L-Eval: Instituting Standardized Evaluation for Long Context Language Models

Recently, there has been growing interest in extending the context length of instruction-following models in order to effectively process single-turn long input (e.g. summarizing a paper) and conversations with more extensive histories. While proprietary models such as GPT-4 and Claude have demonstrated considerable advancements in handling tens of thousands of tokens of context, open-sourced models are still in the early stages of experimentation. It also remains unclear whether developing these long context models can offer substantial gains on practical downstream tasks over retrieval-based methods or models simply trained on chunked contexts. To address this challenge, we propose to institute standardized evaluation for long context language models. Concretely, we develop L-Eval which contains 411 long documents and over 2,000 query-response pairs manually annotated and checked by the authors encompassing areas such as law, finance, school lectures, lengthy conversations, news, long-form novels, and meetings. L-Eval also adopts diverse evaluation methods and instruction styles, enabling a more reliable assessment of Long Context Language Models (LCLMs). Our findings indicate that while open-source models typically lag behind their commercial counterparts, they still exhibit impressive performance. LLaMA2 achieves the best results (win 45\% vs turbo-16k) on open-ended tasks with only 4k context length and ChatGLM2 achieves the best results on closed-ended tasks with 8k input tokens. We release our new evaluation suite, code, and all generation results including predictions from all open-sourced LCLMs, GPT4-32k, Cluade-100k at {\url{https://github.com/OpenLMLab/LEval}}

Fried food consumption and the risk of pancreatic cancer: A large prospective multicenter study

Background and aimsWhether fried food consumption is associated with the risk of pancreatic cancer remains elusive. We aimed to examine this association in a US population.MethodsA population-based cohort of 101,729 US adults was identified. Fried food consumption was assessed with a validated food frequency questionnaire. Hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated. Explanatory analyses were conducted to identify main contributor(s) to the observed association.ResultsDuring an average follow-up of 8.86 years (900871.2 person-years), 402 pancreatic cancer cases occurred. High consumption of total fried foods (deep-fried plus pan-fried foods; HRquartile4 vs. 1 0.71, 95% CI 0.51–0.99, Ptrend = 0.047) and deep-fried foods (HRquartile 4 vs. 1 0.64, 95% CI 0.47–0.88, Ptrend = 0.011), but not pan-fried foods (HRquartile 4 vs. 1 0.98, 95% CI 0.73–1.32; Ptrend = 0.815), was found to be associated with a reduced risk of pancreatic cancer in a non-linear dose–response manner, which was not modified by predefined stratification factors and persisted in sensitivity analyses. In explanatory analyses, only chip consumption was found to be inversely associated with the risk of pancreatic cancer; consistently, the initial significant associations between total fried food and deep-fried food consumption and the risk of pancreatic cancer changed to be non-significant after omitting or further adjusting for chip consumption.ConclusionConsumption of deep-fried foods, but not pan-fried foods, is inversely associated with the risk of pancreatic cancer in this US population. The role of deep-fried foods in reducing the risk of pancreatic cancer appears to be mainly attributable to chips. More studies are needed to confirm our findings in other populations and settings

Plasma Lipoprotein-associated Phospholipase A2 in Patients with Metabolic Syndrome and Carotid Atherosclerosis

<p>Abstract</p> <p>Background</p> <p>Lipoprotein-associated phospholipase A₂(Lp-PLA₂) is a recently identified and potentially useful plasma biomarker for cardiovascular and atherosclerotic diseases. However, the correlation between the Lp-PLA₂activity and carotid atherosclerosis remains poorly investigated in patients with metabolic syndrome (MetS). The present study aimed to evaluate the potential role of Lp-PLA₂as a comprehensive marker of metabolic syndrome in individuals with and without carotid atherosclerosis.</p> <p>Methods</p> <p>We documented 118 consecutive patients with MetS and 70 age- and sex-matched healthy subjects served as controls. The patients were further divided into two groups: 39 with carotid plaques and 79 without carotid plaques to elucidate the influence of Lp-PLA₂on carotid atherosclerosis. The plasma Lp-PLA₂activity was measured by using ELISA method and carotid intimal-media thickness (IMT) was performed by ultrasound in all participants.</p> <p>Results</p> <p>Lp-PLA₂activity was significantly increased in MetS subgroups when compared with controls, and was higher in patients with carotid plaques than those without plaques (<it>P </it>< 0.05). Furthermore, we found that significant difference in Lp-PLA₂was obtained between patients with three and four disorders of metabolic syndrome (<it>P </it>< 0.01). Age (β = 0.183, <it>P </it>= 0.029), LDL-cholesterol (β = 0.401, <it>P </it>= 0.000) and waist-hip ratio (β = 0.410, <it>P </it>= 0.000) emerged as significant and independent determinants of Lp-PLA₂activity. Multiple stepwise regression analysis revealed that LDL-cholesterol (β = 0.309, <it>P </it>= 0.000), systolic blood pressure (β = 0.322, <it>P </it>= 0.002) and age (β = 0.235, <it>P </it>= 0.007) significantly correlated with max IMT, and Lp-PLA₂was not an independent predictor for carotid IMT.</p> <p>Conclusions</p> <p>Lp-PLA₂may be a modulating factor for carotid IMT via age and LDL-cholesterol, not independent predictor in the pathophysiological process of carotid atherosclerosis in patients with MetS.</p

MicroRNA-203 inhibits cell proliferation by repressing ΔNp63 expression in human esophageal squamous cell carcinoma

<p>Abstract</p> <p>Background</p> <p>This study was performed to investigate the effect of microRNA-203 (miR-203) and ΔNp63 on cell proliferation and the functional connection between miR-203 and ΔNp63 in ESCC.</p> <p>Methods</p> <p>We employed 2 human ESCC cell lines, Eca109 and TE-1, as the model system. The effect of miR-203 and ΔNp63 on cell proliferation was determined in cells transfected with miR-203 mimic and ΔNp63 small interfering RNA (siRNA), respectively. The regulation of ΔNp63 expression in ESCC cells by miR-203 was studied by luciferase reporter assay, RT-PCR and western blot analysis in cells transfected with miR-203. The effect of ΔNp63 re-expression on miR-203 induced inhibition of cell proliferation was studied by cell proliferation assay in cells cotransfected with miR-203 and pcDNA-ΔNp63 plasmid (without the 3'-UTR of <it>ΔNp63</it>).</p> <p>Results</p> <p>We found that both miR-203 and ΔNp63 siRNA signicantly inhibited cell proliferation in ESCC. MiR-203 could down-regulate endogenous ΔNp63 expression at the posttranscriptional level. Moreover, re-expression of ΔNp63 in cells transfected with miR-203 significantly attenuated the miR-203 induced inhibition of cell proliferation.</p> <p>Conclusions</p> <p>Our data implied that miR-203 could inhibit cell proliferation in human ESCC through ΔNp63-mediated signal pathway. Therefore, we propose that miR-203 might be used as a therapeutic agent for human ESCC.</p

The autonomic nervous system: A potential link to the efficacy of acupuncture

The autonomic nervous system (ANS) is a diffuse network that regulates physiological systems to maintain body homeostasis by integrating inputs from the internal and external environment, including the sympathetic, parasympathetic, and enteric nervous systems (ENS). Recent evidence suggests that ANS is one of the key neural pathways for acupuncture signal transduction, which has attracted worldwide attention in the acupuncture field. Here, we reviewed the basic and clinical research published in PubMed over the past 20 years on the effects of acupuncture on ANS regulation and homeostasis maintenance. It was found that acupuncture effectively alleviates ANS dysfunction-associated symptoms in its indications, such as migraine, depression, insomnia, functional dyspepsia, functional constipation. Acupuncture stimulation on some specific acupoints activates sensory nerve fibers, the spinal cord, and the brain. Using information integration and efferents from a complex network of autonomic nuclei of the brain, such as the insular cortex (IC), prefrontal cortex, anterior cingulate cortex (ACC), amygdala (AMG), hypothalamus, periaqueductal gray (PAG), nucleus tractus solitarius (NTS), ventrolateral medulla (VLM), nucleus ambiguus (AMB), acupuncture alleviates visceral dysfunction, inflammation via efferent autonomic nerves, and relieves pain and pain affect. The modulating pattern of sympathetic and parasympathetic nerves is associated with acupuncture stimulation on specific acupoints, intervention parameters, and disease models, and the relationships among them require further exploration. In conclusion, ANS is one of the therapeutic targets for acupuncture and mediates acupuncture’s actions, which restores homeostasis. A systemic study is needed to determine the rules and mechanisms underlying the effects of acupoint stimulation on corresponding organs mediated by specific central nervous networks and the efferent ANS

Application of Entity-BERT model based on neuroscience and brain-like cognition in electronic medical record entity recognition

IntroductionIn the medical field, electronic medical records contain a large amount of textual information, and the unstructured nature of this information makes data extraction and analysis challenging. Therefore, automatic extraction of entity information from electronic medical records has become a significant issue in the healthcare domain.MethodsTo address this problem, this paper proposes a deep learning-based entity information extraction model called Entity-BERT. The model aims to leverage the powerful feature extraction capabilities of deep learning and the pre-training language representation learning of BERT(Bidirectional Encoder Representations from Transformers), enabling it to automatically learn and recognize various entity types in medical electronic records, including medical terminologies, disease names, drug information, and more, providing more effective support for medical research and clinical practices. The Entity-BERT model utilizes a multi-layer neural network and cross-attention mechanism to process and fuse information at different levels and types, resembling the hierarchical and distributed processing of the human brain. Additionally, the model employs pre-trained language and sequence models to process and learn textual data, sharing similarities with the language processing and semantic understanding of the human brain. Furthermore, the Entity-BERT model can capture contextual information and long-term dependencies, combining the cross-attention mechanism to handle the complex and diverse language expressions in electronic medical records, resembling the information processing method of the human brain in many aspects. Additionally, exploring how to utilize competitive learning, adaptive regulation, and synaptic plasticity to optimize the model's prediction results, automatically adjust its parameters, and achieve adaptive learning and dynamic adjustments from the perspective of neuroscience and brain-like cognition is of interest.Results and discussionExperimental results demonstrate that the Entity-BERT model achieves outstanding performance in entity recognition tasks within electronic medical records, surpassing other existing entity recognition models. This research not only provides more efficient and accurate natural language processing technology for the medical and health field but also introduces new ideas and directions for the design and optimization of deep learning models

Study of the expression levels of Hepatocyte nuclear factor 4 alpha and 3 beta in patients with different outcome of HBV infection

Hepatocyte nuclear factors 4 alpha (HNF4α) and 3 beta (HNF3β) are members of a group of liver-enriched transcription factors (LETFs) that play important roles in regulating the replication of hepatitis B virus (HBV) and liver inflammation. However, the relationship of the level of HNF4α and HNF3β with the severity of HBV-infected liver diseases is unclear. In this study, liver tissue samples from different types of HBV patients were collected, and HNF4α and HNF3β expression were detected by immunohistochemistry. The expression of HNF4α was significant higher in patients with severe hepatitis B(SHB) than those with chronic hepatitis B(CHB) and liver cirrhosis(LC) (both P < 0.05), but similar between patients with CHB and LC (P > 0.05). And the expression of HNF3β was similar among patients with CHB, LC and SHB (P > 0.05 for all pairwise comparison). This suggests that the expression level of HNF4α was different in patients with different outcome of HBV infection, high expression level of HNF4α may correlate with occurrence of SH

Rhizoma Atractylodis Macrocephalae—Assessing the influence of herbal processing methods and improved effects on functional dyspepsia

Background: The unique pharmaceutical methods for the processing of botanical drugs according to the theory of traditional Chinese medicine (TCM) affect clinical syndrome differentiation and treatment. The objective of this study was to comprehensively elucidate the principles and mechanisms of an herbal processing method by investigating the alterations in the metabolites of Rhizoma Atractylodis Macrocephalae (AMR) processed by Aurantii Fructus Immaturus (AFI) decoction and to determine how these changes enhance the efficacy of aqueous extracts in treating functional dyspepsia (FD).Methods: A qualitative analysis of AMR before and after processing was conducted using UPLC-Q-TOF-MS/MS, and HPLC was employed for quantitative analysis. A predictive analysis was then conducted using a network analysis strategy to establish a botanical drug–metabolite–target–disease (BMTD) network and a protein–protein interaction (PPI) network, and the predictions were validated using an FD rat model.Results: A total of 127 metabolites were identified in the processed AMR (PAMR), and substantial changes were observed in 8 metabolites of PAMR after processing, as revealed by the quantitative analysis. The enhanced aqueous extracts of processed AMR (PAMR) demonstrate improved efficacy in treating FD, which indicates that this processing method enhances the anti-inflammatory properties and promotes gastric motility by modulating DRD2, SCF, and c-kit. However, this enhancement comes at the cost of attenuating the regulation of motilin (MTL), gastrin (GAS), acetylcholine (Ach), and acetylcholinesterase (AchE).Conclusion: Through this series of investigations, we aimed to unravel the factors influencing the efficacy of this herbal formulation in improving FD in clinical settings

Multiple organ infection and the pathogenesis of SARS

After >8,000 infections and >700 deaths worldwide, the pathogenesis of the new infectious disease, severe acute respiratory syndrome (SARS), remains poorly understood. We investigated 18 autopsies of patients who had suspected SARS; 8 cases were confirmed as SARS. We evaluated white blood cells from 22 confirmed SARS patients at various stages of the disease. T lymphocyte counts in 65 confirmed and 35 misdiagnosed SARS cases also were analyzed retrospectively. SARS viral particles and genomic sequence were detected in a large number of circulating lymphocytes, monocytes, and lymphoid tissues, as well as in the epithelial cells of the respiratory tract, the mucosa of the intestine, the epithelium of the renal distal tubules, the neurons of the brain, and macrophages in different organs. SARS virus seemed to be capable of infecting multiple cell types in several organs; immune cells and pulmonary epithelium were identified as the main sites of injury. A comprehensive theory of pathogenesis is proposed for SARS with immune and lung damage as key features