Progressive acceleration in the genetic algorithm by intragenerational mutation.

The intragenerational mutation of the genetic algorithm (IMGA) is proposed to actively broaden the searching space during the optimization process. The searching space is aggressively increased by expanding the variation of mutation rates of all individuals within each generation, leading to the reduction of the required number of iterations, improving the convergence speed and the enhancement factor.

[A multi-center epidemiological study on pneumococcal meningitis in children from 2019 to 2020]. / 2019­2020å¹´160ä¾‹å„¿ç«¥è‚ºç‚Žé“¾çƒèŒè„‘è†œç‚Žä¸´åºŠæµè¡Œç— å­¦å¤šä¸­å¿ƒç ”ç©¶.

OBJECTIVES: To investigate the clinical characteristics and prognosis of pneumococcal meningitis (PM), and drug sensitivity of Streptococcus pneumoniae (SP) isolates in Chinese children. METHODS: A retrospective analysis was conducted on clinical information, laboratory data, and microbiological data of 160 hospitalized children under 15 years old with PM from January 2019 to December 2020 in 33 tertiary hospitals across the country. RESULTS: Among the 160 children with PM, there were 103 males and 57 females. The age ranged from 15 days to 15 years, with 109 cases (68.1%) aged 3 months to under 3 years. SP strains were isolated from 95 cases (59.4%) in cerebrospinal fluid cultures and from 57 cases (35.6%) in blood cultures. The positive rates of SP detection by cerebrospinal fluid metagenomic next-generation sequencing and cerebrospinal fluid SP antigen testing were 40% (35/87) and 27% (21/78), respectively. Fifty-five cases (34.4%) had one or more risk factors for purulent meningitis, 113 cases (70.6%) had one or more extra-cranial infectious foci, and 18 cases (11.3%) had underlying diseases. The most common clinical symptoms were fever (147 cases, 91.9%), followed by lethargy (98 cases, 61.3%) and vomiting (61 cases, 38.1%). Sixty-nine cases (43.1%) experienced intracranial complications during hospitalization, with subdural effusion and/or empyema being the most common complication [43 cases (26.9%)], followed by hydrocephalus in 24 cases (15.0%), brain abscess in 23 cases (14.4%), and cerebral hemorrhage in 8 cases (5.0%). Subdural effusion and/or empyema and hydrocephalus mainly occurred in children under 1 year old, with rates of 91% (39/43) and 83% (20/24), respectively. SP strains exhibited complete sensitivity to vancomycin (100%, 75/75), linezolid (100%, 56/56), and meropenem (100%, 6/6). High sensitivity rates were also observed for levofloxacin (81%, 22/27), moxifloxacin (82%, 14/17), rifampicin (96%, 25/26), and chloramphenicol (91%, 21/23). However, low sensitivity rates were found for penicillin (16%, 11/68) and clindamycin (6%, 1/17), and SP strains were completely resistant to erythromycin (100%, 31/31). The rates of discharge with cure and improvement were 22.5% (36/160) and 66.2% (106/160), respectively, while 18 cases (11.3%) had adverse outcomes. CONCLUSIONS: Pediatric PM is more common in children aged 3 months to under 3 years. Intracranial complications are more frequently observed in children under 1 year old. Fever is the most common clinical manifestation of PM, and subdural effusion/emphysema and hydrocephalus are the most frequent complications. Non-culture detection methods for cerebrospinal fluid can improve pathogen detection rates. Adverse outcomes can be noted in more than 10% of PM cases. SP strains are high sensitivity to vancomycin, linezolid, meropenem, levofloxacin, moxifloxacin, rifampicin, and chloramphenicol.

Single-Cell RNA Sequencing Reveals Transcriptional Signatures and Cell-Cell Communication in Diabetic Retinopathy.

BACKGROUND: Diabetic retinopathy (DR) is a major cause of vision loss in workingage individuals worldwide. Cell-to-cell communication between retinal cells and retinal pigment epithelial cells (RPEs) in DR is still unclear, so this study aimed to generate a single-cell atlas and identify receptor‒ligand communication between retinal cells and RPEs. METHODS: A mouse single-cell RNA sequencing (scRNA-seq) dataset was retrieved from the GEO database (GSE178121) and was further analyzed with the R package Seurat. Cell cluster annotation was performed to further analyze cell‒cell communication. The differentially expressed genes (DEGs) in RPEs were explored through pathway enrichment analysis and the protein‒ protein interaction (PPI) network. Core genes in the PPI were verified by quantitative PCR in ARPE-19 cells. RESULTS: We observed an increased proportion of RPEs in STZ mice. Although some overall intercellular communication pathways did not differ significantly in the STZ and control groups, RPEs relayed significantly more signals in the STZ group. In addition, THBS1, ITGB1, COL9A3, ITGB8, VTN, TIMP2, and FBN1 were found to be the core DEGs of the PPI network in RPEs. qPCR results showed that the expression of ITGB1, COL9A3, ITGB8, VTN, TIMP2, and FBN1 was higher and consistent with scRNA-seq results in ARPE-19 cells under hyperglycemic conditions. CONCLUSION: Our study, for the first time, investigated how signals that RPEs relay to and from other cells underly the progression of DR based on scRNA-seq. These signaling pathways and hub genes may provide new insights into DR mechanisms and therapeutic targets.

Single-cell integrative analysis reveals consensus cancer cell states and clinical relevance in breast cancer.

High heterogeneity and complex interactions of malignant cells in breast cancer has been recognized as a driver of cancer progression and therapeutic failure. However, complete understanding of common cancer cell states and their underlying driver factors remain scarce and challenging. Here, we revealed seven consensus cancer cell states recurring cross patients by integrative analysis of single-cell RNA sequencing data of breast cancer. The distinct biological functions, the subtype-specific distribution, the potential cells of origin and the interrelation of consensus cancer cell states were systematically elucidated and validated in multiple independent datasets. We further uncovered the internal regulons and external cell components in tumor microenvironments, which contribute to the consensus cancer cell states. Using the state-specific signature, we also inferred the abundance of cells with each consensus cancer cell state by deconvolution of large breast cancer RNA-seq cohorts, revealing the association of immune-related state with better survival. Our study provides new insights for the cancer cell state composition and potential therapeutic strategies of breast cancer.