Modern enterprises face a myriad of challenges when it comes to internal data research. Data today is scattered across various sources—spreadsheets, databases, PDFs, and even online platforms—making it difficult to extract coherent insights. Many organizations struggle with disjointed systems where structured SQL queries and unstructured documents do not easily speak the same language. This fragmentation… →
Improving how large language models (LLMs) handle complex reasoning tasks while keeping computational costs low is a challenge. Generating multiple reasoning steps and selecting the best answer increases accuracy, but this process demands a lot of memory and computing power. Dealing with long reasoning chains or huge batches is computationally expensive and slows down models,… →
Acute exercise is suggested to elicit benefits for cool executive function, but the sensitivity of its hot components, such as risky decision making, to exercise remains unclear. However, improvements in risky decision making are relevant due to its predictive value for engagement in unhealthy behaviors in young adults in particular. We investigated the acute effects… →
BACKGROUNDRapid diagnosis to facilitate urgent intervention is critical for treatment of acute spinal cord injury (SCI). We hypothesized that a multi-analyte blood biomarker would support point-of-care SCI diagnosis, correlate with injury severity, and predict long-term neurologic outcomes.METHODSDroplet digital PCR (ddPCR) assays were designed to amplify differentially hypomethylated genomic loci in spinal cord tissue. An optimized… →
CrewAI is an open-source framework for orchestrating autonomous AI agents in a team. It allows you to create an AI “crew” where each agent has a specific role and goal and works together to accomplish complex tasks. In a CrewAI system, multiple agents can collaborate, share information, and coordinate their actions toward a common objective.… →
Encoder models like BERT and RoBERTa have long been cornerstones of natural language processing (NLP), powering tasks such as text classification, retrieval, and toxicity detection. However, while decoder-based large language models (LLMs) like GPT and LLaMA have evolved rapidly—incorporating architectural innovations, larger datasets, and extended context windows—encoders have stagnated. Despite their critical role in embedding-dependent… →
LLMs face challenges in continual learning due to the limitations of parametric knowledge retention, leading to the widespread adoption of RAG as a solution. RAG enables models to access new information without modifying their internal parameters, making it a practical approach for real-time adaptation. However, traditional RAG frameworks rely heavily on vector retrieval, which limits… →
In March 2024, ponatinib received accelerated FDA approval for the treatment of newly diagnosed Philadelphia chromosome-positive acute lymphoblastic leukemia (Ph + ALL) in combination with chemotherapy based on the Phase 3 PhALLCON study (NCT03589326), which demonstrated a higher rate of minimal residual disease (MRD)-negative complete remission (CR) at the end of induction (EOI) with ponatinib… →
INTRODUCTION: Chlorophyllin (CHL) effectively decreases the side effects of radiotherapy (RT) by scavenging radiation-induced free radicals and reactive oxygen species in preclinical trials. This study aims to assess the efficacy of oral CHL for the treatment of brain radionecrosis in patients with diffuse glioma. →
CONCLUSIONS: Integrating traditional care with the modern healthcare system significantly increased TB case detection in high-burden settings. This approach not only enhances current TB control strategies but also has potential applications in managing other chronic diseases in resource-limited areas. Future research should evaluate the cost-effectiveness, scalability, and sustainability of this integrative model. Trial registration Unique… →