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Abstract Single cell chromatin accessibility profiling and transcriptome sequencing are the most widely used technologies for single-cell genomics. Here, we present Microwell-seq3, a high-throughput and facile platform for high-sensitivity single-nucleus chromatin accessibility or full-length transcriptome profiling. The method combines a preindexing strategy and a penetrable chip-in-a-tube for single nucleus loading and DNA amplification and therefore does not require specialized equipment. We used Microwell-seq3 to profile chromatin accessibility in more than 200,000 single nuclei and the full-length transcriptome in ~50,000 nuclei from multiple adult mouse tissues. Compared with the existing polyadenylated transcript capture methods, integrative analysis of cell type-specific regulatory elements and total RNA expression uncovered comprehensive cell type heterogeneity in the brain. Gene regulatory networks based on chromatin accessibility profiling provided an improved cell type communication model. Finally, we demonstrated that Microwell-seq3 can identify malignant cells and their specific regulons in spontaneous lung tumors of aged mice. We envision a broad application of Microwell-seq3 in many areas of research.

Plankton, as an important part of the food chain in the river ecosystem, is sensitive to changes in the environment. To investigate the influence of plankton community structure on the aquatic ecosystem and further verify the applicability of biomanipulation, the midstream of the Jialing River was selected as the study area. A total of 149 species (varieties) from 7 phyla, 18 orders, 34 families, and 72 genera were identified for phytoplankton. A total of 48 species were found for zooplankton. The mean density of phytoplankton was 2.09 × 106 cells/L, and the mean density of zooplankton was 27.1 ind./L. Moreover, the mean values of the Shannon–Wiener diversity index (H′), Pielou homogeneity index (J′), and Margalef abundance index (D) for phytoplankton were 3.17, 0.76, and 4.47, respectively. The mean values of the Shannon–Wiener diversity index (H′), Pielou homogeneity index (J′), and Margalef abundance index (D) for zooplankton were 2.64, 0.92, and 5.07, respectively. Based on the diversity indices, the water quality was evaluated as oligo pollution to β-medium pollution level. Redundancy analysis (RDA) was used to explore the relationship between dominant plankton species and environmental factors. The RDA results show that the main environmental factors affecting the community structure of phytoplankton and zooplankton were water temperature (WT), electrical conductivity (EC), pH, total phosphorus (TP), and transparency (Secchi depth; SD). There were 17 functional groups in phytoplankton, which are X1, S1, Y, Y2, and so on. In zooplankton, there were six functional groups, which are Pmsl, Prsu, Pfif, Pmip, Lrsf and Lmic. Many articles have demonstrated the feasibility of using biological manipulation to control algae. The number of piscivorous fish in the midstream of the Jialing River had increased, and phytoplankton density in this survey was significantly decreased. The results show that traditional biomanipulation can be applied to this region, which can provide better scientific support for the future management of the aquatic environment in the Jialing River basin.

BackgroundPer- and polyfluoroalkyl substance (PFAS) exposure and cardiovascular disease are controversial. We aimed to assess the association between serum PFAS exposure and cardiovascular health (CVH) in U.S. adults.MethodsWe analyzed serum PFAS concentration data of U.S. adults reported in the National Health and Nutrition Examination Survey (NHANES) study (2005–2018). We employed two weighted logistic regression models and a restricted cubic spline (RCS) to examine the association between each PFAS and impaired CVH (defined as moderate and low CVH). Quantile g-computation (Qgcomp) and weighted quantile sum (WQS) analysis were used to estimate the effects of mixed exposures to PFASs on impaired CVH.ResultsPFAS were associated with an increased risk of impaired CVH (ORPFNA: 1.40, 95% CI: 1.09, 1.80; ORPFOA: 1.44, 95% CI: 1.10, 1.88; ORPFOS: 1.62, 95% CI: 1.25, 2.11). PFOA and PFOS exhibited nonlinear relationships with impaired CVH. Significant interactions were observed for impaired CVH between race/ethnicity and PFHxS (p = 0.02), marital status and PFOA (p = 0.03), and both marital status and race/ethnicity with PFOS (p = 0.01 and p = 0.02, respectively). Analysis via WQS and Qgcomp revealed that the mixture of PFAS was positively associated with an increased risk of impaired CVH.ConclusionPFNA, PFOA, and PFOS exposure are associated with an increased risk of impaired CVH in U.S. adults. Race/ethnicity and marital status may influence CVH. Reducing PFAS exposure could alleviate the burden of disease associated with impaired CVH.

Host-microbe interactions are virtually bidirectional, but how the host affects their microbiome is poorly understood. Here, we report that the host is a critical modulator to regulate the lifestyle switch and pathogenicity heterogeneity of the opportunistic pathogens Serratia marcescens utilizing the Drosophila and bacterium model system. First, we find that Drosophila larvae efficiently outcompete S. marcescens and typically drive a bacterial switch from pathogenicity to commensalism toward the fly. Furthermore, Drosophila larvae reshape the transcriptomic and metabolic profiles of S. marcescens characterized by a lifestyle switch. More importantly, the host alters pathogenicity and heterogeneity of S. marcescens in the single-cell resolution. Finally, we find that larvae-derived AMPs are required to recapitulate the response of S. marcescens to larvae. Altogether, our findings provide an insight into the pivotal roles of the host in harnessing the life history and heterogeneity of symbiotic bacterial cells, advancing knowledge of the reciprocal relationships between the host and pathogen.

BackgroundCoronary heart disease (CHD) is representative of cardiovascular disease and the leading cause of death in humans. Previous studies have shown that kidney disease is associated with CHD, and current treatment options that can improve both cardiac and renal functions still have some limitations. The traditional Chinese medicine Bu-Shen-Huo-Xue granule (BSHXG) can promote blood rheology, inhibit platelet agglutination, and improve heart and kidney functions.MethodsThis is a multicenter, randomized, double-blind, placebo-controlled clinical trial. A total of 210 participants will be randomized to the intervention group and the placebo group. The Guang’anmen Hospital of China Academy of Chinese Medical Sciences is the leading center, and the Affiliated Hospital of Shandong University of Chinese Medicine and the First Affiliated Hospital of Guangzhou University of Chinese Medicine are the participating units. In addition to conventional pharmacotherapy for angina, the intervention group will receive BSHXG while the placebo group will receive BSHXG placebo. All participants will receive 2 months of treatment with 6 months of follow-up. The primary outcome is the efficacy of angina pectoris symptoms in CHD. Secondary outcomes are nitroglycerin arrest, ECG efficacy, Seattle Angina Questionnaire score, serology indicators, assessment of safety, and cardiovascular endpoint events. The transcriptome and metabolome will be used to screen biomarkers for diagnosis and efficacy evaluation.DiscussionThis study aimed to evaluate the efficacy and safety of Bu-Shen-Huo-Xue granule in the treatment of coronary heart disease, and to evaluate the benefits to patients with coronary heart disease from both cardiac and renal indicators.Trial registrationThis trial is approved by the Ethical Review Committee of the Guang’anmen Hospital China Academy of Chinese Medical Sciences with the number 2022-224-KY-01, and has been registered on the Chinese Clinical Trials Registry with the number ChiCTR2300070977 on 27 April 2023.

The efficient generation of monodispersed droplets holds great promise for micro‐/nanoparticle synthesis and biochemical analysis. However, it remains a challenge to achieve high‐throughput generation of monodispersed droplets in a portable and/or parallel manner in nonexpert biomedical laboratories. Herein, a versatile microchannel array (μCA) device is reported that is portable, multifaceted, reliable, and mass‐manufacturable, supporting the high‐throughput generation of monodispersed droplets in different ways. This device consists of a silicon‐based μCA chip based on the step emulsification principle, as well as two matching plastic containers, both of which can be mass‐manufactured by traditional microfabrication methods at low material costs. With the μCA device, aqueous solution can be dispersed into emulsion droplets by various modes, such as mechanical pump‐based large‐scale, handheld syringe‐based portable, and gas pump‐based highly parallel droplet generation. Furthermore, it is demonstrated that our cost‐effective device can be applied for digital polymerase chain reaction analysis, supporting the need for more accessible microfluidic systems. Thus, the present device is expected to have a significant impact on both benchside and bedside applications.

Abstract Background The presence of residual cardiovascular risk is an important cause of cardiovascular events. Despite the significant advances in our understanding of residual cardiovascular risk, a comprehensive analysis through bibliometrics has not been performed to date. Our objective is to conduct bibliometric studies to analyze and visualize the current research hotspots and trends related to residual cardiovascular risk. This will aid in understanding the future directions of both basic and clinical research in this area. Methods The literature was obtained from the Web of Science Core Collection database. The literature search date was September 28, 2022. Bibliometric indicators were analyzed using CiteSpace, VOSviewer, Bibliometrix (an R package), and Microsoft Excel. Result A total of 1167 papers were included, and the number of publications is increasing rapidly in recent years. The United States and Harvard Medical School are the leading country and institution, respectively, in the study of residual cardiovascular risk. Ridker PM and Boden WE are outstanding investigators in this field. According to our research results, the New England Journal of Medicine is the most influential journal in the field of residual cardiovascular risk, whereas Atherosclerosis boasts the highest number of publications on this topic. Analysis of keywords and landmark literature identified current research hotspots including complications of residual cardiovascular risk, risk factors, and pharmacological prevention strategies. Conclusion In recent times, global attention toward residual cardiovascular risk has significantly increased. Current research is focused on comprehensive lipid-lowering, residual inflammation risk, and dual-pathway inhibition strategies. Future efforts should emphasize strengthening international communication and cooperation to promote the comprehensive evaluation and management of residual cardiovascular risk.

Abstract Bacteria colonize almost all parts of the human body and can differ significantly. However, the population level transcriptomics measurements can only describe the average bacteria population behaviors, ignoring the heterogeneity among bacteria. Here, we report a droplet-based high-throughput single-microbe RNA-seq assay (smRandom-seq), using random primers for in situ cDNA generation, droplets for single-microbe barcoding, and CRISPR-based rRNA depletion for mRNA enrichment. smRandom-seq showed a high species specificity (99%), a minor doublet rate (1.6%), a reduced rRNA percentage (32%), and a sensitive gene detection (a median of ~1000 genes per single E. coli). Furthermore, smRandom-seq successfully captured transcriptome changes of thousands of individual E. coli and discovered a few antibiotic resistant subpopulations displaying distinct gene expression patterns of SOS response and metabolic pathways in E. coli population upon antibiotic stress. smRandom-seq provides a high-throughput single-microbe transcriptome profiling tool that will facilitate future discoveries in microbial resistance, persistence, microbe-host interaction, and microbiome research.

With increasingly mature commercial operations, football has become the most popular sport in the world. As the main body of football, athletes are prone to injury due to an increasing degree of competition intensity. Their health determines the length of these athletes careers, especially regarding the lower limbs that are mainly used. Therefore, the smart visualization approaches that can realize such function are in urgent demand in the area of sports healthcare. Benefitted by the strong ability of perception and analysis, a convolutional neural network (CNN) is utilized to construct an intelligent data analysis-based medical management method for the lower limb health of football athletes. First, the CNN is formulated as the main backbone, and its parameters are optimized for multiple rounds during the training stage. Then, a statistical analysis software named SPSS is introduced to assess the effect mechanism of different postures on lower limbs. Some experiments are carried out on simulative data to evaluate the proposed method, and results show a good performance of the proposed method.

BackgroundAssessing the impact of dietary live microbe intake on health outcomes has gained increasing interest. This study aimed to elucidate the relationship between dietary live microbe intake and Life's Essential 8 (LE8) scores, a metric for cardiovascular health (CVH), in the U.S. adult population.MethodsWe analyzed data from 10,531 adult participants of the National Health and Nutrition Examination Survey (NHANES) spanning 2005–2018. Participants were stratified into low, medium, and high intake groups of dietary live microbe based on Marco's classification system. We employed weighted logistic and linear regression analyses, along with subgroup, interaction effect, and sensitivity analyses. Additionally, Restricted Cubic Splines (RCS) were used to explore the dose-response relationship between food intake and CVH in different groups.ResultsCompared to the low live microbe intake group, the medium and high live microbe intake groups had significantly higher LE8, with β coefficients of 2.75 (95% CI: 3.89–5.65) and 3.89 (95% CI: 6.05–8.11) respectively. Additionally, moderate and high groups significantly reduced the risk of high cardiovascular health risk, defined as an LE8 score below 50, with odds ratios (OR) of 0.73 and 0.65 respectively. Subgroup analysis and sensitivity analysis proved the stability of the results. In the low intake group, food intake shows a linear negative correlation with LE8, whereas in the high intake group, it exhibits a linear positive correlation. In contrast, in the moderate live microbe intake group, the relationship between food intake and LE8 presents a distinct inverted “U” shape.ConclusionThis study highlights the potential benefits of medium to high dietary intake of live microbe in improving LE8 scores and CVH in adults. These findings advocate for the inclusion of live microbes in dietary recommendations, suggesting their key role in CVH enhancement.

Abstract Tumor heterogeneity and its drivers impair tumor progression and cancer therapy. Single‐cell RNA sequencing is used to investigate the heterogeneity of tumor ecosystems. However, most methods of scRNA‐seq amplify the termini of polyadenylated transcripts, making it challenging to perform total RNA analysis and somatic mutation analysis.Therefore, a high‐throughput and high‐sensitivity method called snHH‐seq is developed, which combines random primers and a preindex strategy in the droplet microfluidic platform. This innovative method allows for the detection of total RNA in single nuclei from clinically frozen samples. A robust pipeline to facilitate the analysis of full‐length RNA‐seq data is also established. snHH‐seq is applied to more than 730 000 single nuclei from 32 patients with various tumor types. The pan‐cancer study enables it to comprehensively profile data on the tumor transcriptome, including expression levels, mutations, splicing patterns, clone dynamics, etc. New malignant cell subclusters and exploring their specific function across cancers are identified. Furthermore, the malignant status of epithelial cells is investigated among different cancer types with respect to mutation and splicing patterns. The ability to detect full‐length RNA at the single‐nucleus level provides a powerful tool for studying complex biological systems and has broad implications for understanding tumor pathology.

AbstractIn a complex navigation environment, it is very important to solve the problems of multiple constraints and complex calculations in the route planning process of multiple reconnaissance unmanned aerial vehicles (UAVs) to improve the flight reconnaissance effect, and the bat algorithm (BA) with simple parameters has a certain effect on it. Aiming at the problems of unbalanced global optimization and local optimization and slow convergence speed in the later iteration of the BA in the path planning of multi-reconnaissance UAVs, a Levy-based flight search strategy and an improved bat algorithm (IBA) with changing speed are designed. In order to solve the multi-UAV route planning problem, the 3D space is reduced to a divided two-dimensional space based on the “scatter plot” method, and the spatial encoding method is used. Finally, it is proved that the IBA is theoretically more concise and effective than other algorithms.

Abstract Formalin-fixed paraffin-embedded (FFPE) tissues constitute a vast and valuable patient material bank for clinical history and follow-up data. It is still challenging to achieve single cell/nucleus RNA (sc/snRNA) profile in FFPE tissues. Here, we develop a droplet-based snRNA sequencing technology (snRandom-seq) for FFPE tissues by capturing full-length total RNAs with random primers. snRandom-seq shows a minor doublet rate (0.3%), a much higher RNA coverage, and detects more non-coding RNAs and nascent RNAs, compared with state-of-art high-throughput scRNA-seq technologies. snRandom-seq detects a median of >3000 genes per nucleus and identifies 25 typical cell types. Moreover, we apply snRandom-seq on a clinical FFPE human liver cancer specimen and reveal an interesting subpopulation of nuclei with high proliferative activity. Our method provides a powerful snRNA-seq platform for clinical FFPE specimens and promises enormous applications in biomedical research.

Ship detection aims to automatically identify whether there are ships in the images, precisely classifies and localizes them. Regardless of whether utilizing early manually designed methods or deep learning technology, ship detection is dedicated to exploring the inherent characteristics of ships to enhance recall. Nowadays, high-precision ship detection plays a crucial role in civilian and military applications. In order to provide a comprehensive review of ship detection in optical remote-sensing images (SDORSIs), this paper summarizes the challenges as a guide. These challenges include complex marine environments, insufficient discriminative features, large scale variations, dense and rotated distributions, large aspect ratios, and imbalances between positive and negative samples. We meticulously review the improvement methods and conduct a detailed analysis of the strengths and weaknesses of these methods. We compile ship information from common optical remote sensing image datasets and compare algorithm performance. Simultaneously, we compare and analyze the feature extraction capabilities of backbones based on CNNs and Transformer, seeking new directions for the development in SDORSIs. Promising prospects are provided to facilitate further research in the future.

The complementary characteristics of SAR and optical images are beneficial in improving the accuracy of land cover classification. Deep learning-based models have achieved some notable results. However, how to effectively extract and fuse the unique features of multi-modal images for pixel-level classification remains challenging. In this article, a two-branch supervised semantic segmentation framework without any pretrained backbone is proposed. Specifically, a novel symmetric attention module is designed with improved strip pooling. The multiple long receptive fields can better perceive irregular objects and obtain more anisotropic contextual information. Meanwhile, to solve the semantic absence and inconsistency of different modalities, we construct a multi-scale fusion module, which is composed of atrous spatial pyramid pooling, varisized convolutions and skip connections. A joint loss function is introduced to constrain the backpropagation and reduce the impact of class imbalance. Validation experiments were implemented on the DFC2020 and WHU-OPT-SAR datasets. The proposed model achieved the best quantitative values on the metrics of OA, Kappa and mIoU, and its class accuracy was also excellent. It is worth mentioning that the number of parameters and the computational complexity of the method are relatively low. The adaptability of the model was verified on RGB–thermal segmentation task.

Summary: Although single-cell multi-omics technologies are undergoing rapid development, simultaneous transcriptome and proteome analysis of a single-cell individual still faces great challenges. Here, we developed a single-cell simultaneous transcriptome and proteome (scSTAP) analysis platform based on microfluidics, high-throughput sequencing, and mass spectrometry technology to achieve deep and joint quantitative analysis of transcriptome and proteome at the single-cell level, providing an important resource for understanding the relationship between transcription and translation in cells. This platform was applied to analyze single mouse oocytes at different meiotic maturation stages, reaching an average quantification depth of 19,948 genes and 2,663 protein groups in single mouse oocytes. In particular, we analyzed the correlation of individual RNA and protein pairs, as well as the meiosis regulatory network with unprecedented depth, and identified 30 transcript-protein pairs as specific oocyte maturational signatures, which could be productive for exploring transcriptional and translational regulatory features during oocyte meiosis.

In remote sensing image (RSI) object detection, the oriented bounding box (OBB) can accurately locate objects with arbitrary orientation and obtain orientation information. The detection based on OBB is still a challenging task. In RSI, the distribution of objects is extremely uneven, which causes aggregation to occur. Some researchers believe that the characteristic of dense distribution is a reason for the difficulty of object detection. However, there are no in-depth experimental studies on this. This paper proposes an OBB-based dense object determination method, which determines the dense objects in datasets by two conditions consisting of interclass distance, intraclass distance, minimum distance between objects, and minimum edge length of objects. The experimental results of dense and non-dense object detection concludes that the characteristics of dense distribution in RSI do not easily cause the objects to be more difficult to detect. To make full use of the object features, we propose a second-stage detection head named RoIF-Net, in which we extract region of interest (RoI) from the input image and fuse it with the RoI extracted from feature maps to add detail features, and construct a feature induction module based on self-attention mechanism to achieve position regression and category classification. This structure can be used in any two-stage network to enhance detection capabilities. Using our method on three credible and challenging datasets, DOTA, DIOR-R, and UCAS-AOD, we obtained 81.80%, 68.49%, and 90.25% mAP, respectively, reaching SOTA based on OBB detection, proving the effectiveness and advancement of our method.

Abstract Background Huangqi Guizhi Wuwu decoction (HQGZWWD) is a traditional Chinese herbal medicine formulation with significant anti-inflammatory activity. However, its underlying mechanism remains unknown. Through network pharmacology and experimental validation, this study aimed to examine the potential mechanism of HQGZWWD in regulating macrophage polarization and inflammation. Methods The active components were obtained from the Traditional Chinese Medicine Systems Pharmacology database and Analysis Platform (TCMSP), whereas the corresponding targets were obtained from the TCMSP and Swiss Target Prediction database. The GeneCards database identified targets associated with macrophage polarization and inflammation. Multiple networks were developed to identify the key compounds, principal biological processes, and pathways of HQGZWWD that regulate macrophage polarization and inflammation. Autodock Vina is utilized to assess the binding ability between targets and active compounds. Finally, confirm the experiment’s central hypothesis. Human histiocytic lymphoma (U-937) cells were transformed into M1 macrophages following stimulation with Lipopolysaccharide (LPS) to evaluate the effect of HQGZWWD drug-containing mouse serum (HQGZWWD serum) on regulating macrophage polarization and inflammation. Results A total of 54 active components and 859 HQGZWWD targets were obtained. There were 9972 targets associated with macrophage polarization and 11,109 targets associated with inflammation. After screening, 34 overlapping targets were identified, of which 5 were identified as central targets confirmed by experiments, including the α7 nicotinic acetylcholine receptor (α7 nAchR), interleukin 6 (IL-6), Interleukin-1 beta (IL-1β), interleukin 10 (IL-10) and growth factor beta (TGF-β1). Pathway enrichment analysis revealed that 34 overlapping targets were enriched in multiple pathways associated with macrophage polarization and inflammation, including the TGF beta signaling pathway, NF-kappa B signaling pathway, JAK-STAT signaling pathway, and TNF signaling pathway. Molecular docking confirmed that the majority of HQGZWWD’s compounds can bind to the target. In vitro experiments, HQGZWWD serum was shown to up-regulate the expression of α7 nAchR, reduce the number of M1 macrophages, stimulate the production of M2 macrophages, inhibit the expression of pro-inflammatory cytokines IL-6 and IL1-β, and increase the expression of anti-inflammatory cytokines IL-10 and TGF-β1. Conclusion HQGZWWD can regulate the number of M1/M2 macrophages and the level of inflammatory cytokines, and the underlying mechanism may be related to the up-regulation of α7 nAchR expression.

In pig production, reducing production costs and improving immunity are important. Grape pomace, a good agricultural by-product, has been thrown away as food waste for a long time. Recently, we found that it could be used as a new source of pig feed. We investigated the effect of grape pomace on inflammation, gut barrier function, meat quality, and growth performance in finishing pigs. Our results indicated that treatment samples showed a significant decrease in water loss, IL-1β, DAO, ROS, and MDA content (p

Fluorescent biosensors are important tools for studying cellular metabolism, but development and optimization are challenging. Koveal et al. present a high-throughput multiparameter screen for sensor performance, and used it to generate LiLac, a high-performance, quantitative lactate sensor.

Urate transporter 1 (URAT1) is a clinically validated target for the treatment of hyperuricemia and gout. Due to the absence of protein structures, the molecular design of new URAT1 inhibitors generally resorts to ligand-based approaches. Two series of biphenyl carboxylic acids were designed based on the structures of URAT1 inhibitors Epaminurad and Telmisartan via a strategy of pharmacophore fusion. Fifty-one novel compounds were synthesized and most of them showed obvious inhibition against human URAT1. A1 and B21 were identified as the most potent URAT1 inhibitors in series A and B, respectively. They exhibited IC50 values of 0.93 μM and 0.17 μM, which were comparable or superior to the clinical uricosuric drug benzbromarone. The results confirmed the effectiveness of ligand-based approaches in identifying novel and potent URAT1 inhibitors.

Recently, object detection in remote sensing images (RSIs) have received extensive attention and made significant progress. Nonetheless, the arbitrary orientations of objects in RSIs make their detection a challenging task. Most of the existing detection methods are difficult to extract the orientation features of objects due to the lack of directionality of conventional convolutions. In addition, the boundary discontinuity in angle regression affects the detection of object orientations. In response to these problems, this article proposes an orientation-first refinement detector (OFRDet), which is based on a strategy that enables the detector to detect the angle of an object ahead of others and presets oriented anchors. In OFRDet, we propose an angle encoding regression module (AERM) and an angle channel attention module (ACAM). AERM transforms angle detection into multiparameter regression, which eliminates boundary discontinuities. ACAM uses convolution kernels with different angles to extract directional features purposefully according to the preset oriented anchors. After these two modules, more accurate bounding boxes are generated and sent to the refined stage to obtain the final detection results. We evaluate our method and demonstrate the effectiveness of it by conducting experiments on two challenging and credible datasets, DOTA, HRSC2016. OFRDet achieves competitive results 79.56%, 96.29% mAP on the two datasets, respectively.

Abstract Cachexia is a progressive metabolic disorder characterized by the excessive depletion of adipose tissue. This hypermetabolic condition has catastrophic impacts on the survival and quality of life for patients suffering from critical illness. However, efficient therapies to prevent adipose expenditure have not been discovered. It has been established that the circadian clock plays an important role in modulating fat metabolic processes. Recently, an increasing number of studies had provided evidence showing that disrupted circadian rhythm leads to insulin resistance and obesity; however, studies analyzing the relationship between circadian misalignment and adipose tissue expenditure in cachexia are scarce. In the present review, we cover the involvement of the circadian clocks in the regulation of adipogenesis, lipid metabolism and thermogenesis as well as inflammation in white and brown adipose tissue. According to the present review, we conclude that circadian clock disruption is associated with lipid metabolism imbalance and elevated adipose tissue inflammation. Moreover, under cachexia conditions, lipid synthesis and storage processes lost rhythm and decreased, while lipolysis and thermogenesis activities remained high for 24 h. Therefore, disordered circadian clock may be responsible for fat expenditure in cachexia by adversely influencing lipid synthesis/ storage/lipolysis/utilization. Further study needs to be performed to explore the direct interaction between circadian clock and fat expenditure in cachexia, it will likely provide potential efficient drugs for the treatment of fat expenditure in cachexia.

In deep learning-based hyperspectral remote sensing image classification tasks, random sampling strategies are typically used to train model parameters for testing and evaluation. However, this approach leads to strong spatial autocorrelation between the training set samples and the surrounding test set samples, and some unlabeled test set data directly participate in the training of the network. This leaked information makes the model overly optimistic. Models trained under these conditions tend to overfit to a single dataset, which limits the range of practical applications. This paper analyzes the causes and effects of information leakage and summarizes the methods from existing models to mitigate the effects of information leakage. Specifically, this paper states the main issues in this area, where the issue of information leakage is addressed in detail. Second, some algorithms and related models used to mitigate information leakage are categorized, including reducing the number of training samples, using spatially disjoint sampling strategies, few-shot learning, and unsupervised learning. These models and methods are classified according to the sample-related phase and the feature extraction phase. Finally, several representative hyperspectral image classification models experiments are conducted on the common datasets and their effectiveness in mitigating information leakage is analyzed.

Abstract Background Guizhi decoction (GZD), a classical Chinese herbal formula, has been widely used to treat hypertension, but its underlying mechanisms remain elusive. The present study aimed to explore the potential mechanisms and therapeutic effects of GZD on hypertension by integrating network pharmacology and experimental validation. Methods The active ingredients and corresponding targets were collected from the Traditional Chinese Medicine Systems Pharmacology database and Analysis Platform (TCMSP). The targets related to hypertension were identified from the CTD, GeneCards, OMIM and Drugbank databases. Multiple networks were constructed to identify the key compounds, hub targets, and main biological processes and pathways of GZD against hypertension. The Surflex-Dock software was used to validate the binding affinity between key targets and their corresponding active compounds. The Dahl salt-sensitive rat model was used to evaluate the therapeutic effects of GZD against hypertension. Results A total of 112 active ingredients, 222 targets of GZD and 341 hypertension-related targets were obtained. Furthermore, 56 overlapping targets were identified, five of which were determined as the hub targets for experimental verification, including interleukin 6 (IL-6), C–C motif chemokine 2 (CCL2), IL-1β, matrix metalloproteinase 2 (MMP-2), and MMP-9. Pathway enrichment analysis results indicated that 56 overlapping targets were mainly enriched in several inflammation pathways such as the tumor necrosis factor (TNF) signaling pathway, Toll-like receptor (TLR) signaling pathway and nuclear factor kappa-B (NF-κB) signaling pathway. Molecular docking confirmed that most active compounds of GZD could bind tightly to the key targets. Experimental studies revealed that the administration of GZD improved blood pressure, reduced the area of cardiac fibrosis, and inhibited the expression of IL-6, CCL2, IL-1β, MMP-2 and MMP-9 in rats. Conclusion The potential mechanisms and therapeutic effects of GZD on hypertension may be attributed to the regulation of cardiac inflammation and fibrosis.

Hyperspectral image (HSI) super-resolution (SR) is a classical computer vision task that aims to accomplish the conversion of images from lower to higher resolutions. With the booming development of deep learning (DL) technology, more and more researchers are dedicated to the research of image SR techniques based on DL and have made remarkable progress. However, no scholar has provided a comprehensive review of the field. As a response, in this paper we aim to supply a comprehensive summary of the DL-based SR techniques for HSI, including upsampling frameworks, upsampling methods, network design, loss functions, representative works with different strategies, and future directions, in which we design several sets of comparative experiments for the advantages and limitations of two-dimensional convolution and three-dimensional convolution in the field of HSI SR and analyze the experimental results in depth. In addition, the paper also briefly discusses the secondary foci such as common datasets, evaluation metrics, and traditional SR algorithms. To the best of our knowledge, this paper is the first review on DL-based HSI SR.

Image super-resolution (SR) technique can improve the spatial resolution of images without upgrading the imaging system. As a result, SR promotes the development of high resolution (HR) remote sensing image applications. Many remote sensing image SR algorithms based on deep learning have been proposed recently, which can effectively improve the spatial resolution under the constraints of HR images. However, images acquired by remote sensing imaging devices typically have lower resolution. Hence, an insufficient number of HR remote sensing images are available for training deep neural networks. In view of this problem, we propose an unsupervised SR method that does not require HR remote sensing images. The proposed method introduces a generative adversarial network (GAN) that obtains SR images through the generator; then, the SR images are downsampled to train the discriminator with low resolution (LR) images. Our method outperformed several methods in terms of the quality of the obtained SR images as measured by 6 evaluation metrics, which proves the satisfactory performance of the proposed unsupervised method for improving the spatial resolution of remote sensing images.

Recently, the classification of hyperspectral images has made great process. Especially, the classification methods based on three-dimensional convolutional neural network have remarkable performance due to the uniqueness of hyperspectral images. However, the hyperspectral classification still faces great challenges due to a series of problems such as the insufficient extraction of spectral-spatial features, the lack of labeled samples, the large amount of noise, the tendency of overfitting and so on. Therefore, SSDANet is proposed to solve the above problems and promote the further development of hyperspectral classification technology based on deep learning. SSDANet is a spectral-spatial three-dimensional convolutional neural network with a deep and wide structure that can significantly improve classification performance. In SSDANet, the spectral-spatial dense connectivity is put forward to protect the integrity of information. It is made up of the spectral branch and the spatial branch, which can learn and reuse the spectral-spatial features. Besides, the spectral-spatial attention mechanism is proposed to adapt the special structure of hyperspectral images. It can excite important spectral-spatial information and suppress unimportant spectral-spatial information. In addition, a series of optimization methods including data augmentation, batch normalization, dropout, exponential decay learning rate, and L2 regularization are adopted to alleviate the problem of overfitting and improve the classification results. To verify the performance of SSDANet, experiments were implemented on two widely used datasets-Pavia University and Indian Pines. Under the condition of limited labeled samples, the classification evaluation indexes of OA, AA, and Kappa on the two datasets all exceeded 99%, reaching state-of-the-art performance.

The fusion of infrared and visible images can utilize the indication characteristics and the textural details of source images to realize the all-weather detection. The deep learning (DL) based fusion solutions can reduce the computational cost and complexity compared with traditional methods since there is no need to design complex feature extraction methods and fusion rules. There are no standard reference images and the publicly available infrared and visible image pairs are scarce. Most supervised DL-based solutions have to take pre-training on other labeled large datasets which may not behave well when testing. The few unsupervised fusion methods can hardly obtain ideal images with good visual impression. In this paper, an infrared and visible image fusion method based on unsupervised convolutional neural network is proposed. When designing the network structure, densely connected convolutional network (DenseNet) is used as the sub-network for feature extraction and reconstruction to ensure that more information of source images can be retained in the fusion images. As to loss function, the perceptual loss is creatively introduced and combined with the structure similarity loss to constrain the updating of weight parameters during the back propagation. The perceptual loss designed helps to improve the visual information fidelity (VIF) of the fusion image effectively. Experimental results show that this method can obtain fusion images with prominent targets and obvious details. Compared with other 7 traditional and deep learning methods, the fusion results of this method are better on objective evaluation and visual observation when taken together.

Background: Exploring the potential biological relationships between heart failure with preserved ejection fraction (HFpEF) and concomitant diseases has been the focus of many studies for the establishment of personalized therapies. Hypertension (HTN) is the most common concomitant disease in HFpEF patients, but the functional connections between HFpEF and HTN are still not fully understood and effective treatment strategies are still lacking.Methods: In this study, tandem mass tag (TMT) quantitative proteomics was used to identify disease-related proteins and construct disease-related networks. Furthermore, functional enrichment analysis of overlapping network modules was used to determine the functional similarities between HFpEF and HTN. Molecular docking and module analyses were combined to identify therapeutic targets for HFpEF and HTN.Results: Seven common differentially expressed proteins (co-DEPs) and eight overlapping modules were identified in HFpEF and HTN. The common biological processes between HFpEF and HTN were mainly related to energy metabolism. Myocardial contraction, energy metabolism, apoptosis, oxidative stress, immune response, and cardiac hypertrophy were all closely associated with HFpEF and HTN. Epinephrine, sulfadimethoxine, chloroform, and prednisolone acetate were best matched with the co-DEPs by molecular docking analyses.Conclusion: Myocardial contraction, energy metabolism, apoptosis, oxidative stress, immune response, and cardiac hypertrophy were the main functional connections between HFpEF and HTN. Epinephrine, sulfadimethoxine, chloroform, and prednisolone acetate could potentially be effective for the treatment of HTN and HFpEF.

Object detection in remote sensing images (RSIs) requires the locating and classifying of objects of interest, which is a hot topic in RSI analysis research. With the development of deep learning (DL) technology, which has accelerated in recent years, numerous intelligent and efficient detection algorithms have been proposed. Meanwhile, the performance of remote sensing imaging hardware has also evolved significantly. The detection technology used with high-resolution RSIs has been pushed to unprecedented heights, making important contributions in practical applications such as urban detection, building planning, and disaster prediction. However, although some scholars have authored reviews on DL-based object detection systems, the leading DL-based object detection improvement strategies have never been summarized in detail. In this paper, we first briefly review the recent history of remote sensing object detection (RSOD) techniques, including traditional methods as well as DL-based methods. Then, we systematically summarize the procedures used in DL-based detection algorithms. Most importantly, starting from the problems of complex object features, complex background information, tedious sample annotation that will be faced by high-resolution RSI object detection, we introduce a taxonomy based on various detection methods, which focuses on summarizing and classifying the existing attention mechanisms, multi-scale feature fusion, super-resolution and other major improvement strategies. We also introduce recognized open-source remote sensing detection benchmarks and evaluation metrics. Finally, based on the current state of the technology, we conclude by discussing the challenges and potential trends in the field of RSOD in order to provide a reference for researchers who have just entered the field.

Abstract Droplet‐based single cell sequencing technologies, such as inDrop, Drop‐seq, and 10X Genomics, are catalyzing a revolution in the understanding of biology. Barcoding beads are key components for these technologies. What is limiting today are barcoding beads that are easy to fabricate, can efficiently deliver primers into drops, and thus achieve high detection efficiency. Here, this work reports an approach to fabricate dissolvable polyacrylamide beads, by crosslinking acrylamide with disulfide bridges that can be cleaved with dithiothreitol. The beads can be rapidly dissolved in drops and release DNA barcode primers. The dissolvable beads are easy to synthesize, and the primer cost for the beads is significantly lower than that for the previous barcoding beads. Furthermore, the dissolvable beads can be loaded into drops with >95% loading efficiency of a single bead per drop and the dissolution of beads does not influence reverse transcription or the polymerase chain reaction (PCR) in drops. Based on this approach, the dissolvable beads are used for single cell RNA and protein analysis.

Link prediction in complex networks predicts the possibility of link generation between two nodes that have not been linked yet in the network, based on known network structure and attributes. It can be applied in various fields, such as friend recommendation in social networks and prediction of protein-protein interaction in biology. However, in the social network, link prediction may raise concerns about privacy and security, because, through link prediction algorithms, criminals can predict the friends of an account user and may even further discover private information such as the address and bank accounts. Therefore, it is urgent to develop a strategy to prevent being identified by link prediction algorithms and protect privacy, utilizing perturbation on network structure at a low cost, including changing and adding edges. This article mainly focuses on the influence of network structural preference perturbation through deletion on link prediction. According to a large number of experiments on the various real networks, edges between large-small degree nodes and medium-medium degree nodes have the most significant impact on the quality of link prediction.

A local search Maximum Likelihood (ML) parameter estimator for mono-component chirp signal in low Signal-to-Noise Ratio (SNR) conditions is proposed in this paper. The approach combines a deep learning denoising method with a two-step parameter estimator. The denoiser utilizes residual learning assisted Denoising Convolutional Neural Network (DnCNN) to recover the structured signal component, which is used to denoise the original observations. Following the denoising step, we employ a coarse parameter estimator, which is based on the Time-Frequency (TF) distribution, to the denoised signal for approximate estimation of parameters. Then around the coarse results, we do a local search by using the ML technique to achieve fine estimation. Numerical results show that the proposed approach outperforms several methods in terms of parameter estimation accuracy and efficiency.

Infrared and visible image fusion can obtain combined images with salient hidden objectives and abundant visible details simultaneously. In this paper, we propose a novel method for infrared and visible image fusion with a deep learning framework based on a generative adversarial network (GAN) and a residual network (ResNet). The fusion is accomplished with an adversarial game and directed by the unique loss functions. The generator with residual blocks and skip connections can extract deep features of source image pairs and generate an elementary fused image with infrared thermal radiation information and visible texture information, and more details in visible images are added to the final images through the discriminator. It is unnecessary to design the activity level measurements and fusion rules manually, which are now implemented automatically. Also, there are no complicated multi-scale transforms in this method, so the computational cost and complexity can be reduced. Experiment results demonstrate that the proposed method eventually gets desirable images, achieving better performance in objective assessment and visual quality compared with nine representative infrared and visible image fusion methods.

One of the challenges in the field of remote sensing is how to automatically identify and classify high-resolution remote sensing images. A number of approaches have been proposed. Among them, the methods based on low-level visual features and middle-level visual features have limitations. Therefore, this paper adopts the method of deep learning to classify scenes of high-resolution remote sensing images to learn semantic information. Most of the existing methods of convolutional neural networks are based on the existing model using transfer learning, while there are relatively few articles about designing of new convolutional neural networks based on the existing high-resolution remote sensing image datasets. In this context, this paper proposes a multi-view scaling strategy, a new convolutional neural network based on residual blocks and fusing strategy of pooling layer maps, and uses optimization methods to make the convolutional neural network named RFPNet more robust. Experiments on two benchmark remote sensing image datasets have been conducted. On the UC Merced dataset, the test accuracy, precision, recall, and F1-score all exceed 93%. On the SIRI-WHU dataset, the test accuracy, precision, recall, and F1-score all exceed 91%. Compared with the existing methods, such as the most traditional methods and some deep learning methods for scene classification of high-resolution remote sensing images, the proposed method has higher accuracy and robustness.