Multiscale adaptive smoothing models for the hemodynamic response function in fMRI

In the event-related functional magnetic resonance imaging (fMRI) data analysis, there is an extensive interest in accurately and robustly estimating the hemodynamic response function (HRF) and its associated statistics (e.g., the magnitude and duration of the activation). Most methods to date are developed in the time domain and they have utilized almost exclusively the temporal information of fMRI data without accounting for the spatial information. The aim of this paper is to develop a multiscale adaptive smoothing model (MASM) in the frequency domain by integrating the spatial and frequency information to adaptively and accurately estimate HRFs pertaining to each stimulus sequence across all voxels in a three-dimensional (3D) volume. We use two sets of simulation studies and a real data set to examine the finite sample performance of MASM in estimating HRFs. Our real and simulated data analyses confirm that MASM outperforms several other state-of-the-art methods, such as the smooth finite impulse response (sFIR) model.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS609 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Forecasting return of used products for remanufacturing using graphical evaluation and review technique (GERT)

This research develops a forecasting model that can predict the quantity, time and probability of product return, recyclable parts/components/materials and disposal. It adopts the Graphical Evaluation and Review Technique (GERT) by translating the remanufacturing operational process into a stochastic network. This stochastic network possesses two characteristics: activities having a probability of occurrence associated with them; and time to perform an activity. Together with the GERT method, Mason’s rule is applied to calculate the equivalence transfer function of the system, therefore predicting the desired outcomes. A generic eight-step process on how to implement this method in any structure of return products and remanufacturing network is provided. A numerical example is presented to demonstrate the result of using GERT on forecasting printer remanufacturing outcomes. The main contribution of this research is: Instead of giving one result such as either return quantity, or time, or probability, our research can forecast three of these outcomes simultaneously, and the algorithm is generalised to be applicable to any product structure and remanufacturing network

Affinity of Tau antibodies for solubilized pathological Tau species but not their immunogen or insoluble Tau aggregates predicts in vivo and ex vivo efficacy

BACKGROUND: A few tau immunotherapies are now in clinical trials with several more likely to be initiated in the near future. A priori, it can be anticipated that an antibody which broadly recognizes various pathological tau aggregates with high affinity would have the ideal therapeutic properties. Tau antibodies 4E6 and 6B2, raised against the same epitope region but of varying specificity and affinity, were tested for acutely improving cognition and reducing tau pathology in transgenic tauopathy mice and neuronal cultures. RESULTS: Surprisingly, we here show that one antibody, 4E6, which has low affinity for most forms of tau acutely improved cognition and reduced soluble phospho-tau, whereas another antibody, 6B2, which has high affinity for various tau species was ineffective. Concurrently, we confirmed and clarified these efficacy differences in an ex vivo model of tauopathy. Alzheimer’s paired helical filaments (PHF) were toxic to the neurons and increased tau levels in remaining neurons. Both toxicity and tau seeding were prevented by 4E6 but not by 6B2. Furthermore, 4E6 reduced PHF spreading between neurons. Interestingly, 4E6’s efficacy relates to its high affinity binding to solubilized PHF, whereas the ineffective 6B2 binds mainly to aggregated PHF. Blocking 4E6's uptake into neurons prevented its protective effects if the antibody was administered after PHF had been internalized. When 4E6 and PHF were administered at the same time, the antibody was protective extracellularly. CONCLUSIONS: Overall, these findings indicate that high antibody affinity for solubilized PHF predicts efficacy, and that acute antibody-mediated improvement in cognition relates to clearance of soluble phospho-tau. Importantly, both intra- and extracellular clearance pathways are in play. Together, these results have major implications for understanding the pathogenesis of tauopathies and for development of immunotherapies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13024-016-0126-z) contains supplementary material, which is available to authorized users

Research on manufacturing dry mixed cement mortar with high compressive strength, high flexural strength, low shrinkage and high watertightness for restoration of damaged hydraulic structures in Vietnam

Using normal materials to manufacture the mixed mortar is necessary for restoration of hydraulic structures in Vietnam. It will salvage the materials and decreases the cost price of the mortar. In this research, we used cement made in Vietnam (Chinfon - Haiphong cement), natural sand (Lo River sand), polymer acrylic and high range water reducing (of SIKA company)' with proportion 1 : 3 : 0.03 : 0.003 by weight. The water to cement ratio is 0.5, which always ensure the compressive strength of mortar more than 40 MPa and small shrinkage, good watertightness, and high adhesion. That is suitable for the restoration of concrete structures in general and hydraulic structures in particular of Vietnam. The dry mixed mortar is manufactured and in bag of 15±0.5 kg weight

Association of mutation profiles with metastasis in patients with non-small cell lung cancer

ObjectiveThis study focused on the analysis of the correlation between common gene mutation types and metastatic sites in NSCLC patients.MethodsWe retrospectively studied 1586 NSCLC patients and used fluorescence Polymerase chain reaction (PCR) to detect EGFR, ALK, ROS1, RET, MET, BRAF, HER2, KRAS, NRAS, and PIK3CA gene mutations, and also investigated sex, smoking status, age at diagnosis, histological type and TNM stage. In addition, we analyzed the site of metastasis in patients with stage IV NSCLC.ResultsThe EGFR-mutation group more frequently metastasized to lung (18.9%, P = 0.004), brain (18.9%, P = 0.001) and bone (27.1%, P = 0.004) than wild-type patients. ALK-mutation group (71.0%, P < 0.001), BRAF-mutation group (82.4%, P = 0.005) and NRAS-mutation group (100%, P = 0.025) were more likely to metastasize than the wild-type group. In the ALK mutation, lung metastasis (24.2%, P = 0.013), brain (24.2%, P = 0.007), bone metastasis (32.3%, P = 0.024), liver metastasis (19.4%, P = 0.001), and pleural metastasis (29.0%, P = 0.021) were common. In the KRAS-mutation group, lung metastasis (21.7%, P = 0.012) and brain metastasis (23.3%, P = 0.001) were more common. Less metastasis occurred in the HER2-mutation group (28.3%, P = 0.014). There was no difference in the RET, MET and PIK3CA mutations.ConclusionPatients with ALK mutant, BRAF mutant or NRAS mutant were more prone to metastasis, while the HER 2 mutation group was less metastatic. Patients with EGFR mutant NSCLC are more likely to develop bone, lung, or brain metastasis

Relationship between abdominal fat area and first-phase insulin secretion function of pancreatic β-cells in patients with type 2 diabetes

Objective·To explore the relationship between abdominal fat area and the first-phase insulin secretion function of pancreatic β-cells in patients with type 2 diabetes, and to establish predictive models of nomogram.Methods·From October 2020 to February 2024, a total of 120 patients with type 2 diabetes, who were hospitalized in the Department of Endocrinology, Zhongshan Hospital, Fudan University, and underwent the arginine stimulation test, were recruited for the study. Patients were categorized into an insulin secretion function-preserved group (i.e. preserved group) and a depleted group according to the results of the arginine stimulation test. General information and laboratory parameters were collected. Subcutaneous fat area (SFA) and visceral fat area (VFA) were non-invasively measured by abdominal fat detector. The variables were screened by univariate analysis, and multivariate Logistic regression was used to identify the influencing factors, followed by the establishment of predictive models of nomogram. The area under the receiver operating characteristic curve (ROC curve) and concordance index (C-index) were used to evaluate the predictive performance of the models.Results·Seventy-four patients (61.7%) were assigned to the preserved group, and 46 patients (38.3%) to the depleted group. Patients in the depleted group had a longer diabetes duration, lower waist circumference, hip circumference, body mass index (BMI), uric acid, free triiodothyronine (FT3), adipose tissue insulin resistance (Adipo-IR), ankle brachial index (ABI), SFA and VFA, and higher brachial ankle pulse wave velocity (baPWV). Multivariate Logistic regression showed that SFA, VFA, FT3, baPWV, and ABI were independent risk factors for the depleted insulin secretion function. Nomogram models were constructed based on the above risk factors. Among them, the model comprising VFA, FT3, ABI, and baPWV showed the best predictive performance with a C-index of 0.81.Conclusion·SFA and VFA are lower in patients with depleted first-phase insulin secretion function of pancreatic β-cells. The nomogram model, including SFA or VFA, can be used to predict first-phase insulin secretion function of pancreatic β-cells in patients with type 2 diabetes

TwinMARM: Two-Stage Multiscale Adaptive Regression Methods for Twin Neuroimaging Data

Twin imaging studies have been valuable for understanding the relative contribution of the environment and genes on brain structures and their functions. Conventional analyses of twin imaging data include three sequential steps: spatially smoothing imaging data, independently fitting a structural equation model at each voxel, and finally correcting for multiple comparisons. However, conventional analyses are limited due to the same amount of smoothing throughout the whole image, the arbitrary choice of smoothing extent, and the decreased power in detecting environmental and genetic effects introduced by smoothing raw images. The goal of this article is to develop a two-stage multiscale adaptive regression method (TwinMARM) for spatial and adaptive analysis of twin neuroimaging and behavioral data. The first stage is to establish the relationship between twin imaging data and a set of covariates of interest, such as age and gender. The second stage is to disentangle the environmental and genetic influences on brain structures and their functions. In each stage, TwinMARM employs hierarchically nested spheres with increasing radii at each location and then captures spatial dependence among imaging observations via consecutively connected spheres across all voxels. Simulation studies show that our TwinMARM significantly outperforms conventional analyses of twin imaging data. Finally, we use our method to detect statistically significant effects of genetic and environmental variations on white matter structures in a neonatal twin study

Rheumatoid arthritis and the risk of chronic kidney diseases: a Mendelian randomization study

BackgroundThe extra-articular lesions of rheumatoid arthritis (RA) are reported to involve multiple organs and systems throughout the body, including the heart, kidneys, liver, and lungs. This study assessed the potential causal relationship between RA and the risk of chronic kidney diseases (CKDs) using the Mendelian randomization (MR) analysis.MethodIndependent genetic instruments related to RA and CKD or CKD subtypes at the genome-wide significant level were chosen from the publicly shared summary-level data of genome-wide association studies (GWAS). Then, we obtained some single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs), which are associated with RA in individuals of European origin, and had genome-wide statistical significance (p5 × 10−8). The inverse-variance weighted (IVW) method was the main analysis method in MR analysis. The other methods, such as weighted median, MR–Egger, simple mode, and weighted mode were used as supplementary sensitivity analyses. Furthermore, the levels of pleiotropy and heterogeneity were assessed using Cochran’s Q test and leave-one-out analysis. Furthermore, the relevant datasets were obtained from the Open GWAS database.ResultsUsing the IVW method, the main method in MR analysis, the results showed that genetically determined RA was associated with higher risks of CKD [odds ratio (OR): 1.22, 95% confidence interval (CI) 1.13–1.31; p < 0.001], glomerulonephritis (OR: 1.23, 95% CI 1.15–1.31; p < 0.000), amyloidosis (OR = 1.43, 95% CI 1.10–1.88, p < 0.001), and renal failure (OR = 1.18, 95% CI 1.00–1.38, p < 0.001). Then, using multiple MR methods, it was confirmed that the associations persisted in sensitivity analyses, and no pleiotropy was detected.ConclusionThe findings revealed a causal relationship between RA and CKD, including glomerulonephritis, amyloidosis, and renal failure. Therefore, RA patients should pay more attention to monitoring their kidney function, thus providing the opportunity for earlier intervention and lower the risk of progression to CKDs

An Improvement of Shotgun Proteomics Analysis by Adding Next-Generation Sequencing Transcriptome Data in Orange

BACKGROUND: Shotgun proteomics data analysis usually relies on database search. Because commonly employed protein sequence databases of most species do not contain sufficient protein information, the application of shotgun proteomics to the research of protein sequence profile remains a big challenge, especially to the species whose genome has not been sequenced yet. METHODOLOGY/PRINCIPAL FINDINGS: In this paper, we present a workflow with integrated database to partly address this problem. First, we downloaded the homologous species database. Next, we identified the transcriptome of the sample, created a protein sequence database based on the transcriptome data, and integtrated it with homologous species database. Lastly, we developed a workflow for identifying peptides simultaneously from shotgun proteomics data. CONCLUSIONS/SIGNIFICANCE: We used datasets from orange leaves samples to demonstrate our workflow. The results showed that the integrated database had great advantage on orange shotgun proteomics data analysis compared to the homologous species database, an 18.5% increase in number of proteins identification