Understanding influence networks among substance abuse treatment clinics may speed the diffusion of innovations. The purpose of this study was to describe influence networks in Massachusetts, Michigan, New York, Oregon, and Washington and test two expectations, using social network analysis: (1) Social network measures can identify influential clinics; and (2) Within a network, some weakly connected clinics access out-of-network sources of innovative evidence-based practices and can spread these innovations through the network. A survey of 201 clinics in a parent study on quality improvement provided the data. Network measures and sociograms were obtained from adjacency matrixes created by UCINet. We used regression analysis to determine whether network status relates to clinics' adopting innovations. Findings suggest that influential clinics can be identified and that loosely linked clinics were likely to join the study sooner than more influential clinics but were not more likely to have improved outcomes than other organizations. Findings identify the structure of influence networks for SUD treatment organizations and have mixed results on how those structures impacted diffusion of the intervention under study. Further study is necessary to test whether use of knowledge of the network structure will have an effect on the pace and breadth of dissemination of innovations.
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http://dx.doi.org/10.1007/s13142-015-0327-y | DOI Listing |
Eur J Med Chem
January 2025
Department of Respiratory and Critical Care Medicine, Targeted Tracer Research and Development Laboratory, Institute of Respiratory Healthand, Department of Frontiers Science Center for Disease-related Molecular Network, Core Facilities, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China. Electronic address:
NEK2, a serine/threonine protein kinase, is integral to mitotic events such as centrosome duplication and separation, microtubule stabilization, spindle assembly checkpoint, and kinetochore attachment. However, NEK2 overexpression leads to centrosome amplification and chromosomal instability, which are significantly associated with various malignancies, including liver, breast, and non-small cell lung cancer. This overexpression could facilitate tumor development and confer resistance to therapy by promoting aberrant cell division and centrosome amplification.
View Article and Find Full Text PDFJMIR Form Res
January 2025
Smith School of Business, Queen's University, Kingston, ON, Canada.
Background: Depression significantly impacts an individual's thoughts, emotions, behaviors, and moods; this prevalent mental health condition affects millions globally. Traditional approaches to detecting and treating depression rely on questionnaires and personal interviews, which can be time consuming and potentially inefficient. As social media has permanently shifted the pattern of our daily communications, social media postings can offer new perspectives in understanding mental illness in individuals because they provide an unbiased exploration of their language use and behavioral patterns.
View Article and Find Full Text PDFBiol Reprod
January 2025
Inner Mongolia SK·Xing Animal Breeding and Breeding Biotechnology Research Institute Co., Ltd, Hohhot 011517, China.
Economic losses in cattle farms are frequently associated with failed pregnancies. Some studies found that the transcriptomic profiles of blood and endometrial tissues in cattle with varying pregnancy outcomes display discrepancies even before artificial insemination (AI) or embryo transfer (ET). In the study, 330 samples from seven distinct sources and two tissue types were integrated and divided into two groups based on the ability to establish and maintain pregnancy after AI or ET: P (pregnant) and NP (nonpregnant).
View Article and Find Full Text PDFJ Food Sci
January 2025
School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing, China.
Whole-grain foods (WGFs) constitute a large part of humans' daily diet, making risk identification of WGFs important for health and safety. However, existing research on WGFs has paid more attention to revealing the effects of a single hazardous substance or various hazardous substances on food safety, neglecting the mutual influence between individual hazardous substances and between hazardous substances and basic information. Therefore, this paper proposes a causal inference of WGFs' risk based on a generative adversarial network (GAN) and Bayesian network (BN) to explore the mutual influence between hazardous substances and basic information.
View Article and Find Full Text PDFEnviron Sci Pollut Res Int
January 2025
Research Engineer I, Applied Research Center for Environment & Marine Studies, Research Institute, King Fahd University of Petroleum & Minerals, 31261, Dhahran, Saudi Arabia.
Concerns regarding disinfection byproducts (DBPs) in drinking water persist, with measurements in water treatment plants (WTPs) being relatively easier than those in water distribution systems (WDSs) due to accessibility challenges, especially during adverse weather conditions. Machine learning (ML) models offer improved predictions of DBPs in WDSs. This study developed multiple ML models to predict Trihalomethanes (THMs), Haloacetic Acids (HAAs), Dichloroacetonitrile (DCAN), and N-nitrosodimethylamine (NDMA) in WDSs using data collected over 13 years (2008-2020) from 113 water supply systems (WSS) in Ontario.
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