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1.
BMC Bioinformatics ; 22(1): 480, 2021 Oct 04.
Artigo em Inglês | MEDLINE | ID: mdl-34607566

RESUMO

BACKGROUND: Identifying interaction effects between genes is one of the main tasks of genome-wide association studies aiming to shed light on the biological mechanisms underlying complex diseases. Multifactor dimensionality reduction (MDR) is a popular approach for detecting gene-gene interactions that has been extended in various forms to handle binary and continuous phenotypes. However, only few multivariate MDR methods are available for multiple related phenotypes. Current approaches use Hotelling's T2 statistic to evaluate interaction models, but it is well known that Hotelling's T2 statistic is highly sensitive to heavily skewed distributions and outliers. RESULTS: We propose a robust approach based on nonparametric statistics such as spatial signs and ranks. The new multivariate rank-based MDR (MR-MDR) is mainly suitable for analyzing multiple continuous phenotypes and is less sensitive to skewed distributions and outliers. MR-MDR utilizes fuzzy k-means clustering and classifies multi-locus genotypes into two groups. Then, MR-MDR calculates a spatial rank-sum statistic as an evaluation measure and selects the best interaction model with the largest statistic. Our novel idea lies in adopting nonparametric statistics as an evaluation measure for robust inference. We adopt tenfold cross-validation to avoid overfitting. Intensive simulation studies were conducted to compare the performance of MR-MDR with current methods. Application of MR-MDR to a real dataset from a Korean genome-wide association study demonstrated that it successfully identified genetic interactions associated with four phenotypes related to kidney function. The R code for conducting MR-MDR is available at https://github.com/statpark/MR-MDR . CONCLUSIONS: Intensive simulation studies comparing MR-MDR with several current methods showed that the performance of MR-MDR was outstanding for skewed distributions. Additionally, for symmetric distributions, MR-MDR showed comparable power. Therefore, we conclude that MR-MDR is a useful multivariate non-parametric approach that can be used regardless of the phenotype distribution, the correlations between phenotypes, and sample size.


Assuntos
Estudo de Associação Genômica Ampla , Redução Dimensional com Múltiplos Fatores , Algoritmos , Simulação por Computador , Epistasia Genética , Modelos Genéticos , Fenótipo , Polimorfismo de Nucleotídeo Único
2.
Clin Mol Hepatol ; 2021 Oct 15.
Artigo em Inglês | MEDLINE | ID: mdl-34649307

RESUMO

Background & Aims: To develop an early prediction model for gestational diabetes (GDM) using machine learning and evaluate whether the inclusion of nonalcoholic fatty liver disease (NAFLD)-associated variables increases the performance of model. Methods: This prospective cohort study evaluated pregnant women for NAFLD by ultrasound at 10-14 weeks and screened them for GDM at 24-28 weeks of gestation. The clinical variables before 14 weeks were used to develop prediction models for GDM (Setting 1, conventional risk factors; Setting 2, addition of new risk factors in recent guidelines; Setting 3, addition of routine clinical variables; Setting 4, addition of NALFD-associated variables, including the presence of NAFLD and laboratory results; and Setting 5, top 11 variables identified from a stepwise variable selection method). The predictive models were constructed using machine learning methods, including logistic regression, random forest, support vector machine, and deep neural networks. Results: Among 1,443 women, 86 (6.0%) were diagnosed with GDM. The highest performing prediction model among Settings 1-4 was Setting 4, which included both clinical and NAFLD-associated variables (area under the receiver operating characteristic curve [AUC] 0.563-0.697 in Settings 1-3 vs. 0.740-0.781 in Setting 4). Setting 5 with top 11 variables (which included NAFLD and hepatic steatosis index), showed similar predictive power to Setting 4 (AUC 0.719-0.819 in Setting 5, p=NS between Settings 4 and 5). Conclusions: We developed an early prediction model for GDM using machine learning. The inclusion of NAFLD-associated variables significantly improved the performance of GDM prediction.

3.
Sci Rep ; 11(1): 20495, 2021 Oct 14.
Artigo em Inglês | MEDLINE | ID: mdl-34650119

RESUMO

The outbreak of novel COVID-19 disease elicited a wide range of anti-contagion and economic policies like school closure, income support, contact tracing, and so forth, in the mitigation and suppression of the spread of the SARS-CoV-2 virus. However, a systematic evaluation of these policies has not been made. Here, 17 implemented policies from the Oxford COVID-19 Government Response Tracker dataset employed in 90 countries from December 31, 2019, to August 31, 2020, were analyzed. A Poisson regression model was applied to analyze the relationship between policies and daily confirmed cases using a generalized estimating equations approach. A lag is a fixed time displacement in time series data. With that, lagging (0, 3, 7, 10, and 14 days) was also considered during the analysis since the effects of policies implemented on a given day may affect the number of confirmed cases several days after implementation. The countries were divided into three groups depending on the number of waves of the pandemic observed in each country. Through subgroup analysis, we showed that with and without lagging, contact tracing and containment policies were significant for countries with two waves, while closing, economic, and health policies were significant for countries with three waves. Wave-specific analysis for each wave showed that significant health, economic, and containment policies varied across waves of the pandemic. Emergency investment in healthcare was consistently significant among the three groups of countries, while the Stringency index was significant among all waves of the pandemic. These findings may help in making informed decisions regarding whether, which, or when these policies should be intensified or lifted.

4.
Bioinformatics ; 2021 Sep 13.
Artigo em Inglês | MEDLINE | ID: mdl-34515762

RESUMO

MOTIVATION: Drug repositioning reveals novel indications for existing drugs and in particular, diseases with no available drugs. Diverse computational drug repositioning methods have been proposed by measuring either drug-treated gene expression signatures or the proximity of drug targets and disease proteins found in prior networks. However, these methods do not explain which signaling subparts allow potential drugs to be selected, and do not consider polypharmacology, i.e., multiple targets of a known drug, in specific subparts. RESULTS: Here, to address the limitations, we developed a subpathway-based polypharmacology drug repositioning method, PATHOME-Drug, based on drug-associated transcriptomes. Specifically, this tool locates subparts of signaling cascading related to phenotype changes (e.g., disease status changes), and identifies existing approved drugs such that their multiple targets are enriched in the subparts. We show that our method demonstrated better performance for detecting signaling context and specific drugs/compounds, compared to WebGestalt and clusterProfiler, for both real biological and simulated datasets. We believe that our tool can successfully address the current shortage of targeted therapy agents. AVAILABILITY: The web-service is available at http://statgen.snu.ac.kr/software/pathome. The source codes and data are available at https://github.com/labnams/pathome-drug. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

5.
ACS Nano ; 2021 Sep 08.
Artigo em Inglês | MEDLINE | ID: mdl-34495639

RESUMO

Next-generation devices and systems require the development and integration of advanced materials, the realization of which inevitably requires two separate processes: property engineering and patterning. Here, we report a one-step, ink-lithography technique to pattern and engineer the properties of thin films of colloidal nanocrystals that exploits their chemically addressable surface. Colloidal nanocrystals are deposited by solution-based methods to form thin films and a local chemical treatment is applied using an ink-printing technique to simultaneously modify (i) the chemical nature of the nanocrystal surface to allow thin-film patterning and (ii) the physical electronic, optical, thermal, and mechanical properties of the nanocrystal thin films. The ink-lithography technique is applied to the library of colloidal nanocrystals to engineer thin films of metals, semiconductors, and insulators on both rigid and flexible substrates and demonstrate their application in high-resolution image replications, anticounterfeit devices, multicolor filters, thin-film transistors and circuits, photoconductors, and wearable multisensors.

6.
Hepatol Commun ; 5(10): 1767-1783, 2021 10.
Artigo em Inglês | MEDLINE | ID: mdl-34558815

RESUMO

Hepatocellular carcinoma (HCC) is a malignant cancer with one of the highest mortality rates. Des-γ-carboxyprothrombin (DCP) is an HCC serologic surveillance marker that can complement the low sensitivity of alpha-fetoprotein (AFP). DCP exists in the blood as a mixture of proteoforms from an impaired carboxylation process at glutamic acid (Glu) residues within the N-terminal domain. The heterogeneity of DCP may affect the accuracy of measurements because DCP levels are commonly determined using an immunoassay that relies on antibody reactivity to an epitope in the DCP molecule. In this study, we aimed to improve the DCP measurement assay by applying a mass spectrometry (MS)-based approach for a more inclusive quantification of various DCP proteoforms. We developed a multiple-reaction monitoring-MS (MRM-MS) assay to quantify multiple noncarboxylated peptides included in the various des-carboxylation states of DCP. We performed the MRM-MS assay in 300 patients and constructed a robust diagnostic model that simultaneously monitored three noncarboxylated peptides. The MS-based quantitative assay for DCP had reliable surveillance power, which was evident from the area under the receiver operating characteristic curve (AUROC) values of 0.874 and 0.844 for the training and test sets, respectively. It was equivalent to conventional antibody-based quantification, which had AUROC values at the optimal cutoff (40 mAU/mL) of 0.743 and 0.704 for the training and test sets, respectively. The surveillance performance of the MS-based DCP assay was validated using an independent validation set consisting of 318 patients from an external cohort, resulting in an AUROC value of 0.793. Conclusion: Due to cost effectiveness and high reproducibility, the quantitative DCP assay using the MRM-MS method is superior to antibody-based quantification and has equivalent performance.

7.
Nutrients ; 13(7)2021 Jul 16.
Artigo em Inglês | MEDLINE | ID: mdl-34371949

RESUMO

Homocysteine (Hcy) is well known to be increased in the metabolic syndrome (MetS) incidence. However, it remains unclear whether the relationship is causal or not. Recently, Mendelian Randomization (MR) has been popularly used to assess the causal influence. In this study, we adopted MR to investigate the causal influence of Hcy on MetS in adults using three independent cohorts. We considered one-sample MR and two-sample MR. We analyzed one-sample MR in 5902 individuals (2090 MetS cases and 3812 controls) from the KARE and two-sample MR from the HEXA (676 cases and 3017 controls) and CAVAS (1052 cases and 764 controls) datasets to evaluate whether genetically increased Hcy level influences the risk of MetS. In observation studies, the odds of MetS increased with higher Hcy concentrations (odds ratio (OR) 1.17, 95%CI 1.12-1.22, p < 0.01). One-sample MR was performed using two-stage least-squares regression, with an MTHFR C677T and weighted Hcy generic risk score as an instrument. Two-sample MR was performed with five genetic variants (rs12567136, rs1801133, rs2336377, rs1624230, and rs1836883) by GWAS data as the instrumental variables. For sensitivity analysis, weighted median and MR-Egger regression were used. Using one-sample MR, we found an increased risk of MetS (OR 2.07 per 1-SD Hcy increase). Two-sample MR supported that increased Hcy was significantly associated with increased MetS risk by using the inverse variance weighted (IVW) method (beta 0.723, SE 0.119, and p < 0.001), the weighted median regression method (beta 0.734, SE 0.097, and p < 0.001), and the MR-Egger method (beta 2.073, SE 0.843, and p = 0.014) in meta-analysis. The MR-Egger slope showed no evidence of pleiotropic effects (intercept -0.097, p = 0.107). In conclusion, this study represented the MR approach and elucidates the significant relationship between Hcy and the risk of MetS in the Korean population.


Assuntos
Predisposição Genética para Doença , Homocisteína/sangue , Síndrome Metabólica/genética , Idoso , Feminino , Humanos , Masculino , Análise da Randomização Mendeliana , Síndrome Metabólica/sangue , Metilenotetra-Hidrofolato Redutase (NADPH2)/genética , Pessoa de Meia-Idade , Razão de Chances , Polimorfismo de Nucleotídeo Único
8.
Artigo em Inglês | MEDLINE | ID: mdl-34300044

RESUMO

The outbreak of the novel COVID-19, declared a global pandemic by WHO, is the most serious public health threat seen in terms of respiratory viruses since the 1918 H1N1 influenza pandemic. It is surprising that the total number of COVID-19 confirmed cases and the number of deaths has varied greatly across countries. Such great variations are caused by age population, health conditions, travel, economy, and environmental factors. Here, we investigated which national factors (life expectancy, aging index, human development index, percentage of malnourished people in the population, extreme poverty, economic ability, health policy, population, age distributions, etc.) influenced the spread of COVID-19 through systematic statistical analysis. First, we employed segmented growth curve models (GCMs) to model the cumulative confirmed cases for 134 countries from 1 January to 31 August 2020 (logistic and Gompertz). Thus, each country's COVID-19 spread pattern was summarized into three growth-curve model parameters. Secondly, we investigated the relationship of selected 31 national factors (from KOSIS and Our World in Data) to these GCM parameters. Our analysis showed that with time, the parameters were influenced by different factors; for example, the parameter related to the maximum number of predicted cumulative confirmed cases was greatly influenced by the total population size, as expected. The other parameter related to the rate of spread of COVID-19 was influenced by aging index, cardiovascular death rate, extreme poverty, median age, percentage of population aged 65 or 70 and older, and so forth. We hope that with their consideration of a country's resources and population dynamics that our results will help in making informed decisions with the most impact against similar infectious diseases.


Assuntos
COVID-19 , Vírus da Influenza A Subtipo H1N1 , Influenza Humana , Humanos , SARS-CoV-2 , Viagem
9.
Artigo em Inglês | MEDLINE | ID: mdl-34066512

RESUMO

Increasing evidence shows that many infections of COVID-19 are asymptomatic, becoming a global challenge, since asymptomatic infections have the same infectivity as symptomatic infections. We developed a probabilistic model for estimating the proportion of undetected asymptomatic COVID-19 patients in the country. We considered two scenarios: one is conservative and the other is nonconservative. By combining the above two scenarios, we gave an interval estimation of 0.0001-0.0027 and in terms of the population, 5200-139,900 is the number of undetected asymptomatic cases in South Korea as of 2 February 2021. In addition, we provide estimates for total cases of COVID-19 in South Korea. Combination of undetected asymptomatic cases and undetected symptomatic cases to the number of confirmed cases (78,844 cases on 2 February 2021) shows that 0.17-0.42% (89,244-218,744) of the population have COVID-19. In conclusion, to control and understand the true ongoing reality of the pandemic, it is of outermost importance to focus on the ratio of undetected asymptomatic cases in the total population.


Assuntos
COVID-19 , Infecções Assintomáticas/epidemiologia , Humanos , Modelos Estatísticos , Pandemias , República da Coreia/epidemiologia , SARS-CoV-2
10.
Psychiatry Investig ; 2021 May 18.
Artigo em Inglês | MEDLINE | ID: mdl-33993688

RESUMO

OBJECTIVE: Bipolar disorder (BD) is complex genetic disorder. Therefore, approaches using clinical phenotypes such as biological rhythm disruption could be an alternative. In this study, we explored the relationship between melatonin pathway genes with circadian and seasonal rhythms of BD. METHODS: We recruited clinically stable patients with BD (n=324). We measured the seasonal variation of mood and behavior (seasonality), and circadian preference, on a lifetime basis. We analyzed 34 variants in four genes (MTNR1a, MTNR1b, AANAT, ASMT) involved in the melatonin pathway. RESULTS: Four variants were nominally associated with seasonality and circadian preference. After multiple test corrections, the rs116879618 in AANAT remained significantly associated with seasonality (corrected p=0.0151). When analyzing additional variants of AANAT through imputation, the rs117849139, rs77121614 and rs28936679 (corrected p=0.0086, 0.0154, and 0.0092) also showed a significant association with seasonality. CONCLUSION: This is the first study reporting the relationship between variants of AANAT and seasonality in patients with BD. Since AANAT controls the level of melatonin production in accordance with light and darkness, this study suggests that melatonin may be involved in the pathogenesis of BD, which frequently shows a seasonality of behaviors and symptom manifestations.

11.
Artigo em Inglês | MEDLINE | ID: mdl-33991409

RESUMO

BACKGROUND/PURPOSE: The current study aimed to develop a prediction model using a multi-marker panel as a diagnostic screening tool for pancreatic ductal adenocarcinoma. METHODS: Multi-center cohort of 1991 blood samples were collected from January 2011 to September 2019, of which 609 were normal, 145 were other cancer (colorectal, thyroid, and breast cancer), 314 were pancreatic benign disease, and 923 were pancreatic ductal adenocarcinoma. The automated multi-biomarker Enzyme-Linked Immunosorbent Assay kit was developed using three potential biomarkers: LRG1, TTR, and CA 19-9. Using a logistic regression model on a training data set, the predicted values for pancreatic ductal adenocarcinoma were obtained, and the result was classification into one of the three risk groups: low, intermediate, and high. The five covariates used to create the model were sex, age, and three biomarkers. RESULTS: Participants were categorized into four groups as normal (n = 609), other cancer (n = 145), pancreatic benign disease (n = 314), and pancreatic ductal adenocarcinoma (n = 923). The normal, other cancer, and pancreatic benign disease groups were clubbed into the non-pancreatic ductal adenocarcinoma group (n = 1068). The positive and negative predictive value, sensitivity, and specificity were 94.12, 90.40, 93.81, and 90.86, respectively. CONCLUSIONS: This study demonstrates a significant diagnostic performance of the multi-marker panel in distinguishing pancreatic ductal adenocarcinoma from normal and benign pancreatic disease states, as well as patients with other cancers.

12.
Gut Liver ; 2021 May 07.
Artigo em Inglês | MEDLINE | ID: mdl-33941710

RESUMO

Background/Aims: Several prediction models for evaluating the prognosis of nonmetastatic resected pancreatic ductal adenocarcinoma (PDAC) have been developed, and their performances were reported to be superior to that of the 8th edition of the American Joint Committee on Cancer (AJCC) staging system. We developed a prediction model to evaluate the prognosis of resected PDAC and externally validated it with data from a nationwide Korean database. Methods: Data from the Surveillance, Epidemiology and End Results (SEER) database were utilized for model development, and data from the Korea Tumor Registry System-Biliary Pancreas (KOTUS-BP) database were used for external validation. Potential candidate variables for model development were age, sex, histologic differentiation, tumor location, adjuvant chemotherapy, and the AJCC 8th staging system T and N stages. For external validation, the concordance index (C-index) and time-dependent area under the receiver operating characteristic curve (AUC) were evaluated. Results: Between 2004 and 2016, data from 9,624 patients were utilized for model development, and data from 3,282 patients were used for external validation. In the multivariate Cox proportional hazard model, age, sex, tumor location, T and N stages, histologic differentiation, and adjuvant chemotherapy were independent prognostic factors for resected PDAC. After an exhaustive search and 10-fold cross validation, the best model was finally developed, which included all prognostic variables. The C-index, 1-year, 2-year, 3-year, and 5-year time-dependent AUCs were 0.628, 0.650, 0.665, 0.675, and 0.686, respectively. Conclusions: The survival prediction model for resected PDAC could provide quantitative survival probabilities with reliable performance. External validation studies with other nationwide databases are needed to evaluate the performance of this model.

13.
ACS Nano ; 2021 Apr 01.
Artigo em Inglês | MEDLINE | ID: mdl-33792304

RESUMO

In this study, non-temperature interference strain gauge sensors, which are only sensitive to strain but not temperature, are developed by engineering the properties and structure from a material perspective. The environmental interference from temperature fluctuations is successfully eliminated by controlling the charge transport in nanoparticles with thermally expandable polymer substrates. Notably, the negative temperature coefficient of resistance (TCR), which originates from the hopping transport in nanoparticle arrays, is compensated by the positive TCR of the effective surface thermal expansion with anchoring effects. This strategy successfully controls the TCR from negative to positive. A near-zero TCR (NZTCR), less than 1.0 × 10-6 K-1, is achieved through precisely controlled expansion. Various characterization methods and finite element and transport simulations are conducted to investigate the correlated electrical, mechanical, and thermal properties of the materials and elucidate the compensated NZTCR mechanism. With this strategy, an all-solution-processed, transparent, highly sensitive, and noninterference strain sensor is fabricated with a gauge factor higher than 5000 at 1% strain, as demonstrated by pulse and motion sensing, as well as the noninterference property under variable-temperature conditions. It is envisaged that the sensor developed herein is applicable to multifunctional wearable sensors or e-skins for artificial skin or robots.

14.
J Med Internet Res ; 23(4): e25852, 2021 04 16.
Artigo em Inglês | MEDLINE | ID: mdl-33822738

RESUMO

BACKGROUND: Limited information is available about the present characteristics and dynamic clinical changes that occur in patients with COVID-19 during the early phase of the illness. OBJECTIVE: This study aimed to develop and validate machine learning models based on clinical features to assess the risk of severe disease and triage for COVID-19 patients upon hospital admission. METHODS: This retrospective multicenter cohort study included patients with COVID-19 who were released from quarantine until April 30, 2020, in Korea. A total of 5628 patients were included in the training and testing cohorts to train and validate the models that predict clinical severity and the duration of hospitalization, and the clinical severity score was defined at four levels: mild, moderate, severe, and critical. RESULTS: Out of a total of 5601 patients, 4455 (79.5%), 330 (5.9%), 512 (9.1%), and 301 (5.4%) were included in the mild, moderate, severe, and critical levels, respectively. As risk factors for predicting critical patients, we selected older age, shortness of breath, a high white blood cell count, low hemoglobin levels, a low lymphocyte count, and a low platelet count. We developed 3 prediction models to classify clinical severity levels. For example, the prediction model with 6 variables yielded a predictive power of >0.93 for the area under the receiver operating characteristic curve. We developed a web-based nomogram, using these models. CONCLUSIONS: Our prediction models, along with the web-based nomogram, are expected to be useful for the assessment of the onset of severe and critical illness among patients with COVID-19 and triage patients upon hospital admission.


Assuntos
COVID-19/diagnóstico , COVID-19/epidemiologia , Modelos Estatísticos , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Criança , Pré-Escolar , Estudos de Coortes , Feminino , Humanos , Lactente , Recém-Nascido , Masculino , Pessoa de Meia-Idade , República da Coreia/epidemiologia , Projetos de Pesquisa , Estudos Retrospectivos , SARS-CoV-2/isolamento & purificação , Adulto Jovem
15.
Biology (Basel) ; 10(3)2021 Mar 13.
Artigo em Inglês | MEDLINE | ID: mdl-33805810

RESUMO

Novel biomarkers for early diagnosis of pancreatic cancer (PC) are necessary to improve prognosis. We aimed to discover candidate biomarkers by identifying compositional differences of microbiome between patients with PC (n = 38) and healthy controls (n = 52), using microbial extracellular vesicles (EVs) acquired from blood samples. Composition analysis was performed using 16S rRNA gene analysis and bacteria-derived EVs. Statistically significant differences in microbial compositions were used to construct PC prediction models after propensity score matching analysis to reduce other possible biases. Between-group differences in microbial compositions were identified at the phylum and genus levels. At the phylum level, three species (Verrucomicrobia, Deferribacteres, and Bacteroidetes) were more abundant and one species (Actinobacteria) was less abundant in PC patients. At the genus level, four species (Stenotrophomonas, Sphingomonas, Propionibacterium, and Corynebacterium) were less abundant and six species (Ruminococcaceae UCG-014, Lachnospiraceae NK4A136 group, Akkermansia, Turicibacter, Ruminiclostridium, and Lachnospiraceae UCG-001) were more abundant in PC patients. Using the best combination of these microbiome markers, we constructed a PC prediction model that yielded a high area under the receiver operating characteristic curve (0.966 and 1.000, at the phylum and genus level, respectively). These microbiome markers, which altered microbial compositions, are therefore candidate biomarkers for early diagnosis of PC.

16.
Adv Mater ; 33(20): e2007346, 2021 May.
Artigo em Inglês | MEDLINE | ID: mdl-33739558

RESUMO

Soft neuroprosthetics that monitor signals from sensory neurons and deliver motor information can potentially replace damaged nerves. However, achieving long-term stability of devices interfacing peripheral nerves is challenging, since dynamic mechanical deformations in peripheral nerves cause material degradation in devices. Here, a durable and fatigue-resistant soft neuroprosthetic device is reported for bidirectional signaling on peripheral nerves. The neuroprosthetic device is made of a nanocomposite of gold nanoshell (AuNS)-coated silver (Ag) flakes dispersed in a tough, stretchable, and self-healing polymer (SHP). The dynamic self-healing property of the nanocomposite allows the percolation network of AuNS-coated flakes to rebuild after degradation. Therefore, its degraded electrical and mechanical performance by repetitive, irregular, and intense deformations at the device-nerve interface can be spontaneously self-recovered. When the device is implanted on a rat sciatic nerve, stable bidirectional signaling is obtained for over 5 weeks. Neural signals collected from a live walking rat using these neuroprosthetics are analyzed by a deep neural network to predict the joint position precisely. This result demonstrates that durable soft neuroprosthetics can facilitate collection and analysis of large-sized in vivo data for solving challenges in neurological disorders.

17.
Sci Rep ; 11(1): 6980, 2021 03 26.
Artigo em Inglês | MEDLINE | ID: mdl-33772054

RESUMO

Meta-analyses increase statistical power by combining statistics from multiple studies. Meta-analysis methods have mostly been evaluated under the condition that all the data in each study have an association with the given phenotype. However, specific experimental conditions in each study or genetic heterogeneity can result in "unassociated statistics" that are derived from the null distribution. Here, we show that power of conventional meta-analysis methods rapidly decreases as an increasing number of unassociated statistics are included, whereas the classical Fisher's method and its weighted variant (wFisher) exhibit relatively high power that is robust to addition of unassociated statistics. We also propose another robust method based on joint distribution of ordered p-values (ordmeta). Simulation analyses for t-test, RNA-seq, and microarray data demonstrated that wFisher and ordmeta, when only a small number of studies have an association, outperformed existing meta-analysis methods. We performed meta-analyses of nine microarray datasets (prostate cancer) and four association summary datasets (body mass index), where our methods exhibited high biological relevance and were able to detect genes that the-state-of-the-art methods missed. The metapro R package that implements the proposed methods is available from both CRAN and GitHub ( http://github.com/unistbig/metapro ).

18.
Sci Rep ; 11(1): 5001, 2021 Mar 02.
Artigo em Inglês | MEDLINE | ID: mdl-33654129

RESUMO

Multiple environmental factors could interact with a single genetic factor to affect disease phenotypes. We used Struct-LMM to identify genetic variants that interacted with environmental factors related to body mass index (BMI) using data from the Korea Association Resource. The following factors were investigated: alcohol consumption, education, physical activity metabolic equivalent of task (PAMET), income, total calorie intake, protein intake, carbohydrate intake, and smoking status. Initial analysis identified 7 potential single nucleotide polymorphisms (SNPs) that interacted with the environmental factors (P value < 5.00 × 10-6). Of the 8 environmental factors, PAMET score was excluded for further analysis since it had an average Bayes Factor (BF) value < 1 (BF = 0.88). Interaction analysis using 7 environmental factors identified 11 SNPs (P value < 5.00 × 10-6). Of these, rs2391331 had the most significant interaction (P value = 7.27 × 10-9) and was located within the intron of EFNB2 (Chr 13). In addition, the gene-based genome-wide association study verified EFNB2 gene significantly interacting with 7 environmental factors (P value = 5.03 × 10-10). BF analysis indicated that most environmental factors, except carbohydrate intake, contributed to the interaction of rs2391331 on BMI. Although the replication of the results in other cohorts is warranted, these findings proved the usefulness of Struct-LMM to identify the gene-environment interaction affecting disease.

19.
Sci Total Environ ; 772: 145386, 2021 Jun 10.
Artigo em Inglês | MEDLINE | ID: mdl-33770858

RESUMO

Soil organic matter (SOM) is related to vegetation, soil bacteria, and soil properties; however, not many studies link all these parameters simultaneously, particularly in tundra ecosystems vulnerable to climate change. Our aim was to describe the relationships between vegetation, bacteria, soil properties, and SOM composition in moist acidic tundra by integrating physical, chemical, and molecular methods. A total of 70 soil samples were collected at two different depths from 36 spots systematically arranged over an area of about 300 m × 50 m. Pyrolysis-gas chromatography/mass spectrometry and pyrosequencing of the 16S rRNA gene were used to identify the molecular compositions of the SOM and bacterial community, respectively. Vegetation and soil physicochemical properties were also measured. The sampling sites were grouped into three, based on their SOM compositions: Sphagnum moss-derived SOM, lipid-rich materials, and aromatic-rich materials. Our results show that SOM composition is spatially structured and linked to microtopography; however, the vegetation, soil properties, and bacterial community composition did not show overall spatial structuring. Simultaneously, soil properties and bacterial community composition were the main factors explaining SOM compositional variation, while vegetation had a residual effect. Verrucomicrobia and Acidobacteria were related to polysaccharides, and Chloroflexi was linked to aromatic compounds. These relationships were consistent across different hierarchical levels. Our results suggest that SOM composition at a local scale is closely linked with soil factors and the bacterial community. Comprehensive observation of ecosystem components is recommended to understand the in-situ function of bacteria and the fate of SOM in the moist acidic tundra.


Assuntos
Ecossistema , Solo , Alaska , Bactérias/genética , RNA Ribossômico 16S/genética , Microbiologia do Solo , Tundra
20.
Artigo em Inglês | MEDLINE | ID: mdl-33671746

RESUMO

Since the outbreak of novel SARS-COV-2, each country has implemented diverse policies to mitigate and suppress the spread of the virus. However, no systematic evaluation of these policies in their alleviation of the pandemic has been done. We investigate the impact of five indices derived from 12 policies in the Oxford COVID-19 Government Response Tracker dataset and the Korean government's index, which is the social distancing level implemented by the Korean government in response to the changing pandemic situation. We employed segmented Poisson model for this analysis. In conclusion, health and the Korean government indices are most consistently effective (with negative coefficients), while the restriction and stringency indexes are mainly effective with lagging (1~10 days), as intuitively daily confirmed cases of a given day is affected by the policies implemented days before, which shows that a period of time is required before the impact of some policies can be observed. The health index demonstrates the importance of public information campaign, testing policy and contact tracing, while the government index shows the importance of social distancing guidelines in mitigating the spread of the virus. These results imply the important roles of these polices in mitigation of the spread of COVID-19 disease.


Assuntos
COVID-19/prevenção & controle , Governo , Política de Saúde , Humanos , Pandemias , República da Coreia
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