An Investigation of Autism Support Groups on Facebook

Autism-affected users, such as autism patients, caregivers, parents, family members, and researchers, currently seek informational support and social support from communities on social media. To reveal the information needs of autism- affected users, this study centers on the research of users’ interactions and information sharing within autism communities on social media. It aims to understand how autism-affected users utilize support groups on Facebook. A systematic method was proposed to aid in the data analysis including social network analysis, topic modeling, sentiment analysis, and inferential analysis. Social network analysis method was adopted to reveal the interaction patterns appearing in the groups, and topic modeling method was employed to uncover the discussion themes that users were concerned with in their daily lives. Sentiment analysis method helped analyze the emotional characteristics of the content that users expressed in the groups. Inferential analysis method was applied to compare the similarities and differences among different autism support groups found on Facebook. This study collected user-generated content from five sampled support groups (an awareness group, a treatment group, a parents group, a research group, and a local support group) on Facebook. Findings show that the discussion topics varied in different groups. Influential users in each Facebook support group were identified through the analysis of the interaction network. The results indicated that the influential users not only attracted more attention from other group members but also led the discussion topics in the group. In addition, it was examined that autism support groups on Facebook offered a supportive emotional atmosphere for group members. The findings of this study revealed the characteristics of user interactions and information exchanges in autism support groups on social media. Theoretically, the findings demonstrated the significance of social media for autism users. The unique implication of this study is to identify support groups on Facebook as a source of informational, social, and emotional support for autism-related users. The methodology applied in this study presented a systematic approach to evaluating the information exchange in health-related support groups on social media. Further, it investigated the potential role of technology in the social lives of autism-related users. The outcomes of this study can contribute to improving online intervention programs by highlighting effective communication approaches

A citation-based review of study on image retrieval

Driven by the development of the information retrieval technologies, image retrieval has been studied for more than several decades. This study centers on revealing the current status and future directions of image retrieval based on reviewing previous related studies. The citation-based analysis was applied to 2243 articles retrieved from Web of Science database. The time series plots of the citation relationships between the retrieved articles reveal a fundamental research article that lay the foundation for the image retrieval field. Co-citation analysis identifies that the existing studies formed two clusters. Each cluster represents one of the two major areas in the field of image retrieval: the text-based image retrieval and the content-based image retrieval. The visualization map shows that the research of content-based image retrieval has received more attention than the area of text-based image retrieval. Relevance feedback was identified as a promising research direction for the future study

Synergistically activated dual-locked fluorescent probes to monitor H₂S-induced DNA damage

Naphthalimide-based fluorescent probes (NAN0-N3 and NAN6-N3) were developed with dual locked fluorescence. Here, ≥1.9 × 10−2 mM of H2S and ≥2.2 × 10−2 μg mL−1 of DNA could unlock a highly sensitive off-on fluorescence response through synergistic changes of the molecular structure and conformation. As such, the probes could monitor DNA damage induced by the overexpression of H2S, and were able to evaluate the degree of apoptosis of living cells mediated by H2S-induced mtDNA or nDNA damage.</p

HSPA12B Promotes Functional Recovery After Ischaemic Stroke Through an eNOS-Dependent Mechanism

Journal of Cellular and Molecular Medicine published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular Medicine. Stroke is the leading cause of disability worldwide. HSPA12B, a heat-shock protein recently identified expression specifically in endothelial cells, is able to promote angiogenesis. Here, we have investigated its effects on functional recovery at chronic phase of ischaemic stroke. Ischaemic stroke was induced by 60 min. of middle cerebral artery occlusion in transgenic mice with overexpression of HSPA12B (HSPA12B Tg) and wild-type littermates (WT). HSPA12B Tg mice demonstrated a significant higher survival rate than WT mice within 28 days post-stroke. Significant improved neurological functions, increased spontaneous locomotor activity and decreased anxiety were detected inHSPA12B Tg mice compared with WT controls within 21 days post-stroke. Stroke-induced hippocampal degeneration was attenuated in HSPA12B Tg mice examined at day 28 post-stroke. Interestingly, HSPA12B Tg mice showed enhanced peri-infarct angiogenesis (examined 28 days post-stroke) and hippocampal neurogenesis (examined 7 days post-stroke), respectively, compared to WT mice. The stroke-induced eNOS phosphorylation and TGF-β1 expression were augmented in HSPA12B Tg mice. However, administration with eNOS inhibitor L-NAME diminished the HSPA12B-induced protection in neurological functional recovery and mice survival post-stroke. The data suggest that HSPA12B promoted functional recovery and survival after stroke in an eNOS-dependent mechanism. Targeting HSPA12B expression may have a therapeutic potential for the stroke-evoked functional disability and mortality

Study on the quantitative assessment of Staphylococcus aureus in the broiler chicken slaughtering line

Objective To analyze the risk and key prevention and control points of Staphylococcus aureus in a large broiler slaughterhouse and to provide guidance for the scientific prevention and control of Staphylococcus aureus contamination in broiler slaughter. Methods Combining the monitoring data and investigation data of Staphylococcus aureus contamination in a large broiler chicken slaughterhouse, a quantitative assessment model was constructed using @ RISK 7 software, and a quantitative assessment was conducted on the four stages of chicken slaughter (depilation, cleaning chamber, pre-cooling and segmentation). Results Our research determined the predictive growth and decline pattern of Staphylococcus aureus in slaughtering process. It showed that the pre-cooling and segmentation and transmission links were the main risk contributor links of Staphylococcus aureus contamination. The critical risk control points of Staphylococcus aureus in broiler slaughtering were the concentration of Staphylococcus aureus in precooled pool water and hand-borne Staphylococcus aureus in workers with the correlation coefficient of 0.62 and 0.50, respectively. Conclusion The identification of key control points and precise control measures of Staphylococcus aureus in broiler slaughtering can effectively guarantee the health and safety of terminal chicken products

Functional Mapping of Dynamic Traits with Robust t-Distribution

Functional mapping has been a powerful tool in mapping quantitative trait loci (QTL) underlying dynamic traits of agricultural or biomedical interest. In functional mapping, multivariate normality is often assumed for the underlying data distribution, partially due to the ease of parameter estimation. The normality assumption however could be easily violated in real applications due to various reasons such as heavy tails or extreme observations. Departure from normality has negative effect on testing power and inference for QTL identification. In this work, we relax the normality assumption and propose a robust multivariate -distribution mapping framework for QTL identification in functional mapping. Simulation studies show increased mapping power and precision with the distribution than that of a normal distribution. The utility of the method is demonstrated through a real data analysis

Heritable Targeted Inactivation of Myostatin Gene in Yellow Catfish (Pelteobagrus fulvidraco) Using Engineered Zinc Finger Nucleases

Yellow catfish (Pelteobagrus fulvidraco) is one of the most important freshwater aquaculture species in China. However, its small size and lower meat yield limit its edible value. Myostatin (MSTN) is a negative regulator of mammalian muscle growth. But, the function of Mstn in fish remains elusive. To explore roles of mstn gene in fish growth and create a strain of yellow catfish with high amount of muscle mass, we performed targeted disruption of mstn in yellow catfish using engineered zinc-finger nucleases (ZFNs). Employing zebrafish embryos as a screening system to identify ZFN activity, we obtained one pair of ZFNs that can edit mstn in yellow catfish genome. Using the ZFNs, we successfully obtained two founders (Founder July29-7 and Founder July29-8) carrying mutated mstn gene in their germ cells. The mutated mstn allele inherited from Founder July29-7 was a null allele (mstnnju6) containing a 4 bp insertion, predicted to encode function null Mstn. The mutated mstn inherited from Founder July29-8 was a complex type of mutation (mstnnju7), predicted to encode a protein lacking two amino acids in the N-terminal secretory signal of Mstn. Totally, we obtained 6 mstnnju6/+ and 14 mstnnju7/+ yellow catfish. To our best knowledge, this is the first endogenous gene knockout in aquaculture fish. Our result will help in understanding the roles of mstn gene in fish

SVSI: Fast and Powerful Set-Valued System Identification Approach to Identifying Rare Variants in Sequencing Studies for Ordered Categorical Traits: SVSIfor Genetic Association Studies

For genetic association studies that involve an ordered categorical phenotype, we usually either regroup multiple categories of the phenotype into two categories (“cases” and “controls”) and then apply the standard logistic regression (LG), or apply ordered logistic (oLG) or ordered probit (oPRB) regression which accounts for the ordinal nature of the phenotype. However, these approaches may lose statistical power or may not control type I error rate due to their model assumption and/or instable parameter estimation algorithm when the genetic variant is rare or sample size is limited. Here to solve this problem, we propose a set-valued (SV) system model, which assumes that an underlying continuous phenotype follows a normal distribution, to identify genetic variants associated with an ordinal categorical phenotype. We couple this model with a set-valued system identification algorithm to identify all the key system parameters. Simulations and two real data analyses show that SV and LG accurately controlled the Type I error rate even at a significance level of 10−6 but not oLG and oPRB in some cases. LG had significantly smaller power than the other three methods due to disregarding of the ordinal nature of the phenotype, and SV had similar or greater power than oLG and oPRB. For instance, in a simulation with data generated from an additive SV model with odds ratio of 7.4 for a phenotype with three categories, a single nucleotide polymorphism with minor allele frequency of 0.75% and sample size of 999 (333 per category), the power of SV, oLG and LG models were 70%, 40% and <1%, respectively, at a significance level of 10−6. Thus, SV should be employed in genetic association studies for ordered categorical phenotype