Aflatoxicosis is a grave threat to the poultry industry. Dietary supplementation with antioxidants showed a great potential in enhancing the immune system; hence, protecting animals against aflatoxin B1-induced toxicity. Grape seed proanthocyanidin extract (GSPE) one of the most well-known and powerful antioxidants. Therefore, the purpose of this research was to investigate the effectiveness of GSPE in the detoxification of AFB1 in broilers. A total of 300 one-day-old Cobb chicks were randomly allocated into five treatments of six replicates (10 birds per replicate), fed ad libitum for four weeks with the following dietary treatments: 1. Basal diet (control); 2. Basal diet + 1 mg/kg AFB1 contaminated corn (AFB1); 3. Basal diet + GSPE 250 mg/kg; (GSPE 250 mg/kg) 4. Basal diet + AFB1 (1 mg/kg) + GSPE 250 mg/kg; (AFB1 + GSPE 250 mg/kg) 5. Basal diet + AFB1 (1mg/kg) + GSPE 500 mg/kg, (AFB1 + GSPE 500 mg/kg). When compared with the control group, feeding broilers with AFB1 alone significantly reduced growth performance, serum immunoglobulin contents, negatively altered serum biochemical contents, and enzyme activities, and induced histopathological lesion in the liver. In addition, AFB1 significantly increased malondialdehyde content and decreased total superoxide dismutase, catalase, glutathione peroxide, glutathione-S transferase, glutathione reductase activities, and glutathione concentration within the liver and serum. The supplementation of GSPE (250 and 500 mg/kg) to AFB1 contaminated diet reduced AFB1 residue in the liver and significantly mitigated AFB1 negative effects. From these results, it can be concluded that dietary supplementation of GSPE has protective effects against aflatoxicosis caused by AFB1 in broiler chickens.

Categorization of images containing visual objects can be successfully recognized using single-trial electroencephalograph (EEG) measured when subjects view images. Previous studies have shown that task-related information contained in event-related potential (ERP) components could discriminate two or three categories of object images. In this study, we investigated whether four categories of objects (human faces, buildings, cats and cars) could be mutually discriminated using single-trial EEG data. Here, the EEG waveforms acquired while subjects were viewing four categories of object images were segmented into several ERP components (P1, N1, P2a and P2b), and then Fisher linear discriminant analysis (Fisher-LDA) was used to classify EEG features extracted from ERP components. Firstly, we compared the classification results using features from single ERP components, and identified that the N1 component achieved the highest classification accuracies. Secondly, we discriminated four categories of objects using combining features from multiple ERP components, and showed that combination of ERP components improved four-category classification accuracies by utilizing the complementarity of discriminative information in ERP components. These findings confirmed that four categories of object images could be discriminated with single-trial EEG and could direct us to select effective EEG features for classifying visual objects.

The current study was to better understand the potential factors affecting aflatoxin B1 (AFB1) accumulation varies between different grains. The nutrient composition and contents of defatted substrates were determined; additionally, according to the nutrient content of the substrates, the effects of starch, soluble sugars, amino acids, and trace elements on AFB1 production and mycelial growth in Czapek-Dox medium were examined. These results verified that removal of lipids from ground substrates significantly reduced the substrate's potential for AFB1 production by Aspergillus flavus. Maltose, glucose, sucrose, arginine, glutamic acid, aspartic acid, and zinc significantly induced AFB1 production up to 1.7- to 26.6-fold. And stachyose more significantly promoted A. flavus growth than the other nutrients. Thus, this study demonstrated that, combined with the nutrients content of grains, in addition to lipids, sucrose, stachyose, glutamic acid, and zinc might play key roles in various grains that are differentially infected by A. flavus. Particularly, two new nutrients (arginine and stachyose) of the grains we found significantly stimulate AFB1 production and A. flavus growth, respectively. The results provide new concepts for antifungal methods to protect food and animal feed from AFB1 contamination.

For patients with mild cognitive impairment (MCI), the likelihood of progression to probable Alzheimer's disease (AD) is important not only for individual patient care, but also for the identification of participants in clinical trial, so as to provide early interventions. Biomarkers based on various neuroimaging modalities could offer complementary information regarding different aspects of disease progression. The current study adopted a weighted multi-modality sparse representation-based classification method to combine data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, from three imaging modalities: Volumetric magnetic resonance imaging (MRI), fluorodeoxyglucose (FDG) positron emission tomography (PET), and florbetapir PET. We included 117 normal controls (NC) and 110 MCI patients, 27 of whom progressed to AD within 36 months (pMCI), while the remaining 83 remained stable (sMCI) over the same time period. Modality-specific biomarkers were identified to distinguish MCI from NC and to predict pMCI among MCI. These included the hippocampus, amygdala, middle temporal and inferior temporal regions for MRI, the posterior cingulum, precentral, and postcentral regions for FDG-PET, and the hippocampus, amygdala, and putamen for florbetapir PET. Results indicated that FDG-PET may be a more effective modality in discriminating MCI from NC and in predicting pMCI than florbetapir PET and MRI. Combining modality-specific sensitive biomarkers from the three modalities boosted the discrimination accuracy of MCI from NC (76.7%) and the prediction accuracy of pMCI (82.5%) when compared with the best single-modality results (73.6% for MCI and 75.6% for pMCI with FDG-PET).

BackgroundSupport vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional studies which focused either merely on the evaluation of different types of SVM or the voxel selection methods, we aimed to investigate the overall performance of linear and RBF SVM for fMRI classification together with voxel selection schemes on classification accuracy and time-consuming.Methodology/Principal FindingsSix different voxel selection methods were employed to decide which voxels of fMRI data would be included in SVM classifiers with linear and RBF kernels in classifying 4-category objects. Then the overall performances of voxel selection and classification methods were compared. Results showed that: (1) Voxel selection had an important impact on the classification accuracy of the classifiers: in a relative low dimensional feature space, RBF SVM outperformed linear SVM significantly; in a relative high dimensional space, linear SVM performed better than its counterpart; (2) Considering the classification accuracy and time-consuming holistically, linear SVM with relative more voxels as features and RBF SVM with small set of voxels (after PCA) could achieve the better accuracy and cost shorter time.Conclusions/SignificanceThe present work provides the first empirical result of linear and RBF SVM in classification of fMRI data, combined with voxel selection methods. Based on the findings, if only classification accuracy was concerned, RBF SVM with appropriate small voxels and linear SVM with relative more voxels were two suggested solutions; if users concerned more about the computational time, RBF SVM with relative small set of voxels when part of the principal components were kept as features was a better choice.

Host defense peptides (HDPs) are efficient defense components of the innate immune system, playing critical roles in intestinal homeostasis and protection against pathogens. This study aims to investigate the interference effects of DON on the intestinal porcine HDPs expression in piglets and intestinal porcine epithelial cell line (IPEC-J2) cells, and elucidate the underlying mechanisms through which it functions. In an animal experiment, intestinal HDPs were determined in weaned piglets fed control and 1.28 mg/kg or 2.89 mg/kg DON-contaminated diets. Dietary exposure to DON significantly decreased piglet average daily gain, increased intestinal permeability and depressed the expression of porcine β-defensin1 (pBD1), pBD2, pBD3, epididymis protein 2 splicing variant C (pEP2C), PMAP23, and proline/arginine-rich peptide of 39 amino acids (PR39) in the intestine (p < 0.05). In IPEC-J2 cells, DON decreased cell viability and inhibited the expression of pBD1, pBD3, pEP2C, PG1-5, and PR39 (p < 0.05). NOD2, key regulator that is responsible for HDPs production, was markedly downregulated, whereas caspase-12 was activated in the presence of DON. In conclusion, DON induced caspase-12 activation and inhibited the NOD2-mediated HDPs production, which led to an impaired intestinal barrier integrity of weaned piglets. Our study provides a promising target for future therapeutic strategies to prevent the adverse effects of DON.

Structural magnetic resonance imaging (MRI) studies have demonstrated that the brain undergoes age-related neuroanatomical changes not only regionally but also on the network level during the normal development and aging process. In recent years, many studies have focused on estimating age using structural MRI measurements. However, the age prediction effects on different structural networks remain unclear. In this study, we established age prediction models based on common structural networks using convolutional neural networks (CNN) with data from 1,454 healthy subjects aged 18-90 years. First, based on the reference map of CorticalParcellation_Yeo2011, we obtained structural network images for each subject, including images of the following: the frontoparietal network (FPN), the dorsal attention network (DAN), the default mode network (DMN), the somatomotor network (SMN), the ventral attention network (VAN), the visual network (VN), and the limbic network (LN). Then, we built a 3D CNN model for each structural network using a large training dataset (n = 1,303) and the predicted ages of the subjects in the test dataset (n = 151). Finally, we estimated the age prediction performance of CNN compared with Gaussian process regression (GPR) and relevance vector regression (RVR). The results of CNN showed that the FPN, DAN, and DMN exhibited the optimal age prediction accuracies with mean absolute errors (MAEs) of 5.55 years, 5.77 years, and 6.07 years, respectively, and the other four networks, i.e., the SMN, VAN, VN, and LN, tended to have larger MAEs of more than 8 years. With respect to GPR and RVR, the top three prediction accuracies were still from the FPN, DAN, and DMN; moreover, CNN made more precise predictions than GPR and RVR for these three networks. Our findings suggested that CNN has the optimal age prediction performance, and our age prediction model can be potentially used for brain disorder diagnosis according to age prediction differences.

In recent years, increasing attention has been given to the identification of the conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD). Brain neuroimaging techniques have been widely used to support the classification or prediction of MCI. The present study combined magnetic resonance imaging (MRI), 18F-fluorodeoxyglucose PET (FDG-PET), and 18F-florbetapir PET (florbetapir-PET) to discriminate MCI converters (MCI-c, individuals with MCI who convert to AD) from MCI non-converters (MCI-nc, individuals with MCI who have not converted to AD in the follow-up period) based on the partial least squares (PLS) method. Two types of PLS models (informed PLS and agnostic PLS) were built based on 64 MCI-c and 65 MCI-nc from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The results showed that the three-modality informed PLS model achieved better classification accuracy of 81.40%, sensitivity of 79.69%, and specificity of 83.08% compared with the single-modality model, and the three-modality agnostic PLS model also achieved better classification compared with the two-modality model. Moreover, combining the three modalities with clinical test score (ADAS-cog), the agnostic PLS model (independent data: florbetapir-PET; dependent data: FDG-PET and MRI) achieved optimal accuracy of 86.05%, sensitivity of 81.25%, and specificity of 90.77%. In addition, the comparison of PLS, support vector machine (SVM), and random forest (RF) showed greater diagnostic power of PLS. These results suggested that our multimodal PLS model has the potential to discriminate MCI-c from the MCI-nc and may therefore be helpful in the early diagnosis of AD.