Charles River Laboratories

About: Charles River Laboratories is a company organization based out in Leiden, Netherlands. It is known for research contribution in the topics: Population & Toxicity. The organization has 1911 authors who have published 2270 publications receiving 50091 citations. The organization is also known as: Charles River Laboratories, Inc. & Charles River.

Fake News Detection on Social Media: A Data Mining Perspective

Abstract: Social media for news consumption is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information lead people to seek out and consume news from social media. On the other hand, it enables the wide spread of \fake news", i.e., low quality news with intentionally false information. The extensive spread of fake news has the potential for extremely negative impacts on individuals and society. Therefore, fake news detection on social media has recently become an emerging research that is attracting tremendous attention. Fake news detection on social media presents unique characteristics and challenges that make existing detection algorithms from traditional news media ine ective or not applicable. First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination. Second, exploiting this auxiliary information is challenging in and of itself as users' social engagements with fake news produce data that is big, incomplete, unstructured, and noisy. Because the issue of fake news detection on social media is both challenging and relevant, we conducted this survey to further facilitate research on the problem. In this survey, we present a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets. We also discuss related research areas, open problems, and future research directions for fake news detection on social media.

Intravascular AAV9 preferentially targets neonatal neurons and adult astrocytes

Abstract: Delivery of genes to the brain and spinal cord across the blood-brain barrier (BBB) has not yet been achieved. Here we show that adeno-associated virus (AAV) 9 injected intravenously bypasses the BBB and efficiently targets cells of the central nervous system (CNS). Injection of AAV9-GFP into neonatal mice through the facial vein results in extensive transduction of dorsal root ganglia and motor neurons throughout the spinal cord and widespread transduction of neurons throughout the brain, including the neocortex, hippocampus and cerebellum. In adult mice, tail vein injection of AAV9-GFP leads to robust transduction of astrocytes throughout the entire CNS, with limited neuronal transduction. This approach may enable the development of gene therapies for a range of neurodegenerative diseases, such as spinal muscular atrophy, through targeting of motor neurons, and amyotrophic lateral sclerosis, through targeting of astrocytes. It may also be useful for rapid postnatal genetic manipulations in basic neuroscience studies.

Site-specific conjugation of a cytotoxic drug to an antibody improves the therapeutic index

Abstract: Antibody-drug conjugates enhance the antitumor effects of antibodies and reduce adverse systemic effects of potent cytotoxic drugs. However, conventional drug conjugation strategies yield heterogenous conjugates with relatively narrow therapeutic index (maximum tolerated dose/curative dose). Using leads from our previously described phage display-based method to predict suitable conjugation sites, we engineered cysteine substitutions at positions on light and heavy chains that provide reactive thiol groups and do not perturb immunoglobulin folding and assembly, or alter antigen binding. When conjugated to monomethyl auristatin E, an antibody against the ovarian cancer antigen MUC16 is as efficacious as a conventional conjugate in mouse xenograft models. Moreover, it is tolerated at higher doses in rats and cynomolgus monkeys than the same conjugate prepared by conventional approaches. The favorable in vivo properties of the near-homogenous composition of this conjugate suggest that our strategy offers a general approach to retaining the antitumor efficacy of antibody-drug conjugates, while minimizing their systemic toxicity.

Fake News Detection on Social Media: A Data Mining Perspective

Abstract: Social media for news consumption is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information lead people to seek out and consume news from social media. On the other hand, it enables the wide spread of "fake news", i.e., low quality news with intentionally false information. The extensive spread of fake news has the potential for extremely negative impacts on individuals and society. Therefore, fake news detection on social media has recently become an emerging research that is attracting tremendous attention. Fake news detection on social media presents unique characteristics and challenges that make existing detection algorithms from traditional news media ineffective or not applicable. First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination. Second, exploiting this auxiliary information is challenging in and of itself as users' social engagements with fake news produce data that is big, incomplete, unstructured, and noisy. Because the issue of fake news detection on social media is both challenging and relevant, we conducted this survey to further facilitate research on the problem. In this survey, we present a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets. We also discuss related research areas, open problems, and future research directions for fake news detection on social media.

Marked differences in differentiation propensity among human embryonic stem cell lines

Abstract: The differentiation potential of 17 human embryonic stem (hES) cell lines was compared. Some lines exhibit a marked propensity to differentiate into specific lineages, often with >100-fold differences in lineage-specific gene expression. For example, HUES 8 is best for pancreatic differentiation and HUES 3 for cardiomyocyte generation. These non-trivial differences in developmental potential among hES cell lines point to the importance of screening and deriving lines for lineage-specific differentiation.