The importance of the gut microbiota (GM) of animals is widely acknowledged because of its pivotal roles in metabolism, immunity, and health maintenance. The level of health can be reflected by the dynamic distribution of GM. In this study, high-throughput sequencing of the bacterial 16S rRNA gene was used to compare the microbial populations from feces in healthy and diarrheic kids, which reflected the dynamic shift of microbiota in kids and investigated differences from adult healthy goats. Healthy kids and goats not only displayed higher species richness but also exhibited higher bacterial diversity than diarrheic kids based on the results of the operational taxonomic unit analysis, alpha diversity, and beta diversity. Firmicutes and Bacteroidetes were the most dominant phyla in all samples. At the genus level, the differences in diversity and abundance between diarrheic kids and the other two groups were gradually observed. In the diarrheic kid intestine, Bacteroides remained the dominant species, and the proportion of Clostridium_sensu_stricto_1 and Paeniclostridium increased, whereas Rikenellaceae_RC9_gut_group, Ruminococcaceae_UCG-005, and Christensenellaceae_R-7_group were significantly reduced. The results showed the differences of GM in diarrheic kids and healthy kids were significant while in kids and goats were not obvious. Differences in the composition of intestinal microbiota may not be the cause of diarrhea, and some changes of bacterial richness may guide our interpretation of diarrhea. This study is the first to investigate the distribution of GM in Boer goats with different ages and health states. Furthermore, this study will provide a theoretical basis for the establishment of a prevention and treatment system for goat diarrhea.

Earth’s habitability is closely tied to its late-stage accretion, during which impactors delivered the majority of life-essential volatiles. However, the nature of these final building blocks remains poorly constrained. Nickel (Ni) can be a useful tracer in characterizing this accretion as most Ni in the bulk silicate Earth (BSE) comes from the late-stage impactors. Here, we apply Ni stable isotope analysis to a large number of meteorites and terrestrial rocks, and find that the BSE has a lighter Ni isotopic composition compared to chondrites. Using first-principles calculations based on density functional theory, we show that core-mantle differentiation cannot produce the observed light Ni isotopic composition of the BSE. Rather, the sub-chondritic Ni isotopic signature was established during Earth’s late-stage accretion, probably through the Moon-forming giant impact. We propose that a highly reduced sulfide-rich, Mercury-like body, whose mantle is characterized by light Ni isotopic composition, collided with and merged into the proto-Earth during the Moon-forming giant impact, producing the sub-chondritic Ni isotopic signature of the BSE, while delivering sulfur and probably other volatiles to the Earth.

Despite the great advances made in the field of image super-resolution (ISR) during the last years, the performance has merely been evaluated perceptually. Thus, it is still unclear whether ISR is helpful for other vision tasks.In this paper, we present the first comprehensive study and analysis of the usefulness of ISR for other vision applications. In particular, six ISR methods are evaluated on four popular vision tasks, namely edge detection, semantic image segmentation, digit recognition, and scene recognition. We show that applying ISR to input images of other vision systems does improve their performance when the input images are of low-resolution. We also study the correlation between four standard perceptual evaluation criteria (namely PSNR, SSIM, IFC, and NQM) and the usefulness of ISR to the vision tasks. Experiments show that they correlate well with each other in general, but perceptual criteria are still not accurate enough to be used as full proxies for the usefulness. We hope this work will inspire the community to evaluate ISR methods also in real vision applications, and to adopt ISR as a pre-processing step of other vision tasks if the resolution of their input images is low. -PSNR / 26.23 PSNR / 26.60 PSNR / 26.64 PSNR / 26.65 PSNR / 26.71 PSNR / 26.72 PSNR / 26.73 APP / 0.975 APP / 0.902 APP / 0.945 APP / 0.966 APP / 0.948 APP / 0.954 APP / 0.957 APP / 0.955 -PSNR / 24.03 PSNR / 24.55 PSNR / 24.53 PSNR / 24.83 PSNR / 24.78 PSNR / 24.76 PSNR / 24.78

A 24 kDa protein is one of the important components in Haemonchus contortus (barber pole worm) excretory/secretory products (HcESPs), which was shown to have important antigenic function. However, little is known about the immunomodulatory effects of this proteinon host cell. In the present study gene encoding 24kDa excretory/secretory protein (HcES-24) was cloned. The recombinant protein of HcES-24 (rHcES-24) was expressed in a histidine-tagged fusion protein soluble form in Escherichia coli. Binding activity of rHcES-24 to goat PBMCs was confirmed by immunofluorescence assay (IFA) and its immunomudulatory effect on cytokine secretion, cell proliferation, cell migration and nitric oxide production were observed by co-incubation of rHcES-24. IFA results revealed that rHcES-24 could bind to the PBMCs. The interaction of rHcES-24 increased the production of IL4, IL10, IL17 and cell migration in dose dependent manner. However, rHcES-24 treatment significantly suppressed the production of IFNγ, proliferation of the PBMC and Nitric oxide (NO) production. Our findings showed that the rHcES-24 played important regulatory effects on the goat PBMCs.

Background: Preoperative differentiation between malignant and benign tumors is important for treatment decisions. Purpose/Hypothesis: To investigate/validate a radiomics nomogram for preoperative differentiation between malignant and benign masses. Study Type: Retrospective. Population: Imaging data of 91 patients. Field Strength/Sequence: T 1 -weighted images (570 msec repetition time [TR]; 17.9 msec echo time [TE], 200-400 mm field of view [FOV], 208-512 × 208-512 matrix), fat-suppressed fast-spin-echo (FSE) T 2 -weighted images (T 2 WIs) (4331 msec TR; 87.9 msec TE, 200-400 mm FOV, 312 × 312 matrix), slice thickness 4 mm, and slice spacing 1 mm. Assessment: Fat-suppressed FSE T 2 WIs were selected for extraction of features. Radiomics features were extracted from fat-suppressed T 2 WIs. A radiomics signature was generated from the training dataset using least absolute shrinkage and selection operator algorithms. Independent risk factors were identified by multivariate logistic regression analysis and a radiomics nomogram was constructed. Nomogram capability was evaluated in the training dataset and validated in the validation dataset. Performance of the nomogram, radiomics signature, and clinical model were compared. Statistical Tests: 1) Independent t-test or Mann-Whitney U-test: for continuous variables. Fisher's exact test or χ 2 test: comparing categorical variables between two groups. Univariate analysis: evaluating associations between clinical/morphological characteristics and malignancy. 2) Least absolute shrinkage and selection operator (LASSO)-logistic regression model: selection of malignancy features. 3) Significant clinical/morphological characteristics and radiomics signature were input variables for multiple logistic regression analysis. Area under the curve (AUC): evaluation of ability of the nomogram to identify malignancy. Hosmer-Lemeshow test and decision curve: evaluation and validation of nomogram results. Results: The radiomics nomogram was able to differentiate malignancy from benignity in the training and validation datasets with an AUC of 0.94. The nomogram outperformed both the radiomics signature and clinical model alone. Data Conclusion: This radiomics nomogram is a noninvasive, low-cost preoperative prediction method combining the radiomics signature and clinical model. Level of Evidence: 3 Technical Efficacy: Stage 2