence including Michael addition of vinylidenebisphosphonate, N-acylation, and deprotection, and the BPRF series were synthesized with a retro-Claisen condensation incorporated into the reaction sequence. Evaluation on model plants demonstrated several compounds having considerable herbicidal activities, and in particular, compound 8m exhibited multifold activity enhancement as compared to the control FOS. The proherbicide properties were comparatively validated. Furthermore, DXR enzyme assay, dimethylallyl pyrophosphate rescue, and molecular docking verified 8m to be a promising proherbicide candidate targeting the DXR enzyme. In addition, 8m also displayed good antimalarial activities.

Joint Representation Learning for Retrieval and Annotation of Genomic Interval Sets.

As available genomic interval data increase in scale, we require fast systems to search them. A common approach is simple string matching to compare a search term to metadata, but this is limited by incomplete or inaccurate annotations. An alternative is to compare data directly through genomic region overlap analysis, but this approach leads to challenges like sparsity, high dimensionality, and computational expense. We require novel methods to quickly and flexibly query large, messy genomic interval databases. Here, we develop a genomic interval search system using representation learning. We train numerical embeddings for a collection of region sets simultaneously with their metadata labels, capturing similarity between region sets and their metadata in a low-dimensional space. Using these learned co-embeddings, we develop a system that solves three related information retrieval tasks using embedding distance computations: retrieving region sets related to a user query string, suggesting new labels for database region sets, and retrieving database region sets similar to a query region set. We evaluate these use cases and show that jointly learned representations of region sets and metadata are a promising approach for fast, flexible, and accurate genomic region information retrieval.

Design, synthesis and activity evaluation of pseudilin analogs against cyanobacteria as IspD inhibitors.

The discovery of safe, effective, and selective chemical algicides is the stringent need for the algicides development, and it is also one of the effective routes to control cyanobacteria harmful algal blooms and to meet the higher requirements of environmental and ecological. In this work, a series of novel bromo-N-phenyl-5-o-hydroxyphenylpyrazole-3-carboxyamides were rationally designed as pseudilin analogs by bioisosteric replacement and molecular hybridization strategies, in which the pyrrole unit of pseudilin was replaced with pyrazole and further combined with the dominant structural fragments of algicide diuron. The synthesis was carried out by a facile four-step routeincluding cyclization, amidation, transanulation, and halogenation. The biological activity evaluation on AtIspD, EcIspD, Synechocystis sp. PCC6803 and Microcystis aeruginosa FACHB905 revealed that most compounds had good EcIspD and excellent cyanobacteria inhibitory activity. In particular, compound 6bb exhibited potent algicidal activity against PCC6803 and FACHB905 with EC50 = 1.28 µM and 0.37 µM, respectively, 1.4-fold and 4.0-fold enhancement compared to copper sulfate (EC50 = 1.79 and 1.49 µM, respectively), and it also showed the best inhibitory activity of EcIspD. The binding of 6bb to EcIspD was explored by molecular docking, and it was confirmed that 6bb could bind to the EcIspD active site. Compound 6bb was proven to be a potential structure for the further development of novel algicides that targets IspD in the MEP pathway.

DARDN: A Deep-Learning Approach for CTCF Binding Sequence Classification and Oncogenic Regulatory Feature Discovery.

Characterization of gene regulatory mechanisms in cancer is a key task in cancer genomics. CCCTC-binding factor (CTCF), a DNA binding protein, exhibits specific binding patterns in the genome of cancer cells and has a non-canonical function to facilitate oncogenic transcription programs by cooperating with transcription factors bound at flanking distal regions. Identification of DNA sequence features from a broad genomic region that distinguish cancer-specific CTCF binding sites from regular CTCF binding sites can help find oncogenic transcription factors in a cancer type. However, the presence of long DNA sequences without localization information makes it difficult to perform conventional motif analysis. Here, we present DNAResDualNet (DARDN), a computational method that utilizes convolutional neural networks (CNNs) for predicting cancer-specific CTCF binding sites from long DNA sequences and employs DeepLIFT, a method for interpretability of deep learning models that explains the model's output in terms of the contributions of its input features. The method is used for identifying DNA sequence features associated with cancer-specific CTCF binding. Evaluation on DNA sequences associated with CTCF binding sites in T-cell acute lymphoblastic leukemia (T-ALL) and other cancer types demonstrates DARDN's ability in classifying DNA sequences surrounding cancer-specific CTCF binding from control constitutive CTCF binding and identifying sequence motifs for transcription factors potentially active in each specific cancer type. We identify potential oncogenic transcription factors in T-ALL, acute myeloid leukemia (AML), breast cancer (BRCA), colorectal cancer (CRC), lung adenocarcinoma (LUAD), and prostate cancer (PRAD). Our work demonstrates the power of advanced machine learning and feature discovery approach in finding biologically meaningful information from complex high-throughput sequencing data.

Design and synthesis of fosmidomycin analogs containing aza-linkers and their biological activity evaluation.

BACKGROUND: The enzymes involved in the 2-C-methyl-d-erythritol 4-phosphate (MEP) pathway are attractive targets of a new mode of action for developing anti-infective drugs and herbicides, and inhibitors against 1-deoxy-d-xylulose 5-phosphate reductoisomerase (IspC), the second key enzyme in the pathway, have been intensively investigated; however, few works are reported regarding IspC inhibitors designed for new herbicide discovery. RESULTS: A series of fosmidomycin (FOS) analogs were designed with nitrogen-containing linkers replacing the trimethylene linker between the two active substructures of FOS, phosphonic acid and hydroxamic acid. Synthesis followed a facile three-step route of sequential aza-Michael addition of α-amino acids to dibenzyl vinylphosphonate, amidation of the amino acid carboxyl with O-benzyl hydroxylamine, and simultaneous removal of the benzyl protective groups. Biological activity evaluation of IspC and model plants revealed that some compounds had moderate enzyme and model plant growth inhibition effects. In particular, compound 10g, which has a N-(4-fluorophenylethyl) nitrogen-containing linker, exhibited the best plant inhibition activities, superior to the control FOS against the model plants Arabidopsis thaliana, Brassica napus L., Amaranthus retroflexus and Echinochloa crus-galli. A dimethylallyl pyrophosphate rescue assay on A. thaliana confirmed that both 10g and FOS exert their herbicidal activity by blocking the MEP pathway. This result consistent with molecular docking, which confirmed 10g and FOS binding to the IspC active site in a similar way. CONCLUSION: Compound 10g has excellent herbicidal activity and represents the first herbicide lead structure of a new mode of action that targets IspC enzyme in the MEP pathway. © 2023 Society of Chemical Industry.

Design, Synthesis and Bioactivity Evaluation of Heterocycle-Containing Mono- and Bisphosphonic Acid Compounds.

Fosmidomycin (FOS) is a naturally occurring compound active against the 1-deoxy-D-xylulose 5-phosphate reductoisomerase (DXR) enzyme in the 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway, and using it as a template for lead structure design is an effective strategy to develop new active compounds. In this work, by replacing the hydroxamate unit of FOS with pyrazole, isoxazole and the related heterocycles that also have metal ion binding affinity, while retaining the monophosphonic acid in FOS or replacing it with a bisphosphonic acid group, heterocycle-containing mono- and bisphosphonic acid compounds as FOS analogs were designed. The key steps involved in the facile synthesis of these FOS analogs included the Michael addition of diethyl vinylphosphonate or tetraethyl vinylidenebisphosphonate to ß-dicarbonyl compounds and the subsequent cyclic condensation with hydrazine or hydroxylamine. Two additional isoxazolinone-bearing FOS analogs were synthesized via the Michaelis-Becker reaction with diethyl phosphite as a key step. The bioactivity evaluation on model plants demonstrated that several compounds have better herbicidal activities compared to FOS, with the most active compound showing a 3.7-fold inhibitory activity on Arabidopsis thaliana, while on the roots and stalks of Brassica napus L. and Echinochloa crus-galli in a pre-emergence inhibitory activity test, the activities of this compound were found to be 3.2- and 14.3-fold and 5.4- and 9.4-fold, respectively, and in a post-emergency activity test on Amaranthus retroflexus and Echinochloa crus-galli, 2.2- and 2.0-fold inhibition activities were displayed. Despite the significant herbicidal activity, this compound exhibited a DXR inhibitory activity lower than that of FOS but comparable to that of other non-hydroxamate DXR inhibitors, and the dimethylallyl pyrophosphate rescue assay gave no statistical significance, suggesting that a different target might be involved in the inhibiting process. This work demonstrates that using bioisosteric replacement can be considered as a valuable strategy to discover new FOS analogs that may have high herbicidal activities.

ProtoCell4P: an explainable prototype-based neural network for patient classification using single-cell RNA-seq.

MOTIVATION: The rapid advance in single-cell RNA sequencing (scRNA-seq) technology over the past decade has provided a rich resource of gene expression profiles of single cells measured on patients, facilitating the study of many biological questions at the single-cell level. One intriguing research is to study the single cells which play critical roles in the phenotypes of patients, which has the potential to identify those cells and genes driving the disease phenotypes. To this end, deep learning models are expected to well encode the single-cell information and achieve precise prediction of patients' phenotypes using scRNA-seq data. However, we are facing critical challenges in designing deep learning models for classifying patient samples due to (i) the samples collected in the same dataset contain a variable number of cells-some samples might only have hundreds of cells sequenced while others could have thousands of cells, and (ii) the number of samples available is typically small and the expression profile of each cell is noisy and extremely high-dimensional. Moreover, the black-box nature of existing deep learning models makes it difficult for the researchers to interpret the models and extract useful knowledge from them. RESULTS: We propose a prototype-based and cell-informed model for patient phenotype classification, termed ProtoCell4P, that can alleviate problems of the sample scarcity and the diverse number of cells by leveraging the cell knowledge with representatives of cells (called prototypes), and precisely classify the patients by adaptively incorporating information from different cells. Moreover, this classification process can be explicitly interpreted by identifying the key cells for decision making and by further summarizing the knowledge of cell types to unravel the biological nature of the classification. Our approach is explainable at the single-cell resolution which can identify the key cells in each patient's classification. The experimental results demonstrate that our proposed method can effectively deal with patient classifications using single-cell data and outperforms the existing approaches. Furthermore, our approach is able to uncover the association between cell types and biological classes of interest from a data-driven perspective. AVAILABILITY AND IMPLEMENTATION: https://github.com/Teddy-XiongGZ/ProtoCell4P.

Leaky-Vivaldi antenna covered with metasurface with leaky wave radiation and aperture radiation.

A leaky-Vivaldi antenna covered with metasurface (LVAM) is proposed in this paper. The traditional Vivaldi antenna covered with metasurface realizes backward frequency beam-scanning from -41∘ to 0∘ in the high-frequency operating band (HFOB) and retains aperture radiation in the low-frequency operating band (LFOB). In the LFOB, the metasurface can be regarded as a transmission line to realize a slow-wave transmission. In the HFOB, the metasurface can be considered a 2D periodic leaky-wave structure to realize a fast-wave transmission. The simulated results show that LVAM has the -10 dB return loss bandwidths of 46.5% and 40.0%, and the realized gain of 8.8-9.6 dBi and 11.8-15.2 dBi cover the 5 G Sub-6 GHz band (3.3-5.3 GHz) and the X band (8.0-12.0 GHz), respectively. The test results are in good agreement with the simulated results. As a dual-band antenna covering the 5 G Sub-6 GHz communication band and military radar band, the proposed antenna can guide the future integrated design of communication and radar antenna systems.

Mussel-inspired dynamic facet-selective capping approach to highly uniform α-calcium sulfate hemihydrate crystals.

We report herein a dynamic facet-selective capping (dFSC) strategy for α-calcium sulfate hemihydrate crystal growth from dihydrate gypsum in the presence of a catechol-derived PEI capping agent (DPA-PEI) with inspiration by the biomineralization process of mussel. The crystal shape is controllable and varies from long and pyramid-tipped prisms to thin hexagonal plate. The highly uniform truncated crystals have extremely high compression and bending strengths after hydration molding.

Highly-time-resolved FMCW LiDAR with synchronously-nonlinearity-corrected acquisition for dynamic locomotion.

Highly-time-resolved and precise tracking of position, velocity, and acceleration is urgently required when highly dynamic legged robots are walking, trotting, and jumping. Frequency-modulated continuous-wave (FMCW) laser ranging is able to provide precise measurement in short distance. However, FMCW light detection and ranging (LiDAR) suffers from a low acquisition rate and poor linearity of laser frequency modulation in wide bandwidth. A sub-millisecond-scale acquisition rate and nonlinearity correction in the wide frequency modulation bandwidth have not been reported in previous studies. This study presents the synchronous nonlinearity correction for a highly-time-resolved FMCW LiDAR. The acquisition rate of 20 kHz is obtained by synchronizing the measurement signal and the modulation signal of laser injection current with a symmetrical triangular waveform. The linearization of laser frequency modulation is conducted by resampling of 1000 intervals interpolated in every up-sweep and down-sweep of 25 µs, while measurement signal is stretched or compressed in every period of 50 µs. The acquisition rate is demonstrated to be equal to the repetition frequency of laser injection current for the first time to the best of authors' knowledge. This LiDAR is successfully used to track the foot trajectory of a jumping single-leg robot. The high velocity up to 7.15 m/s and high acceleration of 365 m/s2 are measured during the up-jumping phase, while heavy shock takes place with high acceleration of 302 m/s2 as the foot end strikes the ground. The measured foot acceleration of over 300 m/s2, which is more than 30 times gravity acceleration, is reported on a jumping single-leg robot for the first time.

Interpretable meta-learning of multi-omics data for survival analysis and pathway enrichment.

MOTIVATION: Despite the success of recent machine learning algorithms' applications to survival analysis, their black-box nature hinders interpretability, which is arguably the most important aspect. Similarly, multi-omics data integration for survival analysis is often constrained by the underlying relationships and correlations that are rarely well understood. The goal of this work is to alleviate the interpretability problem in machine learning approaches for survival analysis and also demonstrate how multi-omics data integration improves survival analysis and pathway enrichment. We use meta-learning, a machine-learning algorithm that is trained on a variety of related datasets and allows quick adaptations to new tasks, to perform survival analysis and pathway enrichment on pan-cancer datasets. In recent machine learning research, meta-learning has been effectively used for knowledge transfer among multiple related datasets. RESULTS: We use meta-learning with Cox hazard loss to show that the integration of TCGA pan-cancer data increases the performance of survival analysis. We also apply advanced model interpretability method called DeepLIFT (Deep Learning Important FeaTures) to show different sets of enriched pathways for multi-omics and transcriptomics data. Our results show that multi-omics cancer survival analysis enhances performance compared with using transcriptomics or clinical data alone. Additionally, we show a correlation between variable importance assignment from DeepLIFT and gene coenrichment, suggesting that genes with higher and similar contribution scores are more likely to be enriched together in the same enrichment sets. AVAILABILITY AND IMPLEMENTATION: https://github.com/berkuva/TCGA-omics-integration.

Multifunctional water-based metamaterial with polarization conversion and absorption.

A multifunctional metamaterial to realize broadband x-to-y cross-polarization conversion (CPC) and the absorption of electromagnetic waves is proposed in this paper. The presented multifunctional water-based metamaterial (MWM) consists of the top metallic dielectric substrate, the middle 3D printed container, and the bottom metal backplane. When the container is filled with water, the polarization conversion ratio (PCR) reaches more than 90% at 5.8-9.4 GHz, and the excellent absorption performance is achieved in the frequency band of 16.1-16.9 GHz. In addition, the CPC is achieved in two frequency bands (5.9-10.0 GHz and 14.3-16.4 GHz) without water injection. The unique properties of the proposed design are validated by experiments. As expected, the MWM simultaneously achieves polarization conversion and absorption functions, which is meaningful and consequential for multifunction and conformal stealth applications.

Design, synthesis, and herbicidal activity of indole-3-carboxylic acid derivatives as potential transport inhibitor response 1 antagonists.

Auxins as an important class of phytohormones play essential roles in plant life cycle; therefore, developing compounds with auxin-like properties for plant growth regulation and weed control applications is of great significance. Herein, we reported the design, synthesis, and herbicidal activity evaluation of a series of novel indole-3-carboxylic acid derivatives as auxin receptor protein TIR1 antagonists. Petri dish herbicidal activity assay demonstrated that most of the as-synthesized target compounds exhibited good-to-excellent inhibition effects (60-97% inhibitory rates) on roots and shoots of both dicotyledonous rape (B. napus) and monocotyledonous barnyard grass (E. crus-galli). The inhibition rates of compounds 10d and 10h reached up to 96% and 95% for the root of rape (B. napus) at 100 mg/L, and they also maintained 92% and 93% inhibition rates even if at 10 mg/L, respectively. Molecular docking revealed that the interactions between these synthesized target compounds and TIR1 protein include tight π-π stacking, hydrogen bond, and hydrophobic interactions. This work expands the range of auxin chemistry for the development of new auxin mimic herbicides.

Comparative Transcriptome Analysis Reveals Gene Expression Differences in Eggplant (Solanum melongena L.) Fruits with Different Brightness.

Fruit brightness is an important quality trait that affects the market value of eggplant. However, few studies have been conducted on eggplant brightness. In this study, we aimed to identify genes related to this trait in three varieties of eggplant with different fruit brightness between 14 and 22 days after pollination. Using RNA-Seq Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses, we found that wax- and cutin-related pathways and differentially expressed genes displayed significant differences among different development stages and varieties. Scanning electron microscopy revealed that the wax layer was thinner in '30-1' and 'QPCQ' than in '22-1'. Gas chromatography-mass spectrometry analysis revealed that wax content was significantly lower in '30-1' than in '22-1', which indicated that wax may be an important factor determining fruit brightness. We further identified and analyzed the KCS gene family, which encodes the rate-limiting enzyme of FA elongation in wax synthesis. The results provide an insight into the molecular mechanisms of fruit brightness in eggplants and further eggplant breeding programs.

Rich-Club Analysis of Structural Brain Network Alterations in HIV Positive Patients With Fully Suppressed Plasma Viral Loads.

Objective: Even with successful combination antiretroviral therapy (cART), patients with human immunodeficiency virus positive (HIV+) continue to present structural alterations and neuropsychological impairments. The purpose of this study is to investigate structural brain connectivity alterations and identify the hub regions in HIV+ patients with fully suppressed plasma viral loads. Methods: In this study, we compared the brain structural connectivity in 48 patients with HIV+ treated with a combination of antiretroviral therapy and 48 healthy controls, using diffusion tensor imaging. Further comparisons were made in 24 patients with asymptomatic neurocognitive impairment (ANI) and 24 individuals with non-HIV-associated neurocognitive disorders forming a subset of HIV+ patients. The graph theory model was used to establish the topological metrics. Rich-club analysis was used to identify hub nodes across groups and abnormal rich-club connections. Correlations of connectivity metrics with cognitive performance and clinical variables were investigated as well. Results: At the regional level, HIV+ patients demonstrated lower degree centrality (DC), betweenness centrality (BC), and nodal efficiency (NE) at the occipital lobe and the limbic cortex; and increased BC and nodal cluster coefficient (NCC) in the occipital lobe, the frontal lobe, the insula, and the thalamus. The ANI group demonstrated a significant reduction in the DC, NCC, and NE in widespread brain regions encompassing the occipital lobe, the frontal lobe, the temporal pole, and the limbic system. These results did not survive the Bonferroni correction. HIV+ patients and the ANI group had similar hub nodes that were mainly located in the occipital lobe and subcortical regions. The abnormal connections were mainly located in the occipital lobe in the HIV+ group and in the parietal lobe in the ANI group. The BC in the calcarine fissure was positively correlated with complex motor skills. The disease course was negatively correlated with NE in the middle occipital gyrus. Conclusion: The results suggest that the occipital lobe and the subcortical regions may be important in structural connectivity alterations and cognitive impairment. Rich-club analysis may contribute to our understanding of the neuropathology of HIV-associated neurocognitive disorders.

PCSK9 Modulates Macrophage Polarization-Mediated Ventricular Remodeling after Myocardial Infarction.

Background and Aims: An increasing number of high-risk patients with coronary heart disease (similar to acute myocardial infarction (AMI)) are using PCSK9 inhibitors. However, whether PCSK9 affects myocardial repair and the molecular mechanism of PCSK9 modulation of immune inflammation after AMI are not known. The present research investigated the role of PCSK9 in the immunomodulation of macrophages after AMI and provided evidence for the clinical application of PCSK9 inhibitors after AMI to improve cardiac repair. Methods and Results: Wild-type C57BL6/J (WT) and PCSK9-/- mouse hearts were subjected to left anterior descending (LAD) coronary artery occlusion to establish an AMI model. Correlation analysis showed that higher PCSK9 expression indicated worse cardiac function after AMI, and PCSK9 knockout reduced infarct size, improved cardiac function, and attenuated inflammatory cell infiltration compared to WT mice. Notably, the curative effects of PCSK9 inhibition were abolished after the systemic depletion of macrophages using clodronate liposomes. PCSK9 showed a regulatory effect on macrophage polarization in vivo and in vitro. Our studies also revealed that activation of the TLR4/MyD88/NF-κB axis was a possible mechanism of PCSK9 regulation of macrophage polarization. Conclusion: Our data suggested that PCSK9 modulated macrophage polarization-mediated ventricular remodeling after myocardial infarction.