Tag: statistics

Sci Rep. 2022 Nov 12;12(1):19390. doi: 10.1038/s41598-022-23781-x.

ABSTRACT

In this study, the capabilities of classical and novel integrated machine learning models were investigated to predict sediment discharge (Q_s) in free-flow flushing. Developed models include Multivariate Linear Regression (MLR), Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Group Method of Data Handling (GMDH), and four hybrid forms of GMDH and Support Vector Regression (SVR) in combination with Henry Gas Solubility Optimization (HGSO) and Equilibrium Optimizer (EO) algorithms. The number of 160 datasets that were applied to assess these models was extracted from the Janssen (PhD’s Thesis, Engineering, Civil and Environmental Engineering. University of California, 1999) experimental study. Input parameters to predict Q_s included the water level in the reservoir (h_w), bed level in the flushing channel (h_b), outflow (Q_out), inflow (Q_in), and elapsed time of flushing (T). The performance of all models was evaluated by four statistical indices of root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R²), and Mean absolute relative error (MARE). Evaluation of results demonstrated that the HGSO and EO algorithms could enhance the accuracy of the GMDH model (up to 26% and 22% in terms of RMSE), respectively. According to statistical criteria, the SVR-EO and SVR-HGSO provided the highest accuracy in both training (R² = 0.98) and validation phases (R² = 0.96). Moreover, among the developed models, the GMDH-HGSO algorithm provided excellent fitness to the observed data (R² = 0.96, RMSE = 22.37, MAE = 15.65, and MARE = 0.26). The results indicated the high efficiency of the HGSO and EO algorithms in improving the accuracy of the GMDH and SVR models. However, among the developed models, the GMDH-HGSO is the most accurate model and is recommended for sediment transport modelling.

PMID:36371476 | DOI:10.1038/s41598-022-23781-x

Ann Biomed Eng. 2022 Nov 12. doi: 10.1007/s10439-022-03099-5. Online ahead of print.

ABSTRACT

Synthetic surrogate head models are used in biomechanical studies to investigate skull, brain, and cervical spine injury. To ensure appropriate biofidelity of these head models, the stiffness is often tuned so that the surrogate’s response approximates the cadaveric response corridor. Impact parameters such as energy, and loading direction and region, can influence injury prediction measures, such as impact force and head acceleration. An improved understanding of how impact parameters affect the head’s structural response is required for designing better surrogate head models. This study comprises a synthesis and review of all existing ex vivo head stiffness data, and the primary factors that influence the force-deformation response are discussed. Eighteen studies from 1972 to 2019 were identified. Head stiffness statistically varied with age (pediatric vs. adult), loading region, and rate. The contact area of the impactor likely affects stiffness, whereas the impactor mass likely does not. The head’s response to frontal impacts was widely reported, but few studies have evaluated the response to other impact locations and directions. The findings from this review indicate that further work is required to assess the effect of head constraints, loading region, and impactor geometry, across a range of relevant scenarios.

PMID:36371475 | DOI:10.1007/s10439-022-03099-5

Commun Med (Lond). 2022 Apr 20;2(1):43. doi: 10.1038/s43856-022-00093-9.

ABSTRACT

BACKGROUND: Frailty is a late-life clinical syndrome resulting from the accumulation of aging-induced decline. Greenspaces measured with normalized difference vegetation index (NDVI) are protective of frailty. However, NDVI is not as informative as structure indices in describing greenspaces’ constitution, shape, and connectivity measured by the largest patch index (LPI), shape index, and cohesion index representing larger, more complex, and more dense greenspaces through higher values. We aim to study the association between greenness structures and frailty in a cohort of Chinese older adults.

METHODS: We included older adults from 2008-2014 China Longitudinal Healthy Longevity Survey (CLHLS). We used greenspace indices from satellite to quantify structures (area-edge, shape, proximity) at county-level, and calculated frailty index (FI) as an outcome. We did cross-sectional analyses using linear and logistical regression, and longitudinal analyses using the generalized estimating equations (GEE).

RESULTS: Among 8776 baseline participants, mean LPI, shape, cohesion, and FI are 7.93, 8.11, 97.6, and 0.17. In cross-sectional analyses, we find negative dose-response relationships for greenspace structures and frailty, especially in females, centenarians, illiterate people, city residents, unmarried people, and individuals with increased frailty. Participants living in the highest quartile of LPI, shape, and cohesion have 32% (95%CI: 21-42%), 35% (95%CI: 24-44%), and 37% (95%CI: 26%-46%) lower odds of frailty than the lowest quartile. However, we do not find a significant association in longitudinal analyses.

CONCLUSIONS: Higher levels of greenness structures (area-edge, shape, and proximity) might be related to lower frailty, while a clear longitudinal benefit cannot be identified in this analysis.

PMID:36371474 | DOI:10.1038/s43856-022-00093-9

Sci Rep. 2022 Nov 12;12(1):19409. doi: 10.1038/s41598-022-24005-y.

ABSTRACT

Drug information centers (DICs) are institutions dedicated to provide objective, independent, and up-to-date information on drugs and their rational use. To overcome the lack of recent DIC reports from central Europe, we analyzed all queries (n = 594) submitted to the DIC run by the Institute for Clinical Pharmacology of Hannover Medical School between October 2018 and April 2022. Approximately one in three queries (31.1%; 185/594) was submitted by internists. 82.8% (492/594) of the queries were patient-specific, while the remaining 17.2% (102/594) were general queries. Adverse drug reactions (ADRs), indications/contraindications, and pharmacodynamic interactions (PDIs) represented the three most frequently addressed query categories, being involved in 44.8% (266/594), 43.3% (257/594), and 34.3% (204/594) of all queries, respectively (assignment of more than one category per query was possible). As compared to general queries, patient-specific queries were statistically significantly more often related to ADRs, PDIs, and pharmacokinetic interactions (PKIs) (ADRs: 35.3% vs. 46.7%, P = 0.034; PDIs: 14.7% vs. 38.4%, P < 0.001; PKIs: 20.6% vs. 31.5%, P = 0.028). To demonstrate the complexity of queries submitted to the clinical-pharmacological DIC, we present and comment on an illustrative selection of queries.

PMID:36371467 | DOI:10.1038/s41598-022-24005-y

Schizophrenia (Heidelb). 2022 Nov 12;8(1):95. doi: 10.1038/s41537-022-00308-x.

ABSTRACT

Both the ability to speak and to infer complex linguistic messages from sounds have been claimed as uniquely human phenomena. In schizophrenia, formal thought disorder (FTD) and auditory verbal hallucinations (AVHs) are manifestations respectively relating to concrete disruptions of those abilities. From an evolutionary perspective, Crow (1997) proposed that “schizophrenia is the price that Homo sapiens pays for the faculty of language”. Epidemiological and experimental evidence points to an overlap between FTD and AVHs, yet a thorough investigation examining their shared neural mechanism in schizophrenia is lacking. In this review, we synthesize observations from three key domains. First, neuroanatomical evidence indicates substantial shared abnormalities in language-processing regions between FTD and AVHs, even in the early phases of schizophrenia. Second, neurochemical studies point to a glutamate-related dysfunction in these language-processing brain regions, contributing to verbal production deficits. Third, genetic findings further show how genes that overlap between schizophrenia and language disorders influence neurodevelopment and neurotransmission. We argue that these observations converge into the possibility that a glutamatergic dysfunction in language-processing brain regions might be a shared neural basis of both FTD and AVHs. Investigations of language pathology in schizophrenia could facilitate the development of diagnostic tools and treatments, so we call for multilevel confirmatory analyses focused on modulations of the language network as a therapeutic goal in schizophrenia.

PMID:36371445 | DOI:10.1038/s41537-022-00308-x

Nat Commun. 2022 Nov 12;13(1):6894. doi: 10.1038/s41467-022-34487-z.

ABSTRACT

Seasonal influenza outbreaks, especially in high-risk groups such as the elderly, represent an important public health problem. Prevailing inadequate efficacy of seasonal vaccines is a crucial bottleneck. Understanding the immunological and molecular mechanisms underpinning differential influenza vaccine responsiveness is essential to improve vaccination strategies. Here we show comprehensive characterization of the immune response of randomly selected elderly participants (≥ 65 years), immunized with the adjuvanted influenza vaccine Fluad. In-depth analyses by serology, multi-parametric flow cytometry, multiplex and transcriptome analysis, coupled to bioinformatics and mathematical modelling, reveal distinguishing immunological and molecular features between responders and non-responders defined by vaccine-induced seroconversion. Non-responders are specifically characterized by multiple suppressive immune mechanisms. The generated comprehensive high dimensional dataset enables the identification of putative mechanisms and nodes responsible for vaccine non-responsiveness independently of confounding age-related effects, with the potential to facilitate development of tailored vaccination strategies for the elderly.

PMID:36371426 | DOI:10.1038/s41467-022-34487-z

Sci Rep. 2022 Nov 12;12(1):19420. doi: 10.1038/s41598-022-23381-9.

ABSTRACT

In the current investigation, a new urchin-like nanostructure using an authorized one-pot precipitation technique was synthesized using Taguchi statistical design. The effect of factors, including the concentration of FeSO₄⋅7H₂O, KMnO₄, NaOH, and reaction temperature, on the diameter-to-length ratio of the nanoneedles and the regularity or irregularity morphology of other samples, was investigated. KMnO₄ and FeSO₄⋅7H₂O, with a contribution of 32.62% and 30.9%, had the most substantial effect on the nanoneedles’ diameter. The surface morphology and chemical composition of the as-prepared samples were studied using field emission scanning electron microscopy, X-ray diffraction, and energy dispersive spectroscopy (EDX) analyses. The length and needle diameter was obtained 140 and 17 nm, respectively. The result of BET analysis for the highest and the lowest value of D/L shows that the smallest ratio of diameter to length has a high specific surface area. The results show that sample S4 has a particular surface area of 74 m²/g, much more than S3 (25 m²/g). The estimated crystallite size in synthesized S3 and S4 samples are 41.64 and 26.49 nm, respectively.

PMID:36371416 | DOI:10.1038/s41598-022-23381-9

Nat Commun. 2022 Nov 12;13(1):6874. doi: 10.1038/s41467-022-34360-z.

ABSTRACT

Joint analyses of genomic datasets obtained in multiple different conditions are essential for understanding the biological mechanism that drives tissue-specificity and cell differentiation, but they still remain computationally challenging. To address this we introduce CLIMB (Composite LIkelihood eMpirical Bayes), a statistical methodology that learns patterns of condition-specificity present in genomic data. CLIMB provides a generic framework facilitating a host of analyses, such as clustering genomic features sharing similar condition-specific patterns and identifying which of these features are involved in cell fate commitment. We apply CLIMB to three sets of hematopoietic data, which examine CTCF ChIP-seq measured in 17 different cell populations, RNA-seq measured across constituent cell populations in three committed lineages, and DNase-seq in 38 cell populations. Our results show that CLIMB improves upon existing alternatives in statistical precision, while capturing interpretable and biologically relevant clusters in the data.

PMID:36371401 | DOI:10.1038/s41467-022-34360-z

Dig Liver Dis. 2022 Nov 9:S1590-8658(22)00749-6. doi: 10.1016/j.dld.2022.10.016. Online ahead of print.

ABSTRACT

BACKGROUND: Association between Hidradenitis Suppurativa (HS) and Inflammatory Bowel Disease (IBD) has been suggested.

AIMS: To assess characteristics of HS and IBD in patients with or without concomitant IBD.

METHODS: In a prospective, nested case-control study, each IBD patient with concomitant HS (Case) was retrospectively matched with 4 patients with HS and no IBD (Controls) for gender and age (±5 years).HS was classified according to the Hurley score and the International Hidradenitis Suppurativa Severity Score System (IHS4). Data were expressed as mean (Standard Deviation). Statistical analysis included Student-t Test or Mann-Whitney Test, χ2 test, univariate and multivariate logistic regression.

RESULTS: The study population included 125 patients with HS: 25 with IBD, 100 matched Controls with no IBD. IBD group included 19 (76%) Crohn’s disease and 6 (24%) Ulcerative Colitis patients. Obesity, familial HS and perianal HS were less frequent in Cases than in Controls (1[4%] vs 25(25%];p = 0.02; 1[4%] vs 21(21%];p = 0.04; 1[4%] vs 31(31%];p = 0.005, respectively).HS was less severe in Cases when assessed by the IHS4 (5.9 ± 4 vs 9 ± 6.7;p = 0.04).Complete drug-induced response for HS was more frequent in IBD (13[53%] vs28 (28%]; p = 0.04).

CONCLUSION: Clinical characteristics of HS and of patients differed between Cases and Controls. Present findings suggest the need to appropriately search and assess skin lesions compatible with HS in IBD.

PMID:36371384 | DOI:10.1016/j.dld.2022.10.016

Acad Radiol. 2022 Nov 9:S1076-6332(22)00572-4. doi: 10.1016/j.acra.2022.10.016. Online ahead of print.

ABSTRACT

RATIONALE AND OBJECTIVES: We investigated the diagnostic performance of dual-energy CT (DECT) virtual non-calcium (VNCa) and Rho/Z images for bone marrow infiltration of primary malignant bone tumors (PMBTs).

MATERIALS AND METHODS: We retrospectively analyzed 65 patients with PMBT who underwent DECT and MRI within 2 weeks. DECT was used to evaluate the presence and extent of marrow involvement surrounding PMBTs using the SCT, VNCa, and Rho/Z images. MRI was used as the reference standard for measurements. CT values of normal and involved bone marrow areas were measured on VNCa images, and Zeff values were measured on Rho/Z images. The statistical methods used were the 2*C chi-square test, ANOVA test, paired samples t test, and diagnostic performance of the different variables were evaluated using receiver operating characteristic curves.

RESULTS: VNCa and Rho/Z images showed higher accuracy (91%, 92% vs. 67%) and sensitivity (90%, 92% vs. 69%) than SCT images for diagnosing bone marrow infiltration in patients with PMBT. The maximum longitudinal diameter of tumor involvement measurements was statistically different between VNCa and SCT, Rho/Z and SCT, MRI, and SCT (all p < 0.05, p = 0.047, p = 0.049, and p = 0.023, respectively). The maximum transverse diameter was statistically significant between SCT and MRI, VNCa and MRI, Rho/Z and MRI (all p < 0.05, and p = 0.015, and p = 0.044, and p = 0.047, respectively). The HU or Zeff values based on the area of interest of VNCa and Rho/Z images differed significantly between the normal and infiltrated bone marrow area (p < 0.001). Receiver operating characteristic curve analysis revealed area under the curves of 0.995 and 0.988, respectively, with cut-off values of -31.57 HU and 7.8, and the sensitivity of both was 96.9%.

CONCLUSION: DECT-VNCa and Rho/Z images have good diagnostic value when evaluating bone marrow infiltration in PMBTs.

PMID:36371375 | DOI:10.1016/j.acra.2022.10.016