Neighborhood-Regularized Self-Training for Learning with Few Labels

Training deep neural networks (DNNs) with limited supervision has been a popular research topic as it can significantly alleviate the annotation burden. Self-training has been successfully applied in semi-supervised learning tasks, but one drawback of self-training is that it is vulnerable to the label noise from incorrect pseudo labels. Inspired by the fact that samples with similar labels tend to share similar representations, we develop a neighborhood-based sample selection approach to tackle the issue of noisy pseudo labels. We further stabilize self-training via aggregating the predictions from different rounds during sample selection. Experiments on eight tasks show that our proposed method outperforms the strongest self-training baseline with 1.83% and 2.51% performance gain for text and graph datasets on average. Our further analysis demonstrates that our proposed data selection strategy reduces the noise of pseudo labels by 36.8% and saves 57.3% of the time when compared with the best baseline. Our code and appendices will be uploaded to https://github.com/ritaranx/NeST.Comment: Accepted to AAAI 202

Development of a high-density CHO-C system enables rapid protein production in 10 days

Please click Additional Files below to see the full abstract

EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records

Large language models (LLMs) have demonstrated exceptional capabilities in planning and tool utilization as autonomous agents, but few have been developed for medical problem-solving. We propose EHRAgent, an LLM agent empowered with a code interface, to autonomously generate and execute code for multi-tabular reasoning within electronic health records (EHRs). First, we formulate an EHR question-answering task into a tool-use planning process, efficiently decomposing a complicated task into a sequence of manageable actions. By integrating interactive coding and execution feedback, EHRAgent learns from error messages and improves the originally generated code through iterations. Furthermore, we enhance the LLM agent by incorporating long-term memory, which allows EHRAgent to effectively select and build upon the most relevant successful cases from past experiences. Experiments on three real-world multi-tabular EHR datasets show that EHRAgent outperforms the strongest baseline by up to 29.6% in success rate. EHRAgent leverages the emerging few-shot learning capabilities of LLMs, enabling autonomous code generation and execution to tackle complex clinical tasks with minimal demonstrations.Comment: Work in Progres

Relationships between Parent-Reported Parenting, Child-Perceived Parenting, and Children’s Mental Health in Taiwanese Children

The current study examines the relationship between parents’ and children’s reports of parenting and their effects on children’s mental health symptoms. Six hundred and sixty-six parent-child dyads in Taiwan participated in this study. The parents and the children filled out the parenting questionnaires, and the children also reported their general mental health. The results demonstrated that parental-reported and child-perceived parenting were positively correlated, but parents tended to report lower scores on authoritarian parenting and higher scores on Chinese parenting than did their children. There were also significant gender differences: The mothers reported higher authoritative parenting than did the fathers; and the boys perceived higher authoritarian and Chinese-culture specific parenting than did the girls. Moreover, the Chinese parenting had a negative effect on children’s mental health outcomes. Finally, our results showed that children’s perception of parenting had a stronger effect on children’s mental health symptoms than did parental reports on parenting, urging future research to include the children’s report when investigating the effects of parenting on children’s mental health outcomes

Efficacy, safety and immunogenicity of a human rotavirus vaccine (RIX4414) in Hong Kong children up to three years of age: A randomized, controlled trial

AbstractBackgroundA phase III, double-blind, randomized, controlled trial was conducted in Hong Kong to evaluate the efficacy, safety and immunogenicity of a human rotavirus vaccine, RIX4414 (Rotarix™) against severe rotavirus gastroenteritis in children up to three years of age.MethodsHealthy infants aged 6–12 weeks were enrolled between 08-December-2003 and 31-August-2005 and received two oral doses of either RIX4414 vaccine (N=1513) or placebo (N=1512) given 2 months apart. Vaccine efficacy was assessed from two weeks post-Dose 2 until the children were two and three years of age. Anti-rotavirus IgA seroconversion rate was calculated pre-vaccination and 1–2 months post-Dose 2 using ELISA (cut-off=20U/mL) for 100 infants. Safety was assessed until the children were two years of age; serious adverse events (SAEs) were recorded throughout the study period.ResultsIn children aged two and three years of life, vaccine efficacy against severe rotavirus gastroenteritis was 95.6% (95% CI: 73.1%–99.9%) and 96.1% (95% CI: 76.5%–99.9%), respectively. The seroconversion rate 1–2 months after the second dose of RIX4414 was 97.5% (95% CI: 86.8%–99.9%). At least one SAE was recorded in 439 and 477 infants who were administered RIX4414 and placebo, respectively (p-value=0.130). Six intussusception cases were reported (RIX4414=4; placebo=2) and none was assessed to be vaccine-related.ConclusionRIX4414 was efficacious, immunogenic and safe in the prevention of rotavirus gastroenteritis for at least two years post-vaccination in Hong Kong children

NF-κB: a new player in angiostatic therapy

Angiogenesis is considered a promising target in the treatment of cancer. Most of the angiogenesis inhibitors in late-stage clinical testing or approved for the treatment of cancer act indirectly on endothelial cells. They either neutralize angiogenic growth factors from the circulation or block the signaling pathways activated by these growth factors. Another group of angiogenesis inhibitors are the direct angiostatic compounds. These agents have a direct effect on the endothelium, affecting cellular regulatory pathways, independently of the tumor cells. The reason that this category of agents is lagging behind regarding their translation to the clinic may be the lack of sufficient knowledge on the mechanism of action of these compounds. The transcription factor NF-κB has been recently connected with multiple aspects of angiogenesis. In addition, several recent studies report that angiogenesis inhibition is associated to NF-κB activation. This is of special interest since in tumor cells NF-κB activation has been associated to inhibition of apoptosis and currently novel treatment strategies are being developed based on inhibition of NF-κB. The paradigm that systemic NF-κB inhibition can serve as an anti-cancer strategy, therefore, might need to be re-evaluated. Based on recent data, it might be speculated that NF-κB activation, when performed specifically in endothelial cells, could be an efficient strategy for the treatment of cancer