Effects of dietary supplementation of postbiotic derived from Bacillus subtilis ACCC 11025 on growth performance, meat yield, meat quality, excreta bacteria, and excreta ammonia emission of broiler chicks.

The primary aim of this study was to explore the impact of dietary supplementation with a postbiotic derived from Bacillus subtilis ACCC 11025 on growth performance, meat yield, meat quality, excreta bacterial populations, and excreta ammonia emissions of broiler chicks. A total of 480 day-old Arbor Acre broiler chicks, initially weighing 52.83 ± 1.38 g, were randomly allocated into 4 distinct groups. Each group was housed in 6 separate cages, each containing 20 birds. The experimental phase spanned 42 d, divided into 2 periods (d 1-21 and d 22-42). Dietary interventions were based on a basal diet, with postbiotic supplementation at levels of 0.000, 0.015, 0.030, or 0.045%. Our findings indicate that dietary supplementation with postbiotic had a positive influence on body weight gain (BWG) and feed efficiency. The most substantial improvements in BWG and feed efficiency were observed in the group of broiler chicks fed a diet containing 0.015% postbiotic. Furthermore, the inclusion of postbiotic in the diet led to an increase in the yield of breast and leg muscles, with a significant difference in meat yields observed between the control group and the group receiving 0.015% postbiotic supplementation. It's noteworthy that dietary manipulation did not exert any discernible impact on the quality of breast and leg muscle samples. Concurrently, we observed an elevation in serum albumin and total protein contents corresponding to the increasing postbiotic dosage in the diet. Additionally, dietary supplementation with postbiotic effectively controlled the emission of ammonia from excreta and reduced the abundance of Salmonella in excreta while enhancing the presence of Lactobacillus bacteria. The group receiving 0.015% postbiotic supplementation displayed the lowest levels of ammonia emission and the highest counts of Lactobacillus bacteria in excreta. In light of these results, we conclude that dietary supplementation with 0.015% postbiotic represents an efficacious strategy for increasing BWG and meat yield of broiler chicks by enhancing feed efficiency as well as mitigating ammonia emissions from excreta by modulating the composition of excreta bacterial communities.

DeepQuality improves infant retinopathy screening.

Image quality variation is a prominent cause of performance degradation for intelligent disease diagnostic models in clinical applications. Image quality issues are particularly prominent in infantile fundus photography due to poor patient cooperation, which poses a high risk of misdiagnosis. Here, we developed a deep learning-based image quality assessment and enhancement system (DeepQuality) for infantile fundus images to improve infant retinopathy screening. DeepQuality can accurately detect various quality defects concerning integrity, illumination, and clarity with area under the curve (AUC) values ranging from 0.933 to 0.995. It can also comprehensively score the overall quality of each fundus photograph. By analyzing 2,015,758 infantile fundus photographs from real-world settings using DeepQuality, we found that 58.3% of them had varying degrees of quality defects, and large variations were observed among different regions and categories of hospitals. Additionally, DeepQuality provides quality enhancement based on the results of quality assessment. After quality enhancement, the performance of retinopathy of prematurity (ROP) diagnosis of clinicians was significantly improved. Moreover, the integration of DeepQuality and AI diagnostic models can effectively improve the model performance for detecting ROP. This study may be an important reference for the future development of other image-based intelligent disease screening systems.

A Linguistic Analysis of News Coverage of E-Healthcare in China with a Heterogeneous Graphical Model.

E-healthcare has been envisaged as a major component of the infrastructure of modern healthcare, and has been developing rapidly in China. For healthcare, news media can play an important role in raising public interest and utilization of a particular service and complicating (and, perhaps clouding) debate on public health policy issues. We conducted a linguistic analysis of news reports from January 2015 to June 2021 related to E-healthcare in mainland China, using a heterogeneous graphical modeling approach. This approach can simultaneously cluster the datasets and estimate the conditional dependence relationships of keywords. It was found that there were eight phases of media coverage. The focuses and main topics of media coverage were extracted based on the network hub and module detection. The temporal patterns of media reports were found to be mostly consistent with the policy trend. Specifically, in the policy embryonic period (2015-2016), two phases were obtained, industry management was the main topic, and policy and regulation were the focuses of media coverage. In the policy development period (2017-2019), four phases were discovered. All the four main topics, namely industry development, health care, financial market, and industry management, were present. In 2017 Q3-2017 Q4, the major focuses of media coverage included social security, healthcare and reform, and others. In 2018 Q1, industry regulation and finance became the focuses. In the policy outbreak period (2020-), two phases were discovered. Financial market and industry management were the main topics. Medical insurance and healthcare for the elderly became the focuses. This analysis can offer insights into how the media responds to public policy for E-healthcare, which can be valuable for the government, public health practitioners, health care industry investors, and others.

Click chemistry-induced modification of acrylated cellulose nanocrystals for application in PVA-based nanocomposites.

The surface functionalization of cellulose nanocrystals (CNC) is crucial for promoting their diverse applications, especially regarding their use as sustainable biobased polymer reinforcements. In this study, we develop poly (vinyl alcohol) (PVA)-CNC composites with improved tensile strength and gas-barrier performance using CNC-based nanofillers. Acrylated CNCs (ACNCs) were prepared from cellulose via one-pot acid hydrolysis/Fischer esterification; subsequently, surface modification was performed through a thiol-ene reaction to obtain surface-thiolated ACNCs, namely, DACNC, MACNC, and PACNC. The various functional groups on the surface-thiolated ACNCs not only affect the dispersion stability but also alter their interfacial interactions with the PVA matrix, thus realizing the PVA nanocomposites with tailored properties, including the thermal properties, mechanical properties, and gas barrier performance. This study demonstrates that surface-thiolated ACNCs with appropriate surface chemistry and loading levels can serve as excellent nanofillers for PVA, forming biobased composites with desired properties.

Regression-based heterogeneity analysis to identify overlapping subgroup structure in high-dimensional data.

Heterogeneity is a hallmark of complex diseases. Regression-based heterogeneity analysis, which is directly concerned with outcome-feature relationships, has led to a deeper understanding of disease biology. Such an analysis identifies the underlying subgroup structure and estimates the subgroup-specific regression coefficients. However, most of the existing regression-based heterogeneity analyses can only address disjoint subgroups; that is, each sample is assigned to only one subgroup. In reality, some samples have multiple labels, for example, many genes have several biological functions, and some cells of pure cell types transition into other types over time, which suggest that their outcome-feature relationships (regression coefficients) can be a mixture of relationships in more than one subgroups, and as a result, the disjoint subgrouping results can be unsatisfactory. To this end, we develop a novel approach to regression-based heterogeneity analysis, which takes into account possible overlaps between subgroups and high data dimensions. A subgroup membership vector is introduced for each sample, which is combined with a loss function. Considering the lack of information arising from small sample sizes, an l2$l_2$ norm penalty is developed for each membership vector to encourage similarity in its elements. A sparse penalization is also applied for regularized estimation and feature selection. Extensive simulations demonstrate its superiority over direct competitors. The analysis of Cancer Cell Line Encyclopedia data and lung cancer data from The Cancer Genome Atlas show that the proposed approach can identify an overlapping subgroup structure with favorable performance in prediction and stability.

Robust structured heterogeneity analysis approach for high-dimensional data.

Revealing relationships between genes and disease phenotypes is a critical problem in biomedical studies. This problem has been challenged by the heterogeneity of diseases. Patients of a perceived same disease may form multiple subgroups, and different subgroups have distinct sets of important genes. It is hence imperative to discover the latent subgroups and reveal the subgroup-specific important genes. Some heterogeneity analysis methods have been proposed in the recent literature. Despite considerable successes, most of the existing studies are still limited as they cannot accommodate data contamination and ignore the interconnections among genes. Aiming at these shortages, we develop a robust structured heterogeneity analysis approach to identify subgroups, select important genes as well as estimate their effects on the phenotype of interest. Possible data contamination is accommodated by employing the Huber loss function. A sparse overlapping group lasso penalty is imposed to conduct regularization estimation and gene identification, while taking into account the possibly overlapping cluster structure of genes. This approach takes an iterative strategy in the similar spirit of K-means clustering. Simulations demonstrate that the proposed approach outperforms alternatives in revealing the heterogeneity and selecting important genes for each subgroup. The analysis of Cancer Cell Line Encyclopedia data leads to biologically meaningful findings with improved prediction and grouping stability.

Effects of dietary supplementation probiotic complex on growth performance, blood parameters, fecal harmful gas, and fecal microbiota in AA+ male broilers.

In this study, Bacillus subtilis, Clostridium butyricum and Enterococcus faecalis were made into a probiotic complex (PC). The PC was supplemented in AA+ male broilers' diets to investigate the effects of PC on broiler growth performance, carcass traits, blood indicators, harmful gas emissions in feces and microbiota. Three hundred and sixty 1-day-old AA+ male broilers with an average initial body weight (data) were randomly divided into 3 dietary treatments of 6 replicates each, with 20 birds per replicate. The control group (T0) was fed a basal diet, while the test groups (T1 and T2) were supplemented with 0.025 and 0.05% PC in the basal diet, respectively. The trail was 42 days. The results showed that the supplementation of 0.05% PC significantly (p < 0.05) improved average daily gain (ADG) and average daily feed intake (ADFI) of broilers from 22 to 42 days and 1-42 days. Compared to the control group, the breast rate was significantly higher in T2, and the thymic index was significantly higher than that in T1 treatment (p < 0.05). The addition of PC had no significant effects on antibody potency in broiler serum (p > 0.05), but significantly increased albumin and total protein content in serum (p < 0.05). The addition of PC reduced H2S and NH3 emissions in the feces; the levels of Escherichia coli and Salmonella in the feces were significantly reduced and the levels of Lactobacillus were increased. And the most significant results were achieved when PC was added at 0.05%. Correlation analysis showed a significant positive correlation (p < 0.05) between the levels of E. coli and Salmonella and the emissions of H2S and NH3. Conclusion: Dietary supplementation with a 0.05% probiotic complex could improve the growth performance of broilers and also reduced fecal H2S and NH3 emissions, as well as fecal levels of E. coli and Salmonella, and increased levels of Lactobacillus. Thus, PC made by Bacillus subtilis, Clostridium butyricum and Enterococcus faecalis is expected to be an alternative to antibiotics. And based on the results of this trial, the recommended dose for use in on-farm production was 0.05%.

Integrating approximate single factor graphical models.

In the analysis of complex and high-dimensional data, graphical models have been commonly adopted to describe associations among variables. When common factors exist which make the associations dense, the single factor graphical model has been proposed, which first extracts the common factor and then conducts graphical modeling. Under other simpler contexts, it has been recognized that results generated from analyzing a single dataset are often unsatisfactory, and integrating multiple datasets can effectively improve variable selection and estimation. In graphical modeling, the increased number of parameters makes the "lack of information" problem more severe. In this article, we integrate multiple datasets and conduct the approximate single factor graphical model analysis. A novel penalization approach is developed for the identification and estimation of important loadings and edges. An effective computational algorithm is developed. A wide spectrum of simulations and the analysis of breast cancer gene expression datasets demonstrate the competitive performance of the proposed approach. Overall, this study provides an effective new venue for taking advantage of multiple datasets and improving graphical model analysis.

Anti-müllerian Hormone for the Prediction of Ovarian Response in Progestin-Primed Ovarian Stimulation Protocol for IVF.

Background: The ability of anti-Müllerian hormone (AMH) to predict ovarian response has been studied extensively in gonadotropin-releasing hormone agonist and antagonist treatments, but no information is available regarding its value in progestin-primed ovarian stimulation (PPOS) protocol. Methods: This retrospective data analysis included 523 patients without polycystic ovary syndrome who underwent their first in vitro fertilization/intracytoplasmic sperm injection cycle with PPOS protocol at our center between Jan. 2015 and Jul. 2018. Serum AMH measurements were acquired within 12 months prior to ovarian stimulation using the automated Access AMH assay. Results: AMH exhibited a significantly positive correlation with the number of retrieved oocytes (r = 0.744, P < 0.001). For the prediction of poor (<4 oocytes) and high (>15 oocytes) response, AMH had an area under the receiver operating characteristic curve (AUC) of 0.861 and 0.773, corresponding with an optimal cutoff point of 1.26 and 4.34 ng/mL, respectively. When stratified according to the dose of medroxyprogesterone acetate (MPA) (4 mg vs. 10 mg per day), AMH retained its similarly high predictive value for poor (AUC = 0.829 and 0.886, respectively) and high response (AUC = 0.770 and 0.814, respectively) in both groups. Amongst the 314 women who received their first frozen embryo transfer (FET) following PPOS protocol, no significant differences were observed on the rates of biochemical pregnancy, clinical pregnancy, implantation, early miscarriage, multiple pregnancy and ectopic pregnancy (all P > 0.05) across AMH quartiles (≤1.43, 1.44-2.55, 2.56-4.35, >4.35 ng/mL). In a multivariable logistic regression model, age was suggested to be the only independent risk factor for clinical pregnancy (P = 0.011). Conclusions: Our data demonstrated that AMH is an adequate predictor of both high and poor ovarian response in PPOS protocol regardless of MPA dose, but it does not associate with pregnancy outcomes in the first FET cycles in a freeze-all strategy.

Assisted graphical model for gene expression data analysis.

The analysis of gene expression data has been playing a pivotal role in recent biomedical research. For gene expression data, network analysis has been shown to be more informative and powerful than individual-gene and geneset-based analysis. Despite promising successes, with the high dimensionality of gene expression data and often low sample sizes, network construction with gene expression data is still often challenged. In recent studies, a prominent trend is to conduct multidimensional profiling, under which data are collected on gene expressions as well as their regulators (copy number variations, methylation, microRNAs, SNPs, etc). With the regulation relationship, regulators contain information on gene expressions and can potentially assist in estimating their characteristics. In this study, we develop an assisted graphical model (AGM) approach, which can effectively use information in regulators to improve the estimation of gene expression graphical structure. The proposed approach has an intuitive formulation and can adaptively accommodate different regulator scenarios. Its consistency properties are rigorously established. Extensive simulations and the analysis of a breast cancer gene expression data set demonstrate the practical effectiveness of the AGM.

Integrative sparse principal component analysis of gene expression data.

In the analysis of gene expression data, dimension reduction techniques have been extensively adopted. The most popular one is perhaps the PCA (principal component analysis). To generate more reliable and more interpretable results, the SPCA (sparse PCA) technique has been developed. With the "small sample size, high dimensionality" characteristic of gene expression data, the analysis results generated from a single dataset are often unsatisfactory. Under contexts other than dimension reduction, integrative analysis techniques, which jointly analyze the raw data of multiple independent datasets, have been developed and shown to outperform "classic" meta-analysis and other multidatasets techniques and single-dataset analysis. In this study, we conduct integrative analysis by developing the iSPCA (integrative SPCA) method. iSPCA achieves the selection and estimation of sparse loadings using a group penalty. To take advantage of the similarity across datasets and generate more accurate results, we further impose contrasted penalties. Different penalties are proposed to accommodate different data conditions. Extensive simulations show that iSPCA outperforms the alternatives under a wide spectrum of settings. The analysis of breast cancer and pancreatic cancer data further shows iSPCA's satisfactory performance.

Promoting structural effects of covariates in the cure rate model with penalization.

Cure rate models have been widely adopted for characterizing survival data that have long-term survivors. Under a mixture cure rate model where the population is a mixture of cured and susceptible subjects, a primary goal is to study covariate effects on the cure probability and survival function of the susceptible subjects. In this article, we propose a penalization method for estimating the mixture cure rate model where we explicitly consider the structural effects of covariates. The proposed method is more informative than the standard estimations and more flexible than the existing works on structural effects. Depending on data characteristics, we develop different penalties and corresponding computational algorithms. Simulation shows that the proposed method outperforms the alternatives by more accurately estimating parameters and identifying relevant variables. Two breast cancer datasets, one with low-dimensional clinical variables and the other with high-dimensional genetic variables, are analyzed.

Single-frequency ytterbium doped photonic bandgap fiber amplifier at 1178 nm.

1178 nm single-frequency amplification by Yb doped photonic bandgap fiber has been demonstrated. 24.6 W output power and 12 dB gain were obtained without parasitic lasing and also stimulated Brillouin scattering. 1.8 dB suppression of Brillouin gain by an acoustic antiguiding effect has been found in the Yb doped photonic bandgap fiber.

High power Yb-doped photonic bandgap fiber oscillator at 1178 nm.

An ytterbium-doped solid-core photonic bandgap fiber oscillator in an all-fiber format is investigated for high power at an extreme long wavelength. The photonic bandgap fiber is spliced with two fiber Bragg gratings to compose the cavity. The sharp-cut bandpass distributed filtering effect of the photonic bandgap fibers efficiently suppresses amplified spontaneous emission in the conventional high-gain region. Fine adjustment of the short cut-off wavelength by coiling with tighter diameter is performed to suppress parasitic lasing. A record output power of 53.6 W with a slope efficiency of 53% at 1178 nm was demonstrated.