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Objective: To establish a prediction Nomogram based on prognostic factors for children with hemophagocytic lymphohistiocytosis (HLH). Methods: A retrospective analysis of pediatric HLH cases diagnosed at the Affiliated Hospital of Zunyi Medical University between January 2012 and December 2022 was conducted. Cox regression analysis was used to identify prognostic factors for pediatric HLH patients. The C-index, ROC curve, and AUC were used to evaluate the discrimination of the model. The DCA was used to evaluate the clinical application value of the model. Results: A total of 133 cases of secondary pediatric HLH patients were included in this study, with 45 deaths and 88 survivors. Univariate analysis showed that age ≤ 2 years, PLT ≤ 50×10 /L, HB ≤90 g/L, AST ≥ 200 U/L, CK-MB ≥ 50 U/L, LDH ≥ 1000 U/L, SF ≥1500 μg/L, PT ≥ 20 s, APTT ≥ 40 s, hypoalbuminemia, hypofibrinogenemia, mechanical ventilation, splenomegaly, ARDS, respiratory failure,CNSL, shock, DIC, pulmonary hemorrhage, and gastrointestinal bleeding are risk factors for the survival of those secondary pediatric HLH patients ( P P = 0.049). Multivariable Cox showed that CNSL (HR = 3.18, 95%CI = 1.72-5.89), PLT ≤ 50×10^9/L (HR = 2.16, 95%CI = 1.11-4.19), hypoalbuminemia (HR=2.65, 95%CI=1.14-5.17), and hypofibrinogenemia (HR = 2.48, 95%CI = 1.19-5.14) were independent risk factors for the outcome of children with HLH, while the use of blood purification therapy (HR = 0.32, 95%CI = 0.16-0.64) was an independent protective factor. A Nomogram prediction model was constructed using R software, and the ROC curve , C-index, and calibration curve showed good discrimination and fit of the model. The DCA curve showed that the model had good clinical applicability. Finally, based on the Nomogram score and HR value, the subjects were divided into three groups, and it was found that the mortality rate in the high-risk group was significantly higher than that in the low-risk group. Conclusion: The development of a Nomogram to predict the prognosis of secondary pediatric HLH patients has good discrimination and accuracy and may have good clinical application value.

Objective To investigate the clinical characteristics of neonatal necrotizing enterocolitis (NEC) complicated by intestinal perforation and predict the incidence of intestinal perforation in NEC. Methods Neonates diagnosed with NEC at the Affiliated Hospital of Zunyi Medical University from January 2012 to May 2022 were enrolled, and the clinical data were collected and analyzed retrospectively. The patients were divided into two groups based on intestinal perforation occurrence or not. Mann-Whitney U tests, t-tests, chi-square tests, and fisher's exact tests were performed between-group comparisons. Logistic and lasso regressions were applied to screen independent risk factors for concomitant bowel perforation, and R software (RMS package) was used to formulate the nomogram prediction model. In addition, the receiver operating curve (ROC) and the calibration curve were drawn to verify the predictive power, while decision curve analysis (DCA) was constructed to evaluate the clinical applicability of the nomogram model. Results 180 neonates with NEC were included, of which 48 had intestinal perforations, and 132 did not; the overall incidence of intestinal perforation was 26.67% (48/180). Bloody stool (OR = 5.60), APTT ≥ 50s (OR = 3.22), thrombocytopenia (OR = 4.74), and hypoalbuminemia (OR = 5.56) were identified as independent risk variables for NEC intestinal perforation (P

Strong light–matter interaction plays a central role in realizing quantum photonic technologies. The entanglement state, which results from the hybridization of excitons and cavity photons, forms the foundation of quantum information science. In this work, an entanglement state is achieved by manipulating the mode coupling between surface lattice resonance and quantum emitter into the strong coupling regime. At the same time, a Rabi splitting of 40 meV is observed. A full quantum model based on the Heisenberg picture is used to describe this unclassical phenomenon, and it perfectly explains the interaction and dissipation process. In addition, the observed concurrency degree of the entanglement state is 0.5, presenting the quantum nonlocality. This work effectively contributes to the understanding of nonclassical quantum effects arising from strong coupling and will intrigue more interesting potential applications in quantum optics.

Strong light-matter interaction plays a central role in realizing quantum photonic technologies. The entanglement state, which results from the hybridization of excitons and cavity photons, forms the foundation of quantum information science. In this work, an entanglement state is achieved by manipulating the mode coupling between surface lattice resonance and quantum emitter into the strong coupling regime. At the same time, a Rabi splitting of 40 meV is observed. A full quantum model based on the Heisenberg picture is used to describe this unclassical phenomenon, and it perfectly explains the interaction and dissipation process. In addition, the observed concurrency degree of the entanglement state is 0.5, presenting the quantum non-locality. This work effectively contributes to the understanding of non-classical quantum effects arising from strong coupling and will intrigue more interesting potential applications in quantum optics.

Background. Acute myeloid leukemia (AML) is one of the most common hematological malignancies and accounts for about 20% of childhood leukemias. Currently, immunotherapy is one of the recommended treatment schemes for recurrent AML patients to improve their survival rates. Nonetheless, low remission and high mortality rates are observed in recurrent AML and challenge the prognosis of AML patients. To address this problem, we aimed to establish and verify a reliable prognostic risk model using immune-related genes to improve the prognostic evaluation and recommendation for personalized treatment of AML. Methods. Transcriptome data and clinical data were acquired from the TARGET database while immune genes were sourced from InnateDB and ImmPort Shared databases. The mRNA expression profile matrix of immune genes from 62 normal samples and 1408 AML cases was extracted from the transcriptome data and subjected to differential expression (DE) analysis. The entire cohort of DE immune genes was randomly divided into the test group and training group. The prognostic model associated with immune genes was constructed using the training group. The test group and entire cohort were employed for model validation. Lastly, we analyzed the potential clinical application of the model and its association with immune cell infiltration. Results. In total, 751 DE immune genes were differentially regulated, including 552 upregulated and 199 downregulated. Based on these DE genes, we developed and validated a prognostic risk model composed of seven immune genes, GDF1, TPM2, IL1R1, PSMD4, IL5RA, DHCR24, and IL12RB2. This model is able to predict the 5-year survival rate more accurately compared with age, gender, and risk stratification. Further analysis showed that CD8+ T-cell contents and neutrophil infiltration decreased but macrophage infiltration increased as the risk score increased. Conclusions. A seven-immune gene model of AML was developed and validated. We propose this model as an independent prognostic variable able to estimate the 5-year survival rate. In addition, the model can also reflect the immune microenvironment of AML patients.

In this article, we propose a novel reinforcement learning network that keeps track of the gradual emotional changes from every utterance throughout the conversation and uses this information for each utterance’s emotion detection. Concretely, we first establish an agent and, then, utilize sliding windows to extract the accumulated emotional information before the current utterance. We define the concatenation of accumulated emotional information and the contextual information as the state of the reinforcement learning framework. The action of the established agent is formulated as the emotional label of the current utterance. On this basis, we formulate the progressive emotional interaction process throughout the conversation as a sequential decision problem and solve it with the reinforcement learning framework. Detailed evaluations on the published multimodal MELD dataset demonstrate the effectiveness of our approach.

Bound states in the continuum (BICs), manifesting themselves as the collapse of Fano resonance, are observed in many photonic and plasmonic systems. The BICs have been studied systematically through various methods such as the topological photonics analysis, temporal coupled mode theory, multipole decomposition method, and the cavity quantum electrodynamics (CQED) method. Since CQED can give a macroscopic and intrinsic description of light–matter interaction, it is expected to study BIC that participates in strong coupling. What is more, the relation between coupling strength, the Fano parameter, and the asymmetry property of BICs needs to be clarified. In this paper, we investigated the strong coupling between the cavity mode and Bloch-surface plasmon polariton (Bloch-SPP) mode induced by BICs within the plasmonic lattices of the metal-dielectric-metal (MDM) layer. The properties of strong coupling and BIC were revealed theoretically by the quantum model based on the CQED. The increase in the Fano parameters of BICs was proved to facilitate the coupling strength, which was indicated by the monotonically increasing relation between the Fano parameter and the coupling strength. This work may pave the way for flexible modulation and application of BIC in the fields of high-quality plasmonic nanocavity, low-threshold nano-lasers, and quantum information.

Methane (CH4) is a common greenhouse gas that plays an important role in global warming and affects people’s production and life. In order to study the temporal and spatial distribution situation of methane concentration in northwest China (Shaanxi, Gansu, Ningxia, Xinjiang, Qinghai), this article used L4B level data products monitored by GOSAT satellite from 2009 to 2019 to study the temporal and spatial distribution characteristics of near-surface methane concentration in northwest China. The results of the analysis were as follows. Firstly, the annual average concentration of near-surface methane in northwest China was in a rising state, and the maximum annual average concentration increment was in 2016. Secondly, seasonal variation of methane concentration: Autumn>Summer>Spring>Winter. Finally, spatial variation of the annual average methane concentration: Shaanxi, Ningxia, and southern Gansu>northwest Xinjiang>other northwest China regions. The methane concentration decreased from the bottom to the top gradually.

In this letter, we propose a novel Interactive Multimodal Attention Network (IMAN) for emotion recognition in conversations. IMAN introduces a cross-modal attention fusion module to capture cross-modal interactions of multimodal information, and employs a conversational modeling module to explore the context information and speaker dependency of the whole conversation. Concretely, the cross-modal attention fusion module captures the cross-modal interactions and complementary information among the pre-extracted unimodal features from textual, visual, acoustic modalities based on the cross-modal attention block. Afterward, the updated features from each modality are fused to concentrate more on the informative modality and achieve a refined feature for each constituent utterance. The conversational modeling module defines three different gated recurrent units (GRUs) with respect to the context information, the speaker dependency, and the emotional state of utterances. In this way, we exploit the speaker dependency and contextual information to obtain the emotional state of utterances for emotion classification. Empirical evaluations on the multimodal benchmark IEMOCAP dataset demonstrate that our IMAN achieves competitive performance compared to the state-of-the-art approaches.

Developing a direct method to easily quantify the number of DNA capped on gold nanoparticles (GNPs) is of great significance. Herein, we found that the high concentration of iodine ion (I

Pure shift methods improve the resolution of proton nuclear magnetic resonance spectra at the cost of time. The pure shift yielded by chirp excitation (PSYCHE) method is a promising pure shift method. We propose a method of reconstructing the undersampled PSYCHE spectra based on deep learning to accelerate the spectra acquisition. It only takes 17 s to obtain a high-quality pure shift spectrum. The network can completely remove undersampling artifacts and chunking sidebands and improve the signal-to-noise ratio, obtaining completely clean pure shift spectra. The reconstruction quality is better than the iterative soft thresholding method. In addition, the network can differentiate low-level signals and chunking sidebands with similar intensities in the mixture, remove sidebands, and retain signals, promoting correct mixture analysis.

Enhancing light–matter interaction into the strong coupling regime attracts tremendous attention in both theory and experiment, which presents essential significance in research from nano-optics to quantum information. In this work, the entanglement effect is observed in the photons emitted from a plasmonic lattice as the coherent light–matter interaction occurs into the strong coupling regime with a Rabi splitting of 93.4 meV. A full quantum mechanical treatment based on the number state representation is established to reveal the underlying physics of the strong coupling phenomenon, especially the femtosecond dynamics of energy exchange and damping. The entangled split states display oscillating concurrence and negative Wigner quasiprobability distribution function, which demonstrates that this designed plasmonic lattice system can serve as an on-demand entangled photon source for quantum information.

T helper 2 (Th2) cytokine production by invariant natural killer T (iNKT) cells is involved in the development of asthma, but the regulation of Th2 cytokines in iNKT cells remains unknown. Although it is known that progranulin (PGRN) induces the production of Th2 cytokines in iNKT cells in vivo, the underlying mechanism is not clear. This study aims to investigate the role of PGRN in iNKT cells. The effects of PGRN on the differentiation of iNKT cells was detected by flow cytometry. Then stimulation of iNKT cells and airway resistance were carried out to evaluate the function of PGRN on iNKT cells. Furthermore, the mechanisms of PGRN in regulating iNKT cells was investigated by RT-PCR, WB, confocal and luciferase reporter assays. The absolute number of iNKT cells decreased in PGRN KO mice despite an increase in the percentage of iNKT cells. Furthermore, analyzing the subsets of iNKT cells, we found that NKT2 cells and their IL-4 production were reduced. Mechanistically, the decrease in NKT2 cells in the PGRN KO mice was caused by increased expression of enhancer of zeste homolog 2 (EZH2), that in turn caused increased degradation and altered nuclear localization of PLZF. Interestingly, PGRN signaling decreased expression of EZH2 and treatment of the PGRN KO mice with the EZH2 specific inhibitor GSK343 rescued the defect in NKT2 differentiation, IL-4 generation, and PLZF expression. Altogether, We have revealed a new pathway (PGRN-EZH2-PLZF), which regulates the Th2 responses of iNKT cells and provides a potentially new target for asthma treatment.

Pectin nanofiber mats prepared with periodate oxidation–electrospinning–adipic acid dihydrazide crosslinking strategy are promising for biomedical applications. In this study, we systematically examined the effects of electrospinning and crosslinking conditions on the properties of pectin nanofiber mats. The properties of mats were tunable in the range of 200–400 nm fiber size, 11–21% ADH residue content, 13–28 times absorbency, 13°–21° contact angle, 2 weeks or longer degradation time, 1.5–2.2 MPa tensile strength, 40–70% elongation, and 0.25–0.27 g/(cm2·24 h) permeability. Increasing polymer concentration, adipic acid dihydrazide amount, time or temperature could increase fiber size and its tensile strength, and decrease the absorbency, hydrophilicity, degradation rate, and elongation. These results indicate that controlling the process parameters can effectively regulate the properties of pectin nanofiber mats and meet the requirements of various biomedical applications.

Insulin autoimmune syndrome (IAS) is characterized by the presence of hyperinsulinemia, with high insulin levels and endogenous insulin autoantibody (IAA) in individuals without previous exposure to exogenous insulin1. Drugs composed of sulfhydryl groups have been considered as important factors. Among sulfhydryl-containing compounds, alpha lipoic acid (ALA) has been reported frequently as a potential determinant of IAS 2. Here we describe a case of Chinese woman diagnosed as IAS after using ALA for trigeminal neuralgia.

Pectin nanofiber mats are promising tissue engineering scaffolds but suffer from poor cell infiltration. In this study, gelatin, a collagen derived cell adhesive protein, was used to crosslink the electrospun nanofibers of periodate oxidized pectin. Cell culture experiment results demonstrated that cells were able to grow into the gelatin-crosslinked pectin nanofiber mats rather than only spread on mat surface. The nanofiber mats showed moderate mechanical strength, with a maximum tensile strength of up to 2.3 MPa, an ultimate tensile strain of up to 15%, and were capable of degrading gradually over 4 weeks or even longer periods in simulated body fluids. Thus, gelatin-crosslinked pectin nanofiber mats hold a great potential for soft tissue regeneration.

Motivation: DNA copy number aberration (CNA) is a hallmark of genomic abnormality in tumor cells. Recurrent CNA (RCNA) occurs in multiple cancer samples across the same chromosomal region and has greater implication in tumorigenesis. Current commonly used methods for RCNA identification require CNA calling for individual samples before cross-sample analysis. This two-step strategy may result in a heavy computational burden, as well as a loss of the overall statistical power due to segmentation and discretization of individual sample's data. We propose a population-based approach for RCNA detection with no need of single-sample analysis, which is statistically powerful, computationally efficient and particularly suitable for high-resolution and large-population studies. Results: Our approach, correlation matrix diagonal segmentation (CMDS), identifies RCNAs based on a between-chromosomal-site correlation analysis. Directly using the raw intensity ratio data from all samples and adopting a diagonal transformation strategy, CMDS substantially reduces computational burden and can obtain results very quickly from large datasets. Our simulation indicates that the statistical power of CMDS is higher than that of single-sample CNA calling based two-step approaches. We applied CMDS to two real datasets of lung cancer and brain cancer from Affymetrix and Illumina array platforms, respectively, and successfully identified known regions of CNA associated with EGFR, KRAS and other important oncogenes. CMDS provides a fast, powerful and easily implemented tool for the RCNA analysis of large-scale data from cancer genomes. Availability: The R and C programs implementing our method are available at https://dsgweb.wustl.edu/qunyuan/software/cmds. Contact: qunyuan@wustl.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Detection and characterization of genomic structural variation are important for understanding the landscape of genetic variation in human populations and in complex diseases such as cancer. Recent studies demonstrate the feasibility of detecting structural variation using next-generation, short-insert, paired-end sequencing reads. However, the utility of these reads is not entirely clear, nor are the analysis methods with which accurate detection can be achieved. The algorithm BreakDancer predicts a wide variety of structural variants including insertion-deletions (indels), inversions and translocations. We examined BreakDancer's performance in simulation, in comparison with other methods and in analyses of a sample from an individual with acute myeloid leukemia and of samples from the 1,000 Genomes trio individuals. BreakDancer sensitively and accurately detected indels ranging from 10 base pairs to 1 megabase pair that are difficult to detect via a single conventional approach.

Lay Summary Acute myeloid leukemia is a highly malignant hematopoietic tumor that affects about 13,000 adults yearly in the United States. The treatment of this disease has changed little in the past two decades, since most of the genetic events that initiate the disease remain undiscovered. Whole genome sequencing is now possible at a reasonable cost and timeframe to utilize this approach for unbiased discovery of tumor-specific somatic mutations that alter the protein-coding genes. Here we show the results obtained by sequencing a typical acute myeloid leukemia genome and its matched normal counterpart, obtained from the patient’s skin. We discovered 10 genes with acquired mutations; two were previously described mutations thought to contribute to tumor progression, and 8 were novel mutations present in virtually all tumor cells at presentation and relapse, whose function is not yet known. Our study establishes whole genome sequencing as an unbiased method for discovering initiating mutations in cancer genomes, and for identifying novel genes that may respond to targeted therapies. We used massively parallel sequencing technology to sequence the genomic DNA of tumor and normal skin cells obtained from a patient with a typical presentation of FAB M1 Acute Myeloid Leukemia (AML) with normal cytogenetics. 32.7-fold ‘haploid’ coverage (98 billion bases) was obtained for the tumor genome, and 13.9-fold coverage (41.8 billion bases) was obtained for the normal sample. Of 2,647,695 well-supported Single Nucleotide Variants (SNVs) found in the tumor genome, 2,588,486 (97.7%) also were detected in the patient’s skin genome, limiting the number of variants that required further study. For the purposes of this initial study, we restricted our downstream analysis to the coding sequences of annotated genes: we found only eight heterozygous, non-synonymous somatic SNVs in the entire genome. All were novel, including mutations in protocadherin/cadherin family members (CDH24 and PCLKC), G-protein coupled receptors (GPR123 and EBI2), a protein phosphatase (PTPRT), a potential guanine nucleotide exchange factor (KNDC1), a peptide/drug transporter (SLC15A1), and a glutamate receptor gene (GRINL1B). We also detected previously described, recurrent somatic insertions in the FLT3 and NPM1 genes. Based on deep readcount data, we determined that all of these mutations (except FLT3) were present in nearly all tumor cells at presentation, and again at relapse 11 months later, suggesting that the patient had a single dominant clone containing all of the mutations. These results demonstrate the power of whole genome sequencing to discover novel cancer-associated mutations.

Human cancer cells typically harbour multiple chromosomal aberrations, nucleotide substitutions and epigenetic modifications that drive malignant transformation. The Cancer Genome Atlas ( TCGA) pilot project aims to assess the value of large- scale multi- dimensional analysis of these molecular characteristics in human cancer and to provide the data rapidly to the research community. Here we report the interim integrative analysis of DNA copy number, gene expression and DNA methylation aberrations in 206 glioblastomas - the most common type of primary adult brain cancer - and nucleotide sequence aberrations in 91 of the 206 glioblastomas. This analysis provides new insights into the roles of ERBB2, NF1 and TP53, uncovers frequent mutations of the phosphatidylinositol- 3- OH kinase regulatory subunit gene PIK3R1, and provides a network view of the pathways altered in the development of glioblastoma. Furthermore, integration of mutation, DNA methylation and clinical treatment data reveals a link between MGMT promoter methylation and a hypermutator phenotype consequent to mismatch repair deficiency in treated glioblastomas, an observation with potential clinical implications. Together, these findings establish the feasibility and power of TCGA, demonstrating that it can rapidly expand knowledge of the molecular basis of cancer.

In this work, we present the Genome Modeling System (GMS), an analysis information management system capable of executing automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled with reliable and repeatable analysis pipelines. The GMS also serves as a platform for bioinformatics development, allowing a large team to collaborate on data analysis, or an individual researcher to leverage the work of others effectively within its data management system. Rather than separating ad-hoc analysis from rigorous, reproducible pipelines, the GMS promotes systematic integration between the two. As a demonstration of the GMS, we performed an integrated analysis of whole genome, exome and transcriptome sequencing data from a breast cancer cell line (HCC1395) and matched lymphoblastoid line (HCC1395BL). These data are available for users to test the software, complete tutorials and develop novel GMS pipeline configurations. The GMS is available at https://github.com/genome/gms.

Since its release in 2000, WormBase (http://www .wormbase.org) has grown from a small resource focusing on a single species and serving a dedicated research community, to one now spanning 15 species essential to the broader biomedical and agricultural research fields. To enhance the rate of curation, we have automated the identification of key data in the scientific literature and use similar methodology for data extraction. To ease access to the data, we are collaborating with journals to link entities in research publications to their report pages at WormBase. To facilitate discovery, we have added new views of the data, integrated large-scale datasets and expanded descriptions of models for human disease. Finally, we have introduced a dramatic overhaul of the WormBase website for public beta testing. Designed to balance complexity and usability, the new site is species-agnostic, highly customizable, and interactive. Casual users and developers alike will be able to leverage the public RESTful application programming interface (API) to generate custom data mining solutions and extensions to the site. We report on the growth of our database and on our work in keeping pace with the growing demand for data, efforts to anticipate the requirements of users and new collaborations with the larger science community. © The Author(s) 2011.

The full complement of DNA mutations that are responsible for the pathogenesis of acute myeloid leukemia (AML) is not yet known.We used massively parallel DNA sequencing to obtain a very high level of coverage (approximately 98%) of a primary, cytogenetically normal, de novo genome for AML with minimal maturation (AML-M1) and a matched normal skin genome.We identified 12 acquired (somatic) mutations within the coding sequences of genes and 52 somatic point mutations in conserved or regulatory portions of the genome. All mutations appeared to be heterozygous and present in nearly all cells in the tumor sample. Four of the 64 mutations occurred in at least 1 additional AML sample in 188 samples that were tested. Mutations in NRAS and NPM1 had been identified previously in patients with AML, but two other mutations had not been identified. One of these mutations, in the IDH1 gene, was present in 15 of 187 additional AML genomes tested and was strongly associated with normal cytogenetic status; it was present in 13 of 80 cytogenetically normal samples (16%). The other was a nongenic mutation in a genomic region with regulatory potential and conservation in higher mammals; we detected it in one additional AML tumor. The AML genome that we sequenced contains approximately 750 point mutations, of which only a small fraction are likely to be relevant to pathogenesis.By comparing the sequences of tumor and skin genomes of a patient with AML-M1, we have identified recurring mutations that may be relevant for pathogenesis.

Determining the genetic basis of cancer requires comprehensive analyses of large collections of histopathologically well-classified primary tumours. Here we report the results of a collaborative study to discover somatic mutations in 188 human lung adenocarcinomas. DNA sequencing of 623 genes with known or potential relationships to cancer revealed more than 1,000 somatic mutations across the samples. Our analysis identified 26 genes that are mutated at significantly high frequencies and thus are probably involved in carcinogenesis. The frequently mutated genes include tyrosine kinases, among them the EGFR homologue ERBB4; multiple ephrin receptor genes, notably EPHA3; vascular endothelial growth factor receptor KDR; and NTRK genes. These data provide evidence of somatic mutations in primary lung adenocarcinoma for several tumour suppressor genes involved in other cancers--including NF1, APC, RB1 and ATM--and for sequence changes in PTPRD as well as the frequently deleted gene LRP1B. The observed mutational profiles correlate with clinical features, smoking status and DNA repair defects. These results are reinforced by data integration including single nucleotide polymorphism array and gene expression array. Our findings shed further light on several important signalling pathways involved in lung adenocarcinoma, and suggest new molecular targets for treatment.