The trend of lymphoma incidence in China from 2005 to 2017 and lymphoma incidence trend prediction from 2018 to 2035: a log-linear regression and Bayesian age-period-cohort analysis.

Objectives: The aims of this study were to explore the incidence characteristics and trend prediction of lymphoma from 2005 to 2035, and to provide data basis for the prevention and control of lymphoma in China. Method: The data on lymphoma incidence in China from 2005 to 2017 were obtained from the Chinese Cancer Registry Annual Report. The Joinpoint regression model was used to calculate annual percentage change (APC) and average annual percentage change (AAPC) to reflect time trends. Age-period-cohort models were conducted to estimate age, period, and cohort effects on the lymphoma incidence. A Bayesian age-period-cohort model was used to predict lymphoma incidence trends from 2018 to 2035. Results: From 2005 to 2017, the incidence of lymphoma was 6.26/100,000, and the age-standardized incidence rate (ASIR) was 4.11/100,000, with an AAPC of 1.4% [95% confidence interval (CI): 0.3%, 2.5%]. The ASIR was higher in men and urban areas than in women and rural areas, respectively. The age effect showed that the incidence risk of lymphoma increased with age. In the period effect, the incidence risk of lymphoma in rural areas decreased first and then increased with 2010 as the cutoff point. The overall risk of lymphoma incidence was higher in the cohort before the 1970-1974 birth cohort than in the cohort after. From 2018 to 2035, the lymphoma incidence in men, women, and urban areas will show an upward trend. Conclusion: From 2005 to 2017, the incidence of lymphoma showed an increasing trend, and was different in regions, genders, and age groups in China. It will show an upward trend from 2018 to 2035. These results are helpful for the formulation and adjustment of lymphoma prevention, control, and management strategies, and have important reference significance for the treatment of lymphoma in China.

Metagenomic next-generation sequencing-assisted diagnosis of a rare case of primary cutaneous acanthamoebiasis in an HIV patient: a case report.

Pathogenic and free-living Acanthamoeba are widely distributed in the environment and have been reported to cause keratitis and universally fatal encephalitis. Primary cutaneous acanthamoebiasis caused by Acanthamoeba is exceedingly rare and presents as isolated necrotic cutaneous lesions without involvement of the cornea or central nervous system. Cutaneous acanthamoebiasis often occurs in immunocompromised patients and is likely overlooked or even misdiagnosed only by cutaneous biopsy tissue histopathological analysis. Here, we report a HIV-infected 63-year-old female with oral leukoplakia for 4 months and scattered large skin ulcers all over the body for 2 months. The cause of the cutaneous lesions was unclear through cutaneous specimens histopathological analysis, and subsequently Acanthamoeba were detected by metagenomic next-generation sequencing (mNGS), which may be the cause of cutaneous lesions. Based on the mNGS results, a pathologist subsequently reviewed the previous pathological slides and found trophozoites of Acanthamoeba so that the cause was identified, and the skin ulcers improved significantly after treatment with multi-drug combination therapy. Acanthamoeba is also a host of pathogenic microorganisms. The presence of endosymbionts enhances the pathogenicity of Acanthamoeba, and no other pathogens were reported in this case. mNGS is helpful for rapidly diagnosing the etiology of rare skin diseases and can indicate the presence or absence of commensal microorganisms.

Chronic restraint stress induces depression-like behaviors and alterations in the afferent projections of medial prefrontal cortex from multiple brain regions in mice.

INTRODUCTION: The medial prefrontal cortex (mPFC) forms output pathways through projection neurons, inversely receiving adjacent and long-range inputs from other brain regions. However, how afferent neurons of mPFC are affected by chronic stress needs to be clarified. In this study, the effects of chronic restraint stress (CRS) on the distribution density of mPFC dendrites/dendritic spines and the projections from the cortex and subcortical brain regions to the mPFC were investigated. METHODS: In the present study, C57BL/6 J transgenic (Thy1-YFP-H) mice were subjected to CRS to establish an animal model of depression. The infralimbic (IL) of mPFC was selected as the injection site of retrograde AAV using stereotactic technique. The effects of CRS on dendrites/dendritic spines and afferent neurons of the mPFC IL were investigaed by quantitatively assessing the distribution density of green fluorescent (YFP) positive dendrites/dendritic spines and red fluorescent (retrograde AAV recombinant protein) positive neurons, respectively. RESULTS: The results revealed that retrograde tracing virus labeled neurons were widely distributed in ipsilateral and contralateral cingulate cortex (Cg1), second cingulate cortex (Cg2), prelimbic cortex (PrL), infralimbic cortex, medial orbital cortex (MO), and dorsal peduncular cortex (DP). The effects of CRS on the distribution density of mPFC red fluorescence positive neurons exhibited regional differences, ranging from rostral to caudal or from top to bottom. Simultaneously, CRS resulted a decrease in the distribution density of basal, proximal and distal dendrites, as well as an increase in the loss of dendritic spines of the distal dendrites in the IL of mPFC. Furthermore, varying degrees of red retrograde tracing virus fluorescence signals were observed in other cortices, amygdala, hippocampus, septum/basal forebrain, hypothalamus, thalamus, mesencephalon, and brainstem in both ipsilateral and contralateral brain. CRS significantly reduced the distribution density of red fluorescence positive neurons in other cortices, hippocampus, septum/basal forebrain, hypothalamus, and thalamus. Conversely, CRS significantly increased the distribution density of red fluorescence positive neurons in amygdala. CONCLUSION: Our results suggest a possible mechanism that CRS leads to disturbances in synaptic plasticity by affecting multiple inputs to the mPFC, which is characterized by a decrease in the distribution density of dendrites/dendritic spines in the IL of mPFC and a reduction in input neurons of multiple cortices to the IL of mPFC as well as an increase in input neurons of amygdala to the IL of mPFC, ultimately causing depression-like behaviors.

GETdb: A comprehensive database for genetic and evolutionary features of drug targets.

The development of an innovative drug is complex and time-consuming, and the drug target identification is one of the critical steps in drug discovery process. Effective and accurate identification of drug targets can accelerate the drug development process. According to previous research, evolutionary and genetic information of genes has been found to facilitate the identification of approved drug targets. In addition, allosteric proteins have great potential as targets due to their structural diversity. However, this information that could facilitate target identification has not been collated in existing drug target databases. Here, we construct a comprehensive drug target database named Genetic and Evolutionary features of drug Targets database (GETdb, http://zhanglab.hzau.edu.cn/GETdb/page/index.jsp). This database not only integrates and standardizes data from dozens of commonly used drug and target databases, but also innovatively includes the genetic and evolutionary information of targets. Moreover, this database features an effective allosteric protein prediction model. GETdb contains approximately 4000 targets and over 29,000 drugs, and is a user-friendly database for searching, browsing and downloading data to facilitate the development of novel targets.

Synchronization in scale-free neural networks under electromagnetic radiation.

The functional networks of the human brain exhibit the structural characteristics of a scale-free topology, and these neural networks are exposed to the electromagnetic environment. In this paper, we consider the effects of magnetic induction on synchronous activity in biological neural networks, and the magnetic effect is evaluated by the four-stable discrete memristor. Based on Rulkov neurons, a scale-free neural network model is established. Using the initial value and the strength of magnetic induction as control variables, numerical simulations are carried out. The research reveals that the scale-free neural network exhibits multiple coexisting behaviors, including resting state, period-1 bursting synchronization, asynchrony, and chimera states, which are dependent on the different initial values of the multi-stable discrete memristor. In addition, we observe that the strength of magnetic induction can either enhance or weaken the synchronization in the scale-free neural network when the parameters of Rulkov neurons in the network vary. This investigation is of significant importance in understanding the adaptability of organisms to their environment.

Translatome and Transcriptome Analyses Reveal the Mechanism that Underlies the Enhancement of Salt Stress by the Small Peptide Ospep5 in Plants.

Salt stress significantly impedes plant growth and the crop yield. This study utilized de novo transcriptome assembly and ribosome profiling to explore mRNA translation's role in rice salt tolerance. We identified unrecognized translated open reading frames (ORFs), including 42 upstream transcripts and 86 unannotated transcripts. A noteworthy discovery was the role of a small ORF, Ospep5, in conferring salt tolerance. Overexpression of Ospep5 in plants increased salt tolerance, while its absence led to heightened sensitivity. This hypothesis was corroborated by the findings that exogenous application of the synthetic small peptide Ospep5 bolstered salt tolerance in both rice and Arabidopsis. We found that the mechanism underpinning the Ospep5-mediated salt tolerance involves the maintenance of intracellular Na+/K+ homeostasis, facilitated by upregulation of high-affinity potassium transporters (HKT) and Na+/H+ exchangers (SOS1). Furthermore, a comprehensive multiomics approach, particularly ribosome profiling, is instrumental in uncovering unannotated ORFs and elucidating their functions in plant stress responses.

Remimazolam Dosing for Gastroscopy: A Randomized Noninferiority Trial.

BACKGROUND: Remimazolam, an ultra-short-acting benzodiazepine, may provide adequate sedation for endoscopy while causing less cardiovascular or respiratory disturbance than propofol. Although fixed-dose administration is suggested, body weight affects the volume of the central chamber and thus affects the sedation depth that can be achieved by the first dose. This study aimed to compare the efficacy and safety of different doses of remimazolam and propofol by body weight for sedation during gastroscopy. METHODS: This multicenter, randomized, single-blind, parallel-controlled noninferiority trial recruited patients from five centers between March 2021 and July 2022. A total of 1,883 patients scheduled to undergo gastroscopy were randomized to groups receiving 0.15 mg/kg remimazolam, 0.2 mg/kg remimazolam, or 1.5 mg/kg propofol. The noninferiority margin was set to 5%. The primary outcome was the success rate of sedation. Adverse events were recorded to evaluate safety. RESULTS: The sedation success rate of the 0.2 mg/kg remimazolam group was not inferior to that of the 1.5 mg/kg propofol group (98.7% vs. 99.4%; risk difference, -0.64%; 97.5% CI, -2.2 to 0.7%, meeting criteria for noninferiority). However, the sedation success rate of the 0.15 mg/kg remimazolam group was 88.5%, and that of the 1.5 mg/kg propofol group was 99.4% (risk difference, -10.8%; 97.5% CI, -14.0% to -8.0%), demonstrating inferiority. Simultaneously, the overall adverse events rate of remimazolam was lower than that of propofol, and the incidence of bradycardia, hypotension, subclinical respiratory depression, and hypoxia in the remimazolam groups was significantly lower than that in the propofol group. CONCLUSIONS: This trial established the noninferior sedation success rate of remimazolam (0.2 mg/kg but not 0.15 mg/kg) compared with propofol (1.5 mg/kg), with a superior safety profile.

GBDT_KgluSite: An improved computational prediction model for lysine glutarylation sites based on feature fusion and GBDT classifier.

BACKGROUND: Lysine glutarylation (Kglu) is one of the most important Post-translational modifications (PTMs), which plays significant roles in various cellular functions, including metabolism, mitochondrial processes, and translation. Therefore, accurate identification of the Kglu site is important for elucidating protein molecular function. Due to the time-consuming and expensive limitations of traditional biological experiments, computational-based Kglu site prediction research is gaining more and more attention. RESULTS: In this paper, we proposed GBDT_KgluSite, a novel Kglu site prediction model based on GBDT and appropriate feature combinations, which achieved satisfactory performance. Specifically, seven features including sequence-based features, physicochemical property-based features, structural-based features, and evolutionary-derived features were used to characterize proteins. NearMiss-3 and Elastic Net were applied to address data imbalance and feature redundancy issues, respectively. The experimental results show that GBDT_KgluSite has good robustness and generalization ability, with accuracy and AUC values of 93.73%, and 98.14% on five-fold cross-validation as well as 90.11%, and 96.75% on the independent test dataset, respectively. CONCLUSION: GBDT_KgluSite is an effective computational method for identifying Kglu sites in protein sequences. It has good stability and generalization ability and could be useful for the identification of new Kglu sites in the future. The relevant code and dataset are available at https://github.com/flyinsky6/GBDT_KgluSite .

Role of body mass index in pregnancy outcomes after emergency cerclage for cervical insufficiency in singleton pregnant patients.

BACKGROUND: The study aims were to analyze pregnancy outcomes after the use of emergency cerclage in patients with different BMIs. METHODS: A total of 76 singleton pregnant patients who underwent emergency cerclage at a tertiary comprehensive hospital in China between Jan 2017 and Dec 2021 were retrospectively divided into an obesity group of 37 patients with BMIs ≥ 28 kg/m2 and a non-obesity group of 39 patients with BMIs < 28 kg/m2. The medical records of patients were reviewed and all relevant clinical data were further collected into an itemized data spreadsheet for various analyses. RESULTS: Emergent cerclage, along with amnioreduction if needed, could be safely performed on both obese and non-obese pregnant women with a dilated external cervix (> 1 cm), which effectively prolonged the gestational week up to ≥ 25 weeks. Obese gravidae had shorter suture-to-delivery intervals and mean pregnancy lengths but more spontaneous preterm births before 37 weeks, and a lower live birth rate (P < 0.05). Logistic regression analysis revealed that BMI, how many times cerclages have been performed during pregnancy (frequency of cerclage) and bacterial vaginosis, aerobic vaginitis and vulvovaginal candidiasis (vaginal microecology) were significantly correlated with fetal loss (P < 0.05), while rank correlation analysis established a negative correlation between BMI values and the suture-to-delivery interval (P = 0.031). CONCLUSIONS: Pregnant cervical insufficiency patients with BMIs > 28 kg/m2 may ill-serve the gestational outcomes and suture-to-delivery interval after their emergent cerclage. Additionally, BMI, frequency of cerclage and vaginal microecology accounted for higher fetal loss in patients who underwent emergency cerclage.

Corrigendum: Detection algorithm for pigmented skin disease based on classifier-level and feature-level fusion.

[This corrects the article DOI: 10.3389/fpubh.2022.1034772.].

GABNet: global attention block for retinal OCT disease classification.

Introduction: The retina represents a critical ocular structure. Of the various ophthalmic afflictions, retinal pathologies have garnered considerable scientific interest, owing to their elevated prevalence and propensity to induce blindness. Among clinical evaluation techniques employed in ophthalmology, optical coherence tomography (OCT) is the most commonly utilized, as it permits non-invasive, rapid acquisition of high-resolution, cross-sectional images of the retina. Timely detection and intervention can significantly abate the risk of blindness and effectively mitigate the national incidence rate of visual impairments. Methods: This study introduces a novel, efficient global attention block (GAB) for feed forward convolutional neural networks (CNNs). The GAB generates an attention map along three dimensions (height, width, and channel) for any intermediate feature map, which it then uses to compute adaptive feature weights by multiplying it with the input feature map. This GAB is a versatile module that can seamlessly integrate with any CNN, significantly improving its classification performance. Based on the GAB, we propose a lightweight classification network model, GABNet, which we develop on a UCSD general retinal OCT dataset comprising 108,312 OCT images from 4686 patients, including choroidal neovascularization (CNV), diabetic macular edema (DME), drusen, and normal cases. Results: Notably, our approach improves the classification accuracy by 3.7% over the EfficientNetV2B3 network model. We further employ gradient-weighted class activation mapping (Grad-CAM) to highlight regions of interest on retinal OCT images for each class, enabling doctors to easily interpret model predictions and improve their efficiency in evaluating relevant models. Discussion: With the increasing use and application of OCT technology in the clinical diagnosis of retinal images, our approach offers an additional diagnostic tool to enhance the diagnostic efficiency of clinical OCT retinal images.

Endothelial TFEB signaling-mediated autophagic disturbance initiates microglial activation and cognitive dysfunction.

Cognitive impairment caused by systemic chemotherapy is a critical question that perplexes the effective implementation of clinical treatment, but related molecular events are poorly understood. Herein, we show that bortezomib exposure leads to microglia activation and cognitive impairment, this occurs along with decreased nuclear translocation of TFEB (transcription factor EB), which is linked to macroautophagy/autophagy disorder, STAT3 (signal transducer and activator of transcription 3) phosphorylation and IL23A (interleukin 23 subunit alpha) expression. Pharmacological enhancement of TFEB nuclear translocation by digoxin restores lysosomal function and reduces STAT3-dependent endothelial IL23A secretion. As a consequence, we found that brain endothelial-specific ablation of Il23a ameliorated both microglia activation and cognitive dysfunction. Thus, the endothelial TFEB-STAT3-IL23A axis in the brain represents a critical cellular event for initiating bortezomib-mediated aberrant microglial activation and synapse engulfment. Our results suggest the reversal of TFEB nuclear translocation may provide a novel therapeutic approach to prevent symptoms of cognitive dysfunction during clinical use of bortezomib.Abbreviations: AAV: adeno-associated virus; BBB: blood-brain barrier; BTZ: bortezomib; DG: digoxin; DGs: dentate gyrus; DLG4/PSD95: discs large MAGUK scaffold protein 4; HBMECs: human brain microvascular endothelial cells; HP: hippocampus; IL23A: interleukin 23 subunit alpha; MBVECs: mouse brain vascular endothelial cells; mPFC: medial prefrontal cortex; NORT: novel object recognition test; OLT: object location test; PLX5622: 6-fluoro-N-([5-fluoro-2-methoxypyridin-3-yl]methyl)-5-(5-methyl-1H-pyrrolo[2,3-b]pyridin-3- yl)methyl; PPP3/calcineurin: protein phosphatase 3; SBEs: STAT3 binding elements; shRNA: small hairpin RNA; SLC17A7/VGLUT1: solute carrier family 17 member 7; SLC32A1/VGAT: solute carrier family 32 member 1; STAT3: signal transducer and activator of transcription 3, TFEB: transcription factor EB; Ub: ubiquitin.

PAM-independent ultra-specific activation of CRISPR-Cas12a via sticky-end dsDNA.

Although CRISPR-Cas12a [clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 12a] combining pre-amplification technology has the advantage of high sensitivity in biosensing, its generality and specificity are insufficient, which greatly restrains its application range. Here, we discovered a new targeting substrate for LbaCas12a (Lachnospiraceae bacterium Cas12a), namely double-stranded DNA (dsDNA) with a sticky-end region (PAM-SE+ dsDNA). We discovered that CRISPR-Cas12a had special enzymatic properties for this substrate DNA, including the ability to recognize and cleave it without needing a protospacer adjacent motif (PAM) sequence and a high sensitivity to single-base mismatches in that substrate. Further mechanism studies revealed that guide RNA (gRNA) formed a triple-stranded flap structure with the substrate dsDNA. We also discovered the property of low-temperature activation of CRISPR-Cas12a and, by coupling with the unique DNA hybridization kinetics at low temperature, we constructed a complete workflow for low-abundance point mutation detection in real samples, which was fast, convenient and free of single-stranded DNA (ssDNA) transformation. The detection limits were 0.005-0.01% for synthesized strands and 0.01-0.05% for plasmid genomic DNA, and the mutation abundances provided by our system for 28 clinical samples were in accordance with next-generation sequencing results. We believe that our work not only reveals novel information about the target recognition mechanism of the CRISPR-Cas12a system, but also greatly broadens its application scenarios.

Development of a Cancer-Associated Fibroblast-Related Prognostic Model in Breast Cancer via Bulk and Single-Cell RNA Sequencing.

Background: The most numerous cells in the tumor microenvironment, cancer-associated fibroblasts (CAFs) play a crucial role in cancer development. Our objective was to develop a cancer-associated fibroblast breast cancer predictive model. Methods: We acquire breast cancer (BC) scRNA-seq data from Gene Expression Omnibus (GEO), and "Seurat" was used for data processing, including quality control, filtering, principal component analysis, and t-SNE. Afterward, "singleR" software was used to annotate cells. Seurat's "FindAllMarkers" program is used to locate particular CAF markers. clusterProfiler was used to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. The Cancer Genome Atlas (TCGA) database was utilized to provide univariate Cox regression, least absolute shrinkage operator (LASSO) analysis using bulk RNA-seq data. For model development, multivariate Cox regression studies are used. Utilizing pRRophetic and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms, chemosensitivity and immunotherapy response were predicted. The "rms" software was used to facilitate and simplify modeling. Results: Integrating the scRNA-seq (GSE176078) dataset yielded 28 cell clusters. In addition, well-known cell types helped identify 12 cell types. We found 193 marker genes that are elevated in CAFs. In addition, a five-gene predictive model associated to CAF was created in the training set. In the training set, the validation set, and the external validation set, greater risk scores were associated with a worse prognosis. And individuals with a higher risk score were more susceptible to immunotherapy and conventional chemotherapy medicines. Conclusion: In conclusion, we establish a strong prognostic model comprised of 5 genes related with CAF that might serve as a potent prognostic indicator and aid clinicians in making more rational medication choices.

Detection algorithm for pigmented skin disease based on classifier-level and feature-level fusion.

Pigmented skin disease is caused by abnormal melanocyte and melanin production, which can be induced by genetic and environmental factors. It is also common among the various types of skin diseases. The timely and accurate diagnosis of pigmented skin disease is important for reducing mortality. Patients with pigmented dermatosis are generally diagnosed by a dermatologist through dermatoscopy. However, due to the current shortage of experts, this approach cannot meet the needs of the population, so a computer-aided system would help to diagnose skin lesions in remote areas containing insufficient experts. This paper proposes an algorithm based on a fusion network for the detection of pigmented skin disease. First, we preprocess the images in the acquired dataset, and then we perform image flipping and image style transfer to augment the images to alleviate the imbalance between the various categories in the dataset. Finally, two feature-level fusion optimization schemes based on deep features are compared with a classifier-level fusion scheme based on a classification layer to effectively determine the best fusion strategy for satisfying the pigmented skin disease detection requirements. Gradient-weighted Class Activation Mapping (Grad_CAM) and Grad_CAM++ are used for visualization purposes to verify the effectiveness of the proposed fusion network. The results show that compared with those of the traditional detection algorithm for pigmented skin disease, the accuracy and Area Under Curve (AUC) of the method in this paper reach 92.1 and 95.3%, respectively. The evaluation indices are greatly improved, proving the adaptability and accuracy of the proposed method. The proposed method can assist clinicians in screening and diagnosing pigmented skin disease and is suitable for real-world applications.

Deep_KsuccSite: A novel deep learning method for the identification of lysine succinylation sites.

Identification of lysine (symbol Lys or K) succinylation (Ksucc) sites centralizes the basis for disclosing the mechanism and function of lysine succinylation modifications. Traditional experimental methods for Ksucc site ientification are often costly and time-consuming. Therefore, it is necessary to construct an efficient computational method to prediction the presence of Ksucc sites in protein sequences. In this study, we proposed a novel and effective predictor for the identification of Ksucc sites based on deep learning algorithms that was termed as Deep_KsuccSite. The predictor adopted Composition, Transition, and Distribution (CTD) Composition (CTDC), Enhanced Grouped Amino Acid Composition (EGAAC), Amphiphilic Pseudo-Amino Acid Composition (APAAC), and Embedding Encoding methods to encode peptides, then constructed three base classifiers using one-dimensional (1D) convolutional neural network (CNN) and 2D-CNN, and finally utilized voting method to get the final results. K-fold cross-validation and independent testing showed that Deep_KsuccSite could serve as an effective tool to identify Ksucc sites in protein sequences. In addition, the ablation experiment results based on voting, feature combination, and model architecture showed that Deep_KsuccSite could make full use of the information of different features to construct an effective classifier. Taken together, we developed Deep_KsuccSite in this study, which was based on deep learning algorithm and could achieved better prediction accuracy than current methods for lysine succinylation sites. The code and dataset involved in this methodological study are permanently available at the URL https://github.com/flyinsky6/Deep_KsuccSite.

High Sulfur-doped hollow carbon sphere with multicavity for high-performance Potassium-ion hybrid capacitors.

S doping is an effective strategy to improve the potassium-ion storage performance of carbon-based materials. However, due to the large atomic radius of S and poor thermal stability, it is challenging to synthesize carbon materials with high sulfur content by solid-phase transformation. In this work, we designed a multi-cavity structure that can confine the molten S during heat treatment and make it fully react, then achieving high S doping (7.6 at. %). As we known, S doping can also effectively increase the active sites of carbon materials to obtain higher capacity. In addition, through different ex/in-situ characterizations and DFT calculations, we confirmed that the S atoms can effectively expand the interlayer spacing of carbon, which facilitates the intercalation/deintercalation reaction of K+, thereby significantly improving the rate performance. Therefore, benefiting from the effect of S-doping, the sample exhibits high reversible specific capacity (401.0 mAh g-1 at 0.1 A/g) and rate performance (167.2 mAh g-1 at 5 A/g). The as-assembled K+ hybrid capacitor delivers both high energy density and power density (138.5 W h kg-1 and 7692.5 W kg-1, respectively). This work provides a new approach to design S content carbon-based materials for high performance K+ storage.

Gene expression pattern of microbes associated with large cyanobacterial colonies for a whole year in Lake Taihu.

Large cyanobacterial colonies, which are unique niches for heterotrophic bacteria, are vital for blooming in eutrophic waters. However, the seasonal dynamics of molecular insights into microbes in these colonies remain unclear. Here, the community composition and metabolism pattern of microbes inhabiting large cyanobacterial colonies (> 120 µm, collected from Lake Taihu in China) were investigated monthly. The community structure of total microbes was mostly influenced by chlorophyll a (Chl a), total phosphorus (TP) concentration, dissolved oxygen, and temperature, whereas the colony-associated bacteria (excluding Cyanobacteria) were mostly influenced by total organic carbon, NO3-, and PO43- concentrations, indicating different response patterns of Cyanobacteria and the associated bacteria to water nutrient conditions. Metatranscriptomic data suggested that similar to that of Cyanobacteria, the gene expression patterns of the most active bacteria, such as Proteobacteria and Bacteroidetes, were not strictly dependent on season but separated by Chl a concentrations. Samples in July and September (high-bloom period) and February and March (non-bloom period) formed two distinct clusters, whereas those of other months (low-bloom period) clustered together. The accumulation of transcripts for pathways, such as phycobilisome from Cyanobacteria and bacterial chemotaxis and flagellum, phosphate metabolism, and sulfur oxidation from Proteobacteria, was enriched in high- and low-bloom periods than in non-bloom period. Network analyses revealed that Cyanobacteria and Proteobacteria exhibited coordinated transcriptional patterns in almost all divided modules. Modules had Cyanobacteria-dominated hub gene were positively correlated with temperature, Chl a, total dissolved phosphorus, and NH4+ and NO2- concentrations, whereas modules had Proteobacteria-dominated hub gene were positively correlated with TP and PO43-. These results indicated labor division might exist in the colonies. This study provided metabolic insights into microbes in large cyanobacterial colonies and would support the understanding and management of the year-round cyanobacterial blooms.

FN-OCT: Disease Detection Algorithm for Retinal Optical Coherence Tomography Based on a Fusion Network.

Optical coherence tomography (OCT) is a new type of tomography that has experienced rapid development and potential in recent years. It is playing an increasingly important role in retinopathy diagnoses. At present, due to the uneven distributions of medical resources in various regions, the uneven proficiency levels of doctors in grassroots and remote areas, and the development needs of rare disease diagnosis and precision medicine, artificial intelligence technology based on deep learning can provide fast, accurate, and effective solutions for the recognition and diagnosis of retinal OCT images. To prevent vision damage and blindness caused by the delayed discovery of retinopathy, a fusion network (FN)-based retinal OCT classification algorithm (FN-OCT) is proposed in this paper to improve upon the adaptability and accuracy of traditional classification algorithms. The InceptionV3, Inception-ResNet, and Xception deep learning algorithms are used as base classifiers, a convolutional block attention mechanism (CBAM) is added after each base classifier, and three different fusion strategies are used to merge the prediction results of the base classifiers to output the final prediction results (choroidal neovascularization (CNV), diabetic macular oedema (DME), drusen, normal). The results show that in a classification problem involving the UCSD common retinal OCT dataset (108,312 OCT images from 4,686 patients), compared with that of the InceptionV3 network model, the prediction accuracy of FN-OCT is improved by 5.3% (accuracy = 98.7%, area under the curve (AUC) = 99.1%). The predictive accuracy and AUC achieved on an external dataset for the classification of retinal OCT diseases are 92 and 94.5%, respectively, and gradient-weighted class activation mapping (Grad-CAM) is used as a visualization tool to verify the effectiveness of the proposed FNs. This finding indicates that the developed fusion algorithm can significantly improve the performance of classifiers while providing a powerful tool and theoretical support for assisting with the diagnosis of retinal OCT.

Gene expression in the microbial consortia of colonial Microcystis aeruginosa-a potential buoyant particulate biofilm.

Microcystis spp., notorious bloom-forming cyanobacteria, are often present in colony form in eutrophic lakes worldwide. Uncovering the mechanisms underlying Microcystis colony formation and maintenance is vital to controlling the blooms, but it has long been a challenge. Here, bacterial communities and gene expression patterns of colonial and unicellular forms of one non-axenic strain of Microcystis aeruginosa isolated from Lake Taihu were compared. Evidently, different microbial communities between them were observed through 16S rDNA MiSeq sequencing. Metatranscriptome analyses revealed that transcripts for pathways involved in bacterial biofilm formation, such as biosynthesis of peptidoglycan and arginine by Bacteroidetes, methionine biosynthesis, alginate metabolism, flagellum, and motility, as well as widespread colonization islands by Proteobacteria, were highly enriched in the colonial form. Furthermore, transcripts for nitrogen fixation and denitrification pathways by Proteobacteria that usually occur in biofilms were significantly enriched in the colonial Microcystis. Results revealed that microbes associated with Microcystis colonies play important roles through regulation of biofilm-related genes in colony formation and maintenance. Moreover, Microcystis colony represents a potential 'buoyant particulate biofilm', which is a good model for biofilm studies. The biofilm features of colonial Microcystis throw a new light on management and control of the ubiquitous blooms in eutrophic waters.