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ObjectiveTubo-ovarian carcinosarcomas are rare, extremely aggressive malignant tumors that contain both carcinomatous and sarcomatous components. Due to the disease’s rarity, developing an effective treatment strategy for ovarian carcinosarcomas has been challenging. A study was conducted to investigate the clinicopathologic and molecular features of this rare disease.MethodsWe enrolled all patients diagnosed with tubo-ovarian carcinosarcomas from January 2007 to December 2022. The clinical and pathological data were gathered from medical records. Kaplan–Meier curves were plotted to calculate OS and PFS. The Log-rank test and Cox regression model were utilized to explore the relationship between clinicopathological parameters and survival. Patients with cancer tissues available had sequencing with a 242-gene panel done to investigate the mutational landscape and signature of the disease.ResultsIn total, 65% of the patients were diagnosed with advanced-stage cancer. The median PFS and OS of this cohort were 27 and 40 months, respectively, and there was no significant difference in survival between the homologous and heterologous components of sarcoma. Unexpectedly, staging did not have effects on prognosis. All patients had surgical attempts, and suboptimal debulking status was correlated with poorer PFS and OS. MSI was identified in 0% with low Tumor mutation burden (TMB) indicating a poor response to immunotherapy. Low HER2 expression is controversial, according to previous reports, and gives us limited choices with this rare and aggressive disease. We surprisingly found the homologous recombination deficiency (HRD)-positive status was identified in 64% of OCS, which is significantly higher than UCS and other types of epithelial ovarian cancer. The fact that all patients in our cohort who received olaparib as maintenance therapy had survived over 30 months and two had no evidence of recurrence at the latest follow-up might further validate the role of poly (ADP-ribose) polymerase inhibitors (PARPi) in the management of OCS.ConclusionOCS patients seemed to respond to carboplatin/paclitaxel with optimal PFS and OS. Cytoreduction with no residuals proved to be the sole independent prognostic factor. WES should be done to assess the prognosis and assist with the targeted therapy, especially the HRD test, which might help select potential patients who benefit from PARPi.

Abstract Long-read sequencing data, particularly those derived from the Oxford Nanopore sequencing platform, tend to exhibit high error rates. Here, we present NextDenovo, an efficient error correction and assembly tool for noisy long reads, which achieves a high level of accuracy in genome assembly. We apply NextDenovo to assemble 35 diverse human genomes from around the world using Nanopore long-read data. These genomes allow us to identify the landscape of segmental duplication and gene copy number variation in modern human populations. The use of NextDenovo should pave the way for population-scale long-read assembly using Nanopore long-read data.

Abstract Postoperative diplopia is the most common complication following orbital fracture repair (OFR). Existing evidence on its risk factors is based on single-institution studies and small sample sizes. Our study is the first multi-center study to develop and validate a risk calculator for the prediction of postoperative diplopia following OFR. We reviewed trauma patients who underwent OFR at two high-volume trauma centers (2015–2019). Excluded were patients

ObjectiveVaricella, a highly contagious viral disease caused by the varicella-zoster virus (VZV), affects millions globally, with a higher prevalence among children. After the initial infection, VZV lies dormant in sensory ganglia and has the potential to reactivate much later, causing herpes zoster (HZ). Vaccination is one of the most effective methods to prevent varicella, and the two-dose varicella vaccine (VarV) regimen is widely used around the world. In China, the VarV has been included in the national immunization programme with a recommended single-dose regimen. This study aimed to compare the effectiveness of the two-dose vs. one-dose VarV regimen in children in Shanghai, China.Materials and methodsA prospective cohort study was conducted in Shanghai, China, from September 2018 to December 2022. The study enrolled children aged 3–18 years who had received either the one-dose, two-dose, or 0-dose VarV regimen. Vaccination history, varicella infection status, and relevant variables, including demographic information (name, date of birth and sex) and medical history (clinical features of varicella and illness duration) were collected through medical record review and parental interviews.ResultsA total of 3,838 children were included in the study, with 407 in the 0-dose regimen group, 2,107 in the one-dose regimen group and 1,324 in the two-dose regimen group. The corresponding incidence density in these groups was 0.13, 0.05 and 0.03 cases per 1,000 person-days, respectively. The adjusted vaccine effectiveness (VE) was 81.7% (95%CI: 59.3–91.8%) for the two-dose regimen and 60.3% (95%CI: 29.3–77.7%) for the one-dose regimen, compared to the 0-dose regimen. The two-dose VarV regimen showed a protective effectiveness of 47.6% (95%CI: 2.5–71.9%) compared to the one-dose VarV regimen.ConclusionThis study provides evidence supporting the greater effectiveness of the two-dose VarV regimen in preventing varicella infection compared to the one-dose regimen.

Abstract Liver cancer is a major malignant tumor, which seriously threatens human health and increases the economic burden on patients. At present, gene therapy has been comprehensively studied as an excellent therapeutic measure in liver cancer treatment. Oncolytic virus (OV) is a kind of virus that can specifically infect and kill tumor cells. After being modified by genetic engineering, the specificity of OV infection to tumor cells is increased, and its influence on normal cells is reduced. To date, OV has shown its effectiveness and safety in experimental and clinical studies on a variety of tumors. Thus, this review primarily introduces the current status of different genetically engineered OVs used in gene therapy for liver cancer, focuses on the application of OVs and different target genes for current liver cancer therapy, and identifies the problems encountered in OVs-based combination therapy and the corresponding solutions, which will provide new insights into the treatment of liver cancer.

Abstract Structural variants (SVs), accounting for a larger fraction of the genome than SNPs/InDels, are an important pool of genetic variation, enabling environmental adaptations. Here, we perform long-read sequencing data of 320 Tibetan and Han samples and show that SVs are highly involved in high-altitude adaptation. We expand the landscape of global SVs, apply robust models of selection and population differentiation combining SVs, SNPs and InDels, and use epigenomic analyses to predict enhancers, target genes and biological functions. We reveal diverse Tibetan-specific SVs affecting the regulatory circuitry of biological functions, including the hypoxia response, energy metabolism and pulmonary function. We find a Tibetan-specific deletion disrupts a super-enhancer and downregulates EPAS1 using enhancer reporter, cellular knock-out and DNA pull-down assays. Our study expands the global SV landscape, reveals the role of gene-regulatory circuitry rewiring in human adaptation, and illustrates the diverse functional roles of SVs in human biology.

Abstract Background Recent state-of-the-art sequencing technologies enable the investigation of challenging regions in the human genome and expand the scope of variant benchmarking datasets. Herein, we sequence a Chinese Quartet, comprising two monozygotic twin daughters and their biological parents, using four short and long sequencing platforms (Illumina, BGI, PacBio, and Oxford Nanopore Technology). Results The long reads from the monozygotic twin daughters are phased into paternal and maternal haplotypes using the parent–child genetic map and for each haplotype. We also use long reads to generate haplotype-resolved whole-genome assemblies with completeness and continuity exceeding that of GRCh38. Using this Quartet, we comprehensively catalogue the human variant landscape, generating a dataset of 3,962,453 SNVs, 886,648 indels (

Abstract Background Genomic DNA reference materials are widely recognized as essential for ensuring data quality in omics research. However, relying solely on reference datasets to evaluate the accuracy of variant calling results is incomplete, as they are limited to benchmark regions. Therefore, it is important to develop DNA reference materials that enable the assessment of variant detection performance across the entire genome. Results We established a DNA reference material suite from four immortalized cell lines derived from a family of parents and monozygotic twins. Comprehensive reference datasets of 4.2 million small variants and 15,000 structural variants were integrated and certified for evaluating the reliability of germline variant calls inside the benchmark regions. Importantly, the genetic built-in-truth of the Quartet family design enables estimation of the precision of variant calls outside the benchmark regions. Using the Quartet reference materials along with study samples, batch effects are objectively monitored and alleviated by training a machine learning model with the Quartet reference datasets to remove potential artifact calls. Moreover, the matched RNA and protein reference materials and datasets from the Quartet project enables cross-omics validation of variant calls from multiomics data. Conclusions The Quartet DNA reference materials and reference datasets provide a unique resource for objectively assessing the quality of germline variant calls throughout the whole-genome regions and improving the reliability of large-scale genomic profiling.

Background:. Gender-affirming mastectomy (GAM) is a gender-affirmation surgery designed to remove or reduce breast tissue, with or without nipple reconstruction. GAM is the most commonly performed gender-affirmation surgery and risk factors associated with unplanned return to the operating room and reoperation continue to be investigated. This is the largest study of transgender and nonbinary patients undergoing GAM to determine predictors of unplanned reoperation. Methods:. The National Surgical Quality Improvement Program database was queried for patients undergoing GAM from 2012 to 2020. The primary outcome was the incidence of unplanned reoperation within 30 days postoperatively. The secondary outcome was the indication for unplanned reoperation within this period. Descriptive statistics were calculated. Bivariate analysis and multivariate logistic regression were performed to determine significant predictors of reoperation after GAM. Results:. A total of 2316 patients underwent GAM, of whom 2.2% (n = 51) underwent unplanned reoperation of the chest. The most common indication for unplanned reoperation was hematoma (n = 41, 71.9%) followed by abscess (n = 5, 8.8%). Significant predictors of reoperation were corticosteroid use [adjusted odds ratio (aOR) 95% confidence interval (CI) 5.07 (1.07–23.89)] and diabetes [aOR (CI) 10.98 (3.0–40.33)]. Hispanic/Latinx ethnicity [aOR (CI) 3.19 (1.22–8.33)] and corticosteroid use [aOR (CI) 6.81 (1.45–31.98)] were significant predictors of unplanned reoperation for hematoma evacuation after GAM. Conclusions:. Diabetes mellitus and corticosteroid use were associated with unplanned reoperation after GAM. Ethnic correlations remain to be better elucidated as well as the effect of intersectionality. These findings can be used to guide patient selection and surgical decision-making.

SVhawkeye is a novel visualization software created to rapidly extract essential structural information from third-generation sequencing data, such as data generated by PacBio or Oxford Nanopore Technologies. Its primary focus is on visualizing various structural variations commonly encountered in whole-genome sequencing (WGS) experiments, including deletions, insertions, duplications, inversions, and translocations. Additionally, SVhawkeye has the capability to display isoform structures obtained from iso-seq data and provides interval depth visualization for deducing local copy number variation (CNV). One noteworthy feature of SVhawkeye is its capacity to genotype structural variations, a critical function that enhances the accuracy of structural variant genotyping. SVhawkeye is an open-source software developed using Python and R languages, and it is freely accessible on GitHub (https://github.com/yywan0913/SVhawkeye).

BackgroundThis meta-analysis was performed to assess the prevalence and circulating strains of rotavirus (RV) among Chinese children under 5 years of age after the implantation of the RV vaccine.Material and methodsStudies published between 2019 and 2023, focused on RV-based diarrhea among children less than 5 years were systematically reviewed using PubMed, Embase, Web of Science, CNKI, Wanfang and SinoMed Data. We synthesized their findings to examine prevalence and genetic diversity of RV after the RV vaccine implementation using a fixed-effects or random-effects model.ResultsSeventeen studies met the inclusion criteria for this meta-analysis. The overall prevalence of RV was found to be 19.00%. The highest infection rate was noted in children aged 12-23months (25.79%), followed by those aged 24-35 months (23.91%), and 6-11 months (22.08%). The serotype G9 emerged as the most predominant RV genotype, accounting for 85.48% of infections, followed by G2 (7.70%), G8 (5.74%), G1 (4.86%), and G3 (3.21%). The most common P type was P[8], representing 64.02% of RV cases. Among G-P combinations, G9P[8] was the most frequent, responsible for 78.46% of RV infections, succeeded by G8P[8] (31.22%) and G3P[8] (8.11%).ConclusionDespite the variation of serotypes observed in China, the G1, G2, G3, G8 and G9 serotypes accounted for most RV strains. The genetic diversity analysis highlights the dynamic nature of RV genotypes, necessitating ongoing surveillance to monitor changes in strain distribution and inform future vaccine strategies.

Objectives: Fewer than one-fifth of all studies on gender-affirming care originate from low- and middle-income countries (LMICs). This is the first systematic review to examine surgical demographics and outcomes following gender-affirming surgery (GAS) in LMICs. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, five databases were systematically searched for original studies and case series on GAS within LMIC settings. Excluded reports included animal studies, non-English language studies, secondary studies including reviews, individual case reports and conference abstracts. Results: This review includes 34 studies involving n = 5064 TGNB individuals. Most studies (22, 64.7%) were from upper-middle-income countries, followed by lower-middle-income countries (12, 35.3%). A total of 31 studies (91.2%) reported on post-operative outcomes. Of n = 5013 patients who underwent GAS, 71.5% (n = 3584) underwent masculinizing and 29.5% (n = 1480) underwent feminizing procedures. The predominant procedures were metoidioplasty (n = 2270/3584, 63.3%) and vaginoplasty (n = 1103/1480, 74.5%). Mean follow-up was 47.7 months. In patients who underwent metoidioplasty, 6.8% (n = 155) of patients experienced a complication and 6.3% (n = 144) underwent revision surgery. In patients who underwent vaginoplasty, 11.5% (n = 127) of patients experienced a complication and 8.5% (n = 94) underwent revision surgery. Of the studies (25/34, 73.5%) that reported on quality of life and post-operative satisfaction, the majority showed marked improvements in psychosocial and functional outcomes. Notably, no post-surgical regret was reported among the surveyed patients. Conclusions: Existing literature on GAS in LMICs remains scarce and is concentrated in select institutions that drive specific procedures. Our review highlights the low reported volumes of GAS, variability in surgical outcomes and quality of life.

Abstract Background Histone acetyltransferases (HATs) of the MYST family are associated with a variety of human cancers. However, the relationship between MYST HATs and their clinical significance in kidney renal clear cell carcinoma (KIRC) has not yet been evaluated. Methods The bioinformatics method was used to investigate the expression patterns and prognostic value of MYST HATs. Western blot was used to detect the expression of MYST HATs in KIRC. Results The expression levels of MYST HATs except KAT8 (KAT5, KAT6A, KAT6B, and KAT7) were significantly reduced in KIRC tissues compared to normal renal tissues, and the western blot results of the KIRC samples also confirmed the result. Reduced expression levels of MYST HATs except KAT8 were significantly associated with high tumor grade and advanced TNM stage in KIRC, and showed a significant association with an unfavorable prognosis in patients with KIRC. We also found that the expression levels of MYST HATs were closely related to each other. Subsequently, gene set enrichment analysis showed that the function of KAT5 was different from that of KAT6A, KAT6B and KAT7. The expression levels of KAT6A, KAT6B and KAT7 had significant positive correlations with cancer immune infiltrates such as B cells, CD4+ T cells and CD8+ T cells. Conclusions Our results indicated that MYST HATs, except KAT8, play a beneficial role in KIRC.

Background Gender-affirming phalloplasty has a complication rate as high as 76.5%. This is the first study to determine the predictors of 30-day complications following phalloplasty using a national registry.

Pachytene Piwi-interacting RNAs (piRNAs) expressed in mammalian germ lines are abundant, but their evolution and function are not fully understood. Here, the authors find that pachytene piRNA loci are hotspots of structural variation, which underlies rapid piRNA birth, divergence, and loss.

Abstract Indoleamine 2,3-dioxygenase 1 (IDO1), the enzyme that catabolizes tryptophan (Trp) metabolism to promote regulatory T cells (Tregs) and suppress CD8+ T cells, is regulated by several intrinsic signaling pathways. Here, we found that tobacco smoke, a major public health concern that kills 8 million people each year worldwide, induced IDO1 in normal and malignant lung epithelial cells in vitro and in vivo. The carcinogen nicotine-derived nitrosaminoketone (NNK) was the tobacco compound that upregulated IDO1 via activation of the transcription factor c-Jun, which has a binding site for the IDO1 promoter. The NNK receptor α7 nicotinic acetylcholine receptor (α7nAChR) was required for NNK-induced c-Jun activation and IDO1 upregulation. In A/J mice, NNK reduced CD8+ T cells and increased Tregs. Clinically, smoker patients with non-small-cell lung cancer (NSCLC) exhibited high IDO1 levels and low Trp/kynurenine (Kyn) ratios. In NSCLC patients, smokers with lower IDO1 responded better to anti-PD1 antibody treatment than those with higher IDO1. These data indicate that tobacco smoke induces IDO1 to catabolize Trp metabolism and immune suppression to promote carcinogenesis, and lower IDO1 might be a potential biomarker for anti-PD1 antibodies in smoker patients, whereas IDO1-high smoker patients might benefit from IDO1 inhibitors in combination with anti-PD1 antibodies.

The study examines the dynamics and multiplicity of political influencers on Twitter by focusing on the discussion of six China-related political events between 2017 and 2020. We consider influence as communication practices aimed to shape the flow of online information, and political influencers as a small number of users who can disproportionately advance certain topics and direct audience attention. Our analysis of English and Chinese data ( N = 2,349,852) shows that political influencers engage in political events through two roles: initiators and amplifiers. While existing institutions such as news outlets are initiators who originate political information, individuals, trolls, and bots flood social media to share information provided by other users. Moreover, the results suggest that political influencers are largely event-dependent in the way that few accounts remain influential across different events, times, and language contexts. These findings advance our understanding of political influencers from a dynamic perspective.

Here, Wang and colleagues combine short and long sequencing reads to characterize structural variations, prophage and CRISPR spacer elements in human gut microbiomes, and reveal functional differences at a finer level of bacterial strains.

Structural variations (SVs) are a key type of cancer genomic alterations, contributing to oncogenesis and progression of many cancers, including colorectal cancer (CRC). However, SVs in CRC remain difficult to be reliably detected due to limited SV-detection capacity of the commonly used short-read sequencing. This study investigated the somatic SVs in 21 pairs of CRC samples by Nanopore whole-genome long-read sequencing. 5200 novel somatic SVs from 21 CRC patients (494 SVs / patient) were identified. A 4.9-Mbp long inversion that silences APC expression (confirmed by RNA-seq) and an 11.2-kbp inversion that structurally alters CFTR were identified. Two novel gene fusions that might functionally impact the oncogene RNF38 and the tumor-suppressor SMAD3 were detected. RNF38 fusion possesses metastasis-promoting ability confirmed by in vitro migration and invasion assay, and in vivo metastasis experiments. This work highlighted the various applications of long-read sequencing in cancer genome analysis, and shed new light on how somatic SVs structurally alter critical genes in CRC. The investigation on somatic SVs via nanopore sequencing revealed the potential of this genomic approach in facilitating precise diagnosis and personalized treatment of CRC.

The importance of structural variants (SVs) for human phenotypes and diseases is now recognized. Although a variety of SV detection platforms and strategies that vary in sensitivity and specificity have been developed, few benchmarking procedures are available to confidently assess their performances in biological and clinical research. To facilitate the validation and application of these SV detection approaches, we established an Asian reference material by characterizing the genome of an Epstein-Barr virus (EBV)-immortalized B lymphocyte line along with identified benchmark regions and high-confidence SV calls. We established a high-confidence SV callset with 8938 SVs by integrating four alignment-based SV callers, including 109× Pacific Biosciences (PacBio) continuous long reads (CLRs), 22× PacBio circular consensus sequencing (CCS) reads, 104× Oxford Nanopore Technologies (ONT) long reads, and 114× Bionano optical mapping platform, and one de novo assembly-based SV caller using CCS reads. A total of 544 randomly selected SVs were validated by PCR amplification and Sanger sequencing, demonstrating the robustness of our SV calls. Combining trio-binning-based haplotype assemblies, we established an SV benchmark for identifying false negatives and false positives by constructing the continuous high-confidence regions (CHCRs), which covered 1.46 gigabase pairs (Gb) and 6882 SVs supported by at least one diploid haplotype assembly. Establishing high-confidence SV calls for a benchmark sample that has been characterized by multiple technologies provides a valuable resource for investigating SVs in human biology, disease, and clinical research.

Butyrophilin 3A1 (BTN3A1), a major histocompatibility complex‐associated gene that encodes a membrane protein with two extracellular immunoglobulin domains and an intracellular B30.2 domain, is critical in T‐cell activation and adaptive immune response. Here, the expression of BTN3A1 in cancers was analyzed in eight databases comprising 86 733 patients of 33 cancers, and the findings were validated in patient samples and cell models. We showed that BTN3A1 was expressed in most cancers, and its expression level was strongly correlated with clinical outcome of 13 cancers. Mutations of BTN3A1 were detected, and the mutations were distributed throughout the entire gene. Gene set enrichment analysis showed that BTN3A1 co‐expression genes and interacting proteins were enriched in immune regulation‐related pathways. BTN3A1 was associated with tumor‐infiltrating immune cells and was co‐expressed with multiple immune checkpoints in patients with breast cancer (BRCA) and non‐small cell lung cancer (NSCLC). We reported that BTN3A1 was downregulated in 46 of 65 (70.8%) NSCLCs, and its expression level was inversely associated with clinical outcome of the patients. BTN3A1 in tumor samples was lower than in counterpart normal tissues in 31 of 38 (81.6%) BRCAs. Bioinformatics analyses showed that BTN3A1 could be a target gene of transcription factor Spi‐1 proto‐oncogene (SPI1), and our ‘wet’ experiments showed that ectopic expression of SPI1 upregulated, whereas silencing of SPI1 downregulated, BTN3A1 expression in cells. These results suggest that BTN3A1 may function as a tumor suppressor and may serve as a potential prognostic biomarker in NSCLCs and BRCAs.

Cyber-physical systems (CPS) refer to systems that integrate communication, control, and computational elements into physical processes to facilitate the control of physical systems and effective monitoring. The systems are designed to interact with the physical world, monitor and control the physical processes while in operation, and generate data. Deep Neural Networks (DNN) comprise multiple layers of interconnected neurons that process input data to produce predictions. Spatial-temporal data represents the physical world and its evolution over time and space. The generated spatial-temporal data is used to make decisions and control the behavior of CPS. This paper systematically reviews the applications of DNNs, namely convolutional, recurrent, and graphs, in handling spatial-temporal data in CPS. An extensive literature survey is conducted to determine the areas in which DNNs have successfully captured spatial-temporal data in CPS and the emerging areas that require attention. The research proposes a three-dimensional framework that considers: CPS (transportation, manufacturing, and others), Target (spatial-temporal data processing, anomaly detection, predictive maintenance, resource allocation, real-time decisions, and multi-modal data fusion), and DNN schemes (CNNs, RNNs, and GNNs). Finally, research areas that need further investigation are identified, such as performance and security. Addressing data quality, strict performance assurance, reliability, safety, and security resilience challenges are the areas that are required for further research.

Abstract Background MicroRNAs (miRNAs) have been shown to be associated with osteoarthritis (OA) progression. This study aimed to explore the role of miR-520c-3p in OA progression. Methods Expression levels of miR-520c-3p and Growth arrest-specific 2 (GAS2) were detected using quantitative real-time PCR. The proliferation and apoptosis of cells were measured using cell counting kit 8 (CCK8) assay and flow cytometry. Furthermore, the protein levels of apoptosis-related markers, extracellular degradation markers, inflammatory response markers, and GAS2 were tested using quantitative real-time polymerase chain reaction (RT-PCR) and western blot (WB) analysis. In addition, the interaction between miR-520c-3p and GAS2 was examined using dual luciferase reporter assay. Results GAS2 was highly expressed, and miR-520c-3p was lowly expressed in OA cartilage tissues. miR-520c-3p could promote the proliferation and inhibit the apoptosis and inflammation of OA chondrocytes. miR-520c-3p could be sponged by GAS2, and its inhibitor could reverse the regulation of GAS2 on the biological functions of OA chondrocytes. GAS2 was a target of miR-520c-3p, which was identified by bioinformatic analysis and dual-luciferase reporter assay. Overexpression of GAS2 could inhibit the proliferation and promoted the apoptosis and inflammation of OA chondrocytes. Conclusion Our data showed that miR-520c-3p might regulate the GAS2 to inhibit the progression of OA.

BackgroundEffective biomarkers for early diagnosis of lung cancer are needed. Previous studies have indicated positive associations between abnormal circulating cytokines and the etiology of lung cancer.MethodsBlood samples were obtained from 286 patients with pretreatment lung cancer and 80 healthy volunteers. Circulating cytokine levels were detected with a Luminex assay and enzyme-linked immunosorbent assay (ELISA). Urine samples were obtained from 284 patients and 122 healthy volunteers. CXC chemokine ligand 14 (CXCL14) expression in tumors and nontumor regions of lung tissues from 133 lung cancer cases was detected by immunohistochemical (IHC) staining and immunofluorescence (IF) staining of formalin fixed paraffin-embedded (FFPE) tissues.ResultsCompared with healthy volunteers, a 65.7-fold increase was observed in the level of CXCL14 in the plasma of lung cancer patients, and a 1.7-fold increase was observed in the level of CXCL14 in the urine of lung cancer patients, achieving a 0.9464 AUC (area under the curve) value and a 0.6476 AUC value for differentiating between lung cancer patients and healthy volunteers, respectively. Stromal CXCL14 expression was significantly associated with advanced pathologic stage (P

Longan (Dimocarpus longan Lour.) is a tropical/subtropical fruit tree of significant economic importance. Floral induction is an essential process for longan flowering and plays decisive effects on the longan yield. Due to the instability of flowering, it is necessary to understand the molecular mechanisms of floral induction in longan. In this study, mRNA and long noncoding RNA (lncRNA) transcriptome sequencing were performed using the apical buds of fruiting branches as materials. A total of 7,221 differential expressions of mRNAs (DEmRNAs) and 3,238 differential expressions of lncRNAs (DElncRNAs) were identified, respectively. KEGG enrichment analysis of DEmRNAs highlighted the importance of starch and sucrose metabolic, circadian rhythms, and plant hormone signal transduction pathways during floral induction. Combining the analysis of weighted gene co-expression network (WGCNA) and expression pattern of DEmRNAs in the three pathways, specific transcriptional characteristics at each stage during floral induction and regulatory network involving co-expressed genes were investigated. The results showed that sucrose metabolism and auxin signal transduction may be crucial for the growth and maturity of autumn shoots in September and October (B1-B2 stage); starch and sucrose metabolic, circadian rhythms, and plant hormone signal transduction pathways participated in the regulation of floral bud physiological differentiation together in November and December (B3-B4 stage) and the crosstalk among three pathways was also found. Hub genes in the co-expression network and key DEmRNAs in three pathways were identified. The circadian rhythm genes FKF1 and GI were found to activate SOC1gene through the photoperiod core factor COL genes, and they were co-expressed with auxin, gibberellin, abscisic acid, ethylene signaling genes, and sucrose biosynthesis genes at B4 stage. A total of 12 hub-DElncRNAs had potential for positively affecting their distant target genes in three putative key pathways, predominantly in a co-transcriptional manner. A hypothetical model of regulatory pathways and key genes and lncRNAs during floral bud induction in longan was proposed finally. Our studies will provide valuable clues and information to help elucidate the potential molecular mechanisms of floral initiation in longan and woody fruit trees.

Background: Short tandem repeats (STRs) are highly variable elements that play a pivotal role in multiple genetic diseases and the regulation of gene expression. Long-read sequencing (LRS) offers a potential solution to genome-wide STR analysis. However, characterizing STRs in human genomes using LRS on a large population scale has not been reported.Methods: We conducted the large LRS-based STR analysis in 193 unrelated samples of the Chinese population and performed genome-wide profiling of STR variation in the human genome. The repeat dynamic index (RDI) was introduced to evaluate the variability of STR. We sourced the expression data from the Genotype-Tissue Expression to explore the tissue specificity of highly variable STRs related genes across tissues. Enrichment analyses were also conducted to identify potential functional roles of the high variable STRs.Results: This study reports the large-scale analysis of human STR variation by LRS and offers a reference STR database based on the LRS dataset. We found that the disease-associated STRs (dSTRs) and STRs associated with the expression of nearby genes (eSTRs) were highly variable in the general population. Moreover, tissue-specific expression analysis showed that those highly variable STRs related genes presented the highest expression level in brain tissues, and enrichment pathways analysis found those STRs are involved in synaptic function-related pathways.Conclusion: Our study profiled the genome-wide landscape of STR using LRS and highlighted the highly variable STRs in the human genome, which provide a valuable resource for studying the role of STRs in human disease and complex traits.

As technology continues to evolve, our society is becoming enriched with more intelligent devices that help us perform our daily activities more efficiently and effectively. One of the most significant technological advancements of our time is the Internet of Things (IoT), which interconnects various smart devices (such as smart mobiles, intelligent refrigerators, smartwatches, smart fire alarms, smart door locks, and many more) allowing them to communicate with each other and exchange data seamlessly. We now use IoT technology to carry out our daily activities, for example, transportation. In particular, the field of smart transportation has intrigued researchers due to its potential to revolutionize the way we move people and goods. IoT provides drivers in a smart city with many benefits, including traffic management, improved logistics, efficient parking systems, and enhanced safety measures. Smart transportation is the integration of all these benefits into applications for transportation systems. However, as a way of further improving the benefits provided by smart transportation, other technologies have been explored, such as machine learning, big data, and distributed ledgers. Some examples of their application are the optimization of routes, parking, street lighting, accident prevention, detection of abnormal traffic conditions, and maintenance of roads. In this paper, we aim to provide a detailed understanding of the developments in the applications mentioned earlier and examine current researches that base their applications on these sectors. We aim to conduct a self-contained review of the different technologies used in smart transportation today and their respective challenges. Our methodology encompassed identifying and screening articles on smart transportation technologies and its applications. To identify articles addressing our topic of review, we searched for articles in the four significant databases: IEEE Xplore, ACM Digital Library, Science Direct, and Springer. Consequently, we examined the communication mechanisms, architectures, and frameworks that enable these smart transportation applications and systems. We also explored the communication protocols enabling smart transportation, including Wi-Fi, Bluetooth, and cellular networks, and how they contribute to seamless data exchange. We delved into the different architectures and frameworks used in smart transportation, including cloud computing, edge computing, and fog computing. Lastly, we outlined current challenges in the smart transportation field and suggested potential future research directions. We will examine data privacy and security issues, network scalability, and interoperability between different IoT devices.

With the wide use of polyethylene film, the influence of polyethylene microplastic particles produced by its weathering on the rhizosphere soil microenvironment has attracted more and more attention from scientific research circles. In this study, the effects of low (0.2% w/w), medium (1% w/w), and high (2% w/w) doses of polyethylene particles and the combined reed biomass (2% w/w) on soil environmental factors and bacterial communities and metabolites in the reed rhizosphere were evaluated by a 90-day pot microscopic simulation system. The shape and surface microstructure of polyethylene particles in each treatment group changed obviously. A high (2% w/w) dose of microplastics significantly increased the TKN, TOC, and TP in reed root soil. The addition of the biomass significantly improved the activities of urease and sucrase in the soil. The α diversity of bacteria was not significantly affected by the addition of LDPE microplastics and biomass, but the β diversity of the bacterial community and the relative abundance of the Candidatus_Roku Bacteria, Chloroflexi, Unclassified_Blastocatella_Genus were significantly changed by the addition of middle (1% w/w) and high (2% w/w) doses of microplastics. In addition, the spectrum analysis of the soil metabolites showed that the abundance of soil metabolites was changed in each treatment group, and the differential metabolites were significantly up-regulated or down-regulated. Our findings provide a scientific reference to elucidate the impact of LDPE microplastic particles on the inter-rooted soil microenvironment and improve our understanding of the potential risks of microplastics in soil ecosystems.

Intra Block Copy (IBC) cannot work well for blocks with symmetry or mirror characteristic, because it is based on the translational block matching technique. In order to solve this problem, in this paper, an adequately designed IBC-Mirror mode is proposed for Versatile Video Coding (VVC), which extends our previous work. The newly proposed IBC-Mirror mode consists of two sub-modes, including IBC-Mirror Advanced Motion Vector Prediction (AMVP) mode and IBC-Mirror Merge mode. In IBC-Mirror AMVP mode, various Motion Estimation (ME) methods are applied, including previously proposed spatial search, and newly designed methods, i.e., Hash-based, Cache-based, Predictor-based and History-based IBC-Mirror search. As for IBC-Mirror Merge mode, Motion Information, composed of Block Vector (BV) and mirror flipping information, is added to the merge list to improve coding efficiency. Compared to the traditional IBC mode in VTM-10.0, the latest test model, the proposed mode can achieve 1~2% BD-rate saving. Compared to our previous work, methods in this paper can further achieve 0.5% coding gains and reduce 4% coding time. Higher coding efficiency and lower complexity show the superiority of extensions and improvements in this paper.

The Internet of Things (IoT) has created a novel ecosystem for sensing and actuation throughout our world, enabling intelligently controlled autonomous systems to conserve energy, water crops, manage factories, and provide situation awareness on an unprecedented scale. As IoT progresses, the interest in IoT search engines, that is, search engines to find IoT devices and retrieve IoT data, has grown. While basic examples of IoT search engines exist, considerable challenges prevent the full realization of an efficient and intelligent IoT search engine that provides universal data service, scalable data communication and retrieval, and efficient querying of massively distributed heterogeneous devices and data. In this article, we first propose a generic framework for the IoT search engine, and then present a naming service for the IoT system, an essential component for an effective IoT search engine. We also outline some research challenges and possible solutions for building efficiency and intelligence in the IoT search engine. Further, we present a case study and seek to address a particular aspect of the query process for IoT search, namely efficient and timely query processing. Given the now obvious advances in machine learning, the potential for deep learning-based prediction to improve resource use, and thus query retrieval, is clear. In detail, we utilize Long-Short-Term Memory (LSTM) neural network architecture to predict aggregated query volumes to be preemptively applied and stored for immediate response. Combining several realistic IoT datasets, we explore the efficacy of simultaneously predicting multiple targets for predictive query retrieval.

Recombinant antibodies are rapidly developing therapeutic agents; approximately 40 novel antibody molecules enter clinical trials each year, most of which are produced from Chinese hamster ovary (CHO) cells. However, one of the major bottlenecks restricting the development of antibody drugs is how to perform high-level expression and production of recombinant antibodies. The high-efficiency expression and quality of recombinant antibodies in CHO cells is determined by multiple factors. This review provides a comprehensive overview of several state-of-the-art approaches, such as optimization of gene sequence of antibody, construction and optimization of high-efficiency expression vector, using antibody expression system, transformation of host cell lines, and glycosylation modification. Finally, the authors discuss the potential of large-scale production of recombinant antibodies and development of culture processes for biopharmaceutical manufacturing in the future.

Background As the demand for cosmetic surgery continues to rise, plastic surgery programs and the training core curriculum have evolved to reflect these changes. This study aims to evaluate the perceived quality of current cosmetic surgery training in terms of case exposure and educational methods. Methods A 16-question survey was sent to graduates who completed their training at a U.S. plastic surgery training program in 2017. The survey assessed graduates’ exposure to cosmetic surgery, teaching modalities employed and their overall perceived competence. Case complexity was characterized by the minimum number of cases needed by the graduate to feel confident in performing the procedure. Results There was a 25% response rate. The majority of respondents were residents (83%, n=92) and the remaining were fellows (17%, n=18). Almost three quarters of respondents were satisfied with their cosmetic training. Respondents rated virtual training as the most effective learning modality and observing attendings’ patients/cases as least effective. Perceived competence was more closely aligned with core curriculum status than case complexity, i.e. graduates feel more prepared for core cosmetic procedures despite being more technically difficult than non-core procedures. Conclusions Despite the variability in cosmetic exposure during training, most plastic surgery graduates are satisfied with their aesthetic training. Incorporation of teaching modalities, such as virtual training, can increase case exposure and allow trainees more autonomy. The recommended core curriculum is adequately training plastic surgery graduates for common procedures and more specialized procedures should be consigned to aesthetic fellowship training.

Tung tree (Vernicia fordii) is an economically important woody oil plant that produces tung oil rich in eleostearic acid. Here, we report a high-quality chromosome-scale genome sequence of tung tree. The genome sequence was assembled by combining Illumina short reads, Pacific Biosciences single-molecule real-time long reads, and Hi-C sequencing data. The size of tung tree genome is 1.12 Gb, with 28,422 predicted genes and over 73% repeat sequences. The V. fordii underwent an ancient genome triplication event shared by core eudicots but no further whole-genome duplication in the subsequent ca. 34.55 million years of evolutionary history of the tung tree lineage. Insertion time analysis revealed that repeat-driven genome expansion might have arisen as a result of long-standing long terminal repeat retrotransposon bursts and lack of efficient DNA deletion mechanisms. The genome harbors 88 resistance genes encoding nucleotide-binding sites; 17 of these genes may be involved in early-infection stage of Fusarium wilt resistance. Further, 651 oil-related genes were identified, 88 of which are predicted to be directly involved in tung oil biosynthesis. Relatively few phosphoenolpyruvate carboxykinase genes, and synergistic effects between transcription factors and oil biosynthesis-related genes might contribute to the high oil content of tung seed. The tung tree genome constitutes a valuable resource for understanding genome evolution, as well as for molecular breeding and genetic improvements for oil production.

Cancer patients are at a high risk of being infected with COVID-19 and have a poor prognosis after infection. Breast cancer is one of the most common cancers. Since vaccination is an effective measure to prevent the spread of COVID-19, we studied the vaccination rate among breast cancer survivors and analyzed their characteristics to provide evidence for boosting the vaccination rate. The researchers conducted a multicenter, cross-sectional study on 747 breast cancer survivors from six hospitals in Wuhan city between June 5, 2021, and June 12, 2021. The self-administrated questionnaires based on relevant studies were distributed. The researchers then compared differences in characteristics among vaccinated patients, hesitant patients, and non-vaccinated patients. Moreover, they performed univariable and multivariable logistic regression analyses to identify potential factors associated with vaccination hesitancy. The researchers assessed a total of 744 breast cancer survivors −94 cases in the vaccinated group, 103 in the planning group, 295 in the hesitancy group, and 252 in the refusal group. The vaccination rate was 12.63% (95% CI 10.25–15.02%) and 37.23% (95% CI 27.48–47.82%) patients reported adverse reactions. The vaccination hesitancy/refusal rate was 73.52% (95% CI 70.19–76.66%), which was independently associated with current endocrine or targeted therapy (odds ratio [OR] = 1.52, 95% CI 1.03–2.24), no notification from communities or units (OR = 2.46, 95% CI 1.69–3.59) and self-perceived feel (general vs. good, OR = 1.46, 95% CI 1.01–2.13; bad vs. good, OR = 4.75, 95% CI 1.85–12.16). In the hesitancy/refusal group, the primary reason was “I did not know who to ask whether I can get vaccinated” (46.07%), the person who would most influence decisions of patients was the doctor in charge of treatment (35.83%). Effective interaction between doctors and patients, simple and consistent practical guidelines on vaccination, and timely and positive information from authoritative media could combat misinformation and greatly reduce vaccine hesitancy among breast cancer survivors.

A major determinant of fruit production in longan (Dimocarpus longan Lour.) is the difficulty of blossoming. In this study, high-throughput microRNA sequencing (miRNA-Seq) was carried out to compare differentially expressed miRNAs (DEmiRNAs) and their target genes between a continuous flowering cultivar ‘Sijimi’ (SJ), and a unique cultivar ‘Lidongben’ (LD), which blossoms only once in the season. Over the course of our study, 1662 known miRNAs and 235 novel miRNAs were identified and 13,334 genes were predicted to be the target of 1868 miRNAs. One conserved miRNA and 29 new novel miRNAs were identified as differently expressed; among them, 16 were upregulated and 14 were downregulated. Through the KEGG pathway and cluster analysis of DEmiRNA target genes, three critical regulatory pathways, plant–pathogen interaction, plant hormone signal transduction, and photosynthesis-antenna protein, were discovered to be strongly associated with the continuous flowering trait of the SJ. The integrated correlation analysis of DEmiRNAs and their target mRNAs revealed fourteen important flowering-related genes, including COP1-like, Casein kinase II, and TCP20. These fourteen flowering-related genes were targeted by five miRNAs, which were novel-miR137, novel-miR76, novel-miR101, novel-miR37, and csi-miR3954, suggesting these miRNAs might play vital regulatory roles in flower regulation in longan. Furthermore, novel-miR137 was cloned based on small RNA sequencing data analysis. The pSAK277-miR137 transgenic Arabidopsis plants showed delayed flowering phenotypes. This study provides new insight into molecular regulation mechanisms of longan flowering.

Lung cancer patients who smoke show a better response to immunotherapy than non-smokers. Here, the authors show that tobacco smoke induces PD-L1 expression on lung epithelial cells via AhR that is associated with benefits of PD-1 inhibitor in patients, shedding new lights on lung carcinogenesis and immunotherapy.

The advancement of the Internet of Things (IoT) has allowed for unprecedented data collection, automation, and remote sensing and actuation, transforming autonomous systems and bringing smart command and control into numerous cyber physical systems (CPS) that our daily lives depend on. Simultaneously, dramatic improvements in machine learning and deep neural network architectures have enabled unprecedented analytical capabilities, which we see in increasingly common applications and production technologies, such as self-driving vehicles and intelligent mobile applications. Predictably, these technologies have seen rapid adoption, which has left many implementations vulnerable to threats unforeseen or undefended against. Moreover, such technologies can be used by malicious actors, and the potential for cyber threats, attacks, intrusions, and obfuscation that are only just being considered, applied, and countered. In this paper, we consider the good, the bad, and the ugly use of machine learning for cybersecurity and CPS/IoT. In detail, we consider the numerous benefits (good use) that machine learning has brought, both in general, and specifically for security and CPS/IoT, such as the improvement of intrusion detection mechanisms and decision accuracy in CPS/IoT. More pressing, we consider the vulnerabilities of machine learning (bad use) from the perspectives of security and CPS/IoT, including the ways in which machine learning systems can be compromised, misled, and subverted at all stages of the machine learning life-cycle (data collection, pre-processing, training, validation, implementation, etc.). Finally, the most concerning, a growing trend has been the utilization of machine learning in the execution of cyberattacks and intrusions (ugly use). Thus, we consider existing mechanisms with the potential to improve target acquisition and existing threat patterns, as well as those that can enable novel attacks yet to be seen.

In response to the recent frequent outbreaks of bursty events, if the supervision is not strengthened, the grievances caused by it will have an extremely negative impact on society. Therefore, how to effectively detect bursty events in social networks has become the focus of research. In order to eliminate the interference of local daily events generated by noisy data, this paper proposes a bursty event detection method for quantifying the influence of microblog text. Through the analysis of high-impact microblogs, the burst words are mined, the potential bursty event data sets are constructed, and the k-means cluster analysis method is applied to detect the event. In order to verify the method, this paper compares with the commonly used detection methods on the real dataset. The experimental results show that the method has certain improvement in accuracy and efficiency.

With the development of the Internet of Things (IoT), massive numbers of IoT devices (smart sensors, cameras, phones, and so on) have been deployed and utilized in various environments, supporting numerous smart-world applications. Those devices communicate with computing infrastructure servers over network infrastructures to send relevant collected data in order to link physical objects to the cyber world. As the number of IoT devices increases rapidly, the volume of collected data likewise increases prodigiously. Thus, how to search for and through specific IoT datasets among the enormous amount of data become critical issues for potential data consumers. Moreover, various IoT devices and applications establish IoT-based systems, also known as the smart-world systems or smart cyber-physical systems (CPS), such as smart grids, smart transportation, smart healthcare, smart cities, smart homes, and smart manufacturing systems, among others. However, the individual CPS are independently designed and deployed, such that they collect and analyze data independently, with no information sharing or interconnection, raising serious challenges in searching for valuable information. Thus, in order to efficiently and precisely utilize the IoT datasets, suitable search techniques designed for the IoT environments are fundamental. In this paper, we first summarize popular web search techniques and survey existing research on the search and analysis related to the IoT. We then outline the opportunities and challenges of the IoT search techniques. Furthermore, we propose a problem space for the IoT search techniques and provide a clear view of potential future research directions.

The degree of land use reflects the progress of social and economic development; however, it also has a direct impact on land resources. Maximizing the ecosystem services of land resources in a limited space is a key issue in China’s rapid urbanization. Therefore, this study aims to analyze the spatial relationship between land use patterns and ecosystem services to enhance the benefits of urban ecology. First, we used the landscape pattern index to represent land use patterns and the equivalence factor method to quantify the ecosystem services value (ESV); second, spatial autocorrelation and spatial autoregression were used to explore the spatial relationship between the landscape pattern index and ESV. Our main conclusions were that (1) the landscape pattern index and ESV both showed obvious spatial aggregation, but that of ESV was more significant; (2) the largest patch index and contagion index had a greater degree of influence on ESV than other variables, with the largest patch index having a positive effect and the contagion index having a negative effect; (3) it was necessary to cultivate the landscape dominance of land patches in ecological spatial regulation and to form large-scale ecological agglomeration in key ecological source areas and nodes. The research results can ensure that land resources exert a higher level of ecological value by adjusting the spatial form of the landscape patch.