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Nature-inspired metaheuristic algorithms are important components of artificial intelligence, and are increasingly used across disciplines to tackle various types of challenging optimization problems. This paper demonstrates the usefulness of such algorithms for solving a variety of challenging optimization problems in statistics using a nature-inspired metaheuristic algorithm called competitive swarm optimizer with mutated agents (CSO-MA). This algorithm was proposed by one of the authors and its superior performance relative to many of its competitors had been demonstrated in earlier work and again in this paper. The main goal of this paper is to show a typical nature-inspired metaheuristic algorithmi, like CSO-MA, is efficient for tackling many different types of optimization problems in statistics. Our applications are new and include finding maximum likelihood estimates of parameters in a single cell generalized trend model to study pseudotime in bioinformatics, estimating parameters in the commonly used Rasch model in education research, finding M-estimates for a Cox regression in a Markov renewal model, performing matrix completion tasks to impute missing data for a two compartment model, and selecting variables optimally in an ecology problem in China. To further demonstrate the flexibility of metaheuristics, we also find an optimal design for a car refueling experiment in the auto industry using a logistic model with multiple interacting factors. In addition, we show that metaheuristics can sometimes outperform optimization algorithms commonly used in statistics., (© 2024. The Author(s).)

Background: Acute lung injury (ALI) and its final severe stage, acute respiratory distress syndrome, are associated with high morbidity and mortality rates in patients due to the lack of effective specific treatments. Gut microbiota homeostasis, including that in ALI, is important for human health. Evidence suggests that the gut microbiota improves lung injury through the lung-gut axis. Human umbilical cord mesenchymal cells (HUC-MSCs) have attractive prospects for ALI treatment. This study hypothesized that HUC-MSCs improve ALI via the lung-gut microflora., Aim: To explore the effects of HUC-MSCs on lipopolysaccharide (LPS)-induced ALI in mice and the involvement of the lung-gut axis in this process., Methods: C57BL/6 mice were randomly divided into four groups (18 rats per group): Sham, sham + HUC-MSCs, LPS, and LPS + HUC-MSCs. ALI was induced in mice by intraperitoneal injections of LPS (10 mg/kg). After 6 h, mice were intervened with 0.5 mL phosphate buffered saline (PBS) containing 1 × 10 6 HUC-MSCs by intraperitoneal injections. For the negative control, 100 mL 0.9% NaCl and 0.5 mL PBS were used. Bronchoalveolar lavage fluid (BALF) was obtained from anesthetized mice, and their blood, lungs, ileum, and feces were obtained by an aseptic technique following CO 2 euthanasia. Wright's staining, enzyme-linked immunosorbent assay, hematoxylin-eosin staining, Evans blue dye leakage assay, immunohistochemistry, fluorescence in situ hybridization, western blot, 16S rDNA sequencing, and non-targeted metabolomics were used to observe the effect of HUC-MSCs on ALI mice, and the involvement of the lung-gut axis in this process was explored. One-way analysis of variance with post-hoc Tukey's test, independent-sample Student's t -test, Wilcoxon rank-sum test, and Pearson correlation analysis were used for statistical analyses., Results: HUC-MSCs were observed to improve pulmonary edema and lung and ileal injury, and decrease mononuclear cell and neutrophil counts, protein concentrations in BALF and inflammatory cytokine levels in the serum, lung, and ileum of ALI mice. Especially, HUC-MSCs decreased Evans blue concentration and Toll-like receptor 4, myeloid differentiation factor 88, p-nuclear factor kappa-B (NF-κB)/NF-κB, and p-inhibitor α of NF-κB (p-IκBα)/IκBα expression levels in the lung, and raised the pulmonary vascular endothelial-cadherin, zonula occludens-1 (ZO-1), and occludin levels and ileal ZO-1, claudin-1, and occludin expression levels. HUC-MSCs improved gut and BALF microbial homeostases. The number of pathogenic bacteria decreased in the BALF of ALI mice treated with HUC-MSCs. Concurrently, the abundances of Oscillospira and Coprococcus in the feces of HUS-MSC-treated ALI mice were significantly increased. In addition, Lactobacillus , Bacteroides , and unidentified_Rikenellaceae genera appeared in both feces and BALF. Moreover, this study performed metabolomic analysis on the lung tissue and identified five upregulated metabolites and 11 downregulated metabolites in the LPS + MSC group compared to the LPS group, which were related to the purine metabolism and the taste transduction signaling pathways. Therefore, an intrinsic link between lung metabolite levels and BALF flora homeostasis was established., Conclusion: This study suggests that HUM-MSCs attenuate ALI by redefining the gut and lung microbiota., Competing Interests: Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article., (©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.)

Introduction: Continuous glucose monitoring (CGM) devices capture longitudinal data on interstitial glucose levels and are increasingly used to show the dynamics of diabetes metabolism. Given the complexity of CGM data, it is crucial to extract important patterns hidden in these data through efficient visualization and statistical analysis techniques., Methods: In this paper, we adopted the concept of glucodensity, and using a subset of data from an ongoing clinical trial in pediatric individuals and young adults with new-onset type 1 diabetes, we performed a cluster analysis of glucodensities. We assessed the differences among the identified clusters using analysis of variance (ANOVA) with respect to residual pancreatic beta-cell function and some standard CGM-derived parameters such as time in range, time above range, and time below range., Results: Distinct CGM data patterns were identified using cluster analysis based on glucodensities. Statistically significant differences were shown among the clusters with respect to baseline levels of pancreatic beta-cell function surrogate (C-peptide) and with respect to time in range and time above range., Discussion: Our findings provide supportive evidence for the value of glucodensity in the analysis of CGM data. Some challenges in the modeling of CGM data include unbalanced data structure, missing observations, and many known and unknown confounders, which speaks to the importance of--and provides opportunities for--taking an approach integrating clinical, statistical, and data science expertise in the analysis of these data., Competing Interests: The analyzed data is from a study sponsored by Novartis Institutes for Biomedical Research. Authors AG, MQ, DJ, and OS are employed by the company Novartis. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Cui, Goldfine, Quinlan, James and Sverdlov.)

Swine influenza virus (SIV) circulates worldwide, posing substantial economic loss and disease burden to humans and animals. Vaccination remains the most effective way to prevent SIV infection and transmission. In this study, we evaluated the protective efficacy of a recombinant, baculovirus-insect cell system-expressed bivalent nanoparticle SIV vaccine in mice challenged with drifted swine influenza H1N1 and H3N2 viruses. After a prime-boost immunization, the bivalent nanoparticle vaccine (BNV) induced high levels of hemagglutination inhibition (HAI) antibodies, virus-neutralization (VN) antibodies, and antigen-specific IgG antibodies in mice, as well as more efficient cytokine levels. The MF59 and CPG1 adjuvant could significantly promote both humoral and cellular immunity of BNV. The MF59 adjuvant showed a balanced Th1/Th2 immune response, and the CPG1 adjuvant tended to show a Th1-favored response. The BALB/c challenge test showed that BNV could significantly reduce lung viral loads and feces viral shedding, and showed fewer lung pathological lesions than those in PBS and inactivated vaccine groups. These results suggest that this novel bivalent nanoparticle swine influenza vaccine can be used as an efficacious vaccine candidate to induce robust immunity and provide broad protection against drifted subtypes in mice. Immune efficacy in pigs needs to be further evaluated.

Motivation: Modeling single-cell gene expression trends along cell pseudotime is a crucial analysis for exploring biological processes. Most existing methods rely on nonparametric regression models for their flexibility; however, nonparametric models often provide trends too complex to interpret. Other existing methods use interpretable but restrictive models. Since model interpretability and flexibility are both indispensable for understanding biological processes, the single-cell field needs a model that improves the interpretability and largely maintains the flexibility of nonparametric regression models., Results: Here, we propose the single-cell generalized trend model (scGTM) for capturing a gene's expression trend, which may be monotone, hill-shaped or valley-shaped, along cell pseudotime. The scGTM has three advantages: (i) it can capture non-monotonic trends that are easy to interpret, (ii) its parameters are biologically interpretable and trend informative, and (iii) it can flexibly accommodate common distributions for modeling gene expression counts. To tackle the complex optimization problems, we use the particle swarm optimization algorithm to find the constrained maximum likelihood estimates for the scGTM parameters. As an application, we analyze several single-cell gene expression datasets using the scGTM and show that scGTM can capture interpretable gene expression trends along cell pseudotime and reveal molecular insights underlying biological processes., Availability and Implementation: The Python package scGTM is open-access and available at https://github.com/ElvisCuiHan/scGTM., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

This study aimed to analyze nosocomial respiratory infection (NRI) in patients with bronchial asthma. Among the clinical data of 575 asthmatic patients that was collected and analyzed, 52 were diagnosed with NRI. The most common gram-positive bacterial species was Streptococcus pneumoniae, which was detected in 8 patients, whereas the predominant Gram-negative bacteria included Haemophilus influenzae (11 patients), Moraxella catarrhalis (8 patients), and Escherichia coli (7 patients). The simultaneous detection of all strains was predominant in patients older than 65 years of age, whereas the detection rates of S. pneumoniae, H. influenzae, E. coli, and M. catarrhalis were predominant in patients younger than 65 years old. The differences in the detection rates were not significant between the male and female groups. From this study, we can conclude that S. pneumoniae, H. influenzae, E. coli, and M. catarrhalis are common NRI-causing pathogens, and bacterial infection is the main risk factor for NRI in asthmatic patients.

Aim: To investigate the expression profile of microRNAs in inoperable advanced non-small cell lung cancer (NSCLC) patients receiving chemotherapy and the potential relevance of microRNAs to clinicopathological characteristics and prognosis., Methods: Serum samples were taken from 260 inoperable advanced NSCLC patients and 260 healthy individuals. All the patients received cisplatin-based chemotherapy, including NP/NC regimens, GP/GC regimens, and TP/TC regimens. The serum levels of microRNAs (miR-125b, miR-10b, miR-34a and miR-155) were determined by quantitative real-time PCR., Results: Serum levels of the 4 microRNAs examined in NSCLC patients were significantly increased as compared with healthy individuals. The levels of miR-125b and miR-155 were changed in a similar pattern: the patients with stage IV disease had the highest one, while the patients with stage III A and stage III B disease showed similar increased levels. The levels of miR-10b and miR-34a in the patients with different stages were increased to similar extent. The level of miR-125b in poorly differentiated cancer was significantly higher than those in well and moderately differentiated cancers, while the levels of miR-10b, miR-34a, and miR-155 did not significantly differ with cancer differentiation. Among the 4 microRNAs examined, only miR-125b was significantly associated with therapeutic response, exhibiting higher expression levels in non-responsive patients. Furthermore, the high level of miR-125b was significantly correlated with poor patient survival. A multivariate Cox regression analysis showed that the expression level of miR-125b was an independent prognostic marker in NSCLC patients., Conclusion: Our results suggest that miR-125b is a potential diagnostic or prognostic biomarker for NSCLC. This finding has important implications for development of targeted therapeutics to overcome chemotherapeutic resistance in NSCLC.