Comparison of Laboratory Rhythms in Several Species and Genera of Ants

Few simultaneous comparative studies of functional diversity have been made at the genus level. Four genera (Pogonomyrmex, Veromessor, Formica, Myrmecocystus) of ants were compared, two of them represented by two species each. Under controlled temperatures, in alternating light and dark, there was more difference in phase of rhythm among than within genera. This evidence adds to previous field evidence for a taxonomic explanation of such diversity in ants

CAPSTONE: Curriculum Sampling for Dense Retrieval with Document Expansion

The dual-encoder has become the de facto architecture for dense retrieval. Typically, it computes the latent representations of the query and document independently, thus failing to fully capture the interactions between the query and document. To alleviate this, recent research has focused on obtaining query-informed document representations. During training, it expands the document with a real query, but during inference, it replaces the real query with a generated one. This inconsistency between training and inference causes the dense retrieval model to prioritize query information while disregarding the document when computing the document representation. Consequently, it performs even worse than the vanilla dense retrieval model because its performance heavily relies on the relevance between the generated queries and the real query.In this paper, we propose a curriculum sampling strategy that utilizes pseudo queries during training and progressively enhances the relevance between the generated query and the real query. By doing so, the retrieval model learns to extend its attention from the document alone to both the document and query, resulting in high-quality query-informed document representations. Experimental results on both in-domain and out-of-domain datasets demonstrate that our approach outperforms previous dense retrieval models.Comment: Accetpted to EMNLP 202

AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators

Many natural language processing (NLP) tasks rely on labeled data to train machine learning models to achieve high performance. However, data annotation can be a time-consuming and expensive process, especially when the task involves a large amount of data or requires specialized domains. Recently, GPT-3.5 series models have demonstrated remarkable few-shot and zero-shot ability across various NLP tasks. In this paper, we first claim that large language models (LLMs), such as GPT-3.5, can serve as an excellent crowdsourced annotator by providing them with sufficient guidance and demonstrated examples. To make LLMs to be better annotators, we propose a two-step approach, 'explain-then-annotate'. To be more precise, we begin by creating prompts for every demonstrated example, which we subsequently utilize to prompt a LLM to provide an explanation for why the specific ground truth answer/label was chosen for that particular example. Following this, we construct the few-shot chain-of-thought prompt with the self-generated explanation and employ it to annotate the unlabeled data. We conduct experiments on three tasks, including user input and keyword relevance assessment, BoolQ and WiC. The annotation results from GPT-3.5 surpasses those from crowdsourced annotation for user input and keyword relevance assessment. Additionally, for the other two tasks, GPT-3.5 achieves results that are comparable to those obtained through crowdsourced annotation

Reduced CD27-IgD- B cells in blood and raised CD27-IgD- B cells in gut-associated lymphoid tissue in inflammatory bowel disease.

The intestinal mucosa in inflammatory bowel disease (IBD) contains increased frequencies of lymphocytes and a disproportionate increase in plasma cells secreting immunoglobulin (Ig)G relative to other isotypes compared to healthy controls. Despite consistent evidence of B lineage cells in the mucosa in IBD, little is known of B cell recruitment to the gut in IBD. Here we analyzed B cells in blood of patients with Crohn's disease (CD) and ulcerative colitis (UC) with a range of disease activities. We analyzed the frequencies of known B cell subsets in blood and observed a consistent reduction in the proportion of CD27−IgD− B cells expressing all Ig isotypes in the blood in IBD (independent of severity of disease and treatment) compared to healthy controls. Successful treatment of patients with biologic therapies did not change the profile of B cell subsets in blood. By mass cytometry we demonstrated that CD27−IgD− B cells were proportionately enriched in the gut-associated lymphoid tissue (GALT) in IBD. Since production of TNFα is a feature of IBD relevant to therapies, we sought to determine whether B cells in GALT or the CD27−IgD− subset in particular could contribute to pathology by secretion of TNFα or IL-10. We found that donor matched GALT and blood B cells are capable of producing TNFα as well as IL-10, but we saw no evidence that CD27−IgD− B cells from blood expressed more TNFα compared to other subsets. The reduced proportion of CD27−IgD− B cells in blood and the increased proportion in the gut implies that CD27−IgD− B cells are recruited from the blood to the gut in IBD. CD27−IgD− B cells have been implicated in immune responses to intestinal bacteria and recruitment to GALT, and may contribute to the intestinal inflammatory milieu in IBD

Local structural alignment of RNA with affine gap model

BACKGROUND: Predicting new non-coding RNAs (ncRNAs) of a family can be done by aligning the potential candidate with a member of the family with known sequence and secondary structure. Existing tools either only consider the sequence similarity or cannot handle local alignment with gaps. RESULTS: In this paper, we consider the problem of finding the optimal local structural alignment between a query RNA sequence (with known secondary structure) and a target sequence (with unknown secondary structure) with the affine gap penalty model. We provide the algorithm to solve the problem. CONCLUSIONS: Based on an experiment, we show that there are ncRNA families in which considering local structural alignment with gap penalty model can identify real hits more effectively than using global alignment or local alignment without gap penalty model.published_or_final_versio

Glyoxalase-I Is a Novel Prognosis Factor Associated with Gastric Cancer Progression

Glyoxalase I (GLO1), a methylglyoxal detoxification enzyme, is implicated in the progression of human malignancies. The role of GLO1 in gastric cancer development or progression is currently unclear. The expression of GLO1 was determined in primary gastric cancer specimens using quantitative polymerase chain reaction, immunohistochemistry (IHC), and western blotting analyses. GLO1 expression was higher in gastric cancer tissues, compared with that in adjacent noncancerous tissues. Elevated expression of GLO1 was significantly associated with gastric wall invasion, lymph node metastasis, and pathological stage, suggesting a novel role of GLO1 in gastric cancer development and progression. The 5-year survival rate of the lower GLO1 expression groups was significantly greater than that of the higher expression groups (log rank P = 0.0373) in IHC experiments. Over-expression of GLO1 in gastric cancer cell lines increases cell proliferation, migration and invasiveness. Conversely, down-regulation of GLO1 with shRNA led to a marked reduction in the migration and invasion abilities. Our data strongly suggest that high expression of GLO1 in gastric cancer enhances the metastasis ability of tumor cells in vitro and in vivo, and support its efficacy as a potential marker for the detection and prognosis of gastric cancer

Impact of the Herbal Medicine Sophora flavescens on the Oral Pharmacokinetics of Indinavir in Rats: The Involvement of CYP3A and P-Glycoprotein

Sophora flavescens is a Chinese medicinal herb used for the treatment of gastrointestinal hemorrhage, skin diseases, pyretic stranguria and viral hepatitis. In this study the herb-drug interactions between S. flavescens and indinavir, a protease inhibitor for HIV treatment, were evaluated in rats. Concomitant oral administration of Sophora extract (0.158 g/kg or 0.63 g/kg, p.o.) and indinavir (40 mg/kg, p.o.) in rats twice a day for 7 days resulted in a dose-dependent decrease of plasma indinavir concentrations, with 55%–83% decrease in AUC0-∞ and 38%–78% reduction in Cmax. The CL (Clearance)/F (fraction of dose available in the systemic circulation) increased up to 7.4-fold in Sophora-treated rats. Oxymatrine treatment (45 mg/kg, p.o.) also decreased indinavir concentrations, while the ethyl acetate fraction of Sophora extract had no effect. Urinary indinavir (24-h) was reduced, while the fraction of indinavir in faeces was increased after Sophora treatment. Compared to the controls, multiple dosing of Sophora extract elevated both mRNA and protein levels of P-gp in the small intestine and liver. In addition, Sophora treatment increased intestinal and hepatic mRNA expression of CYP3A1, but had less effect on CYP3A2 expression. Although protein levels of CYP3A1 and CYP3A2 were not altered by Sophora treatment, hepatic CYP3A activity increased in the Sophora-treated rats. All available data demonstrated that Sophora flavescens reduced plasma indinavir concentration after multiple concomitant doses, possibly through hepatic CYP3A activity and induction of intestinal and hepatic P-gp. The animal study would be useful for predicting potential interactions between natural products and oral pharmaceutics and understanding the mechanisms prior to human studies. Results in the current study suggest that patients using indinavir might be cautioned in the use of S. flavescens extract or Sophora-derived products

NF-κB Hyper-Activation by HTLV-1 Tax Induces Cellular Senescence, but Can Be Alleviated by the Viral Anti-Sense Protein HBZ

Activation of I-κB kinases (IKKs) and NF-κB by the human T lymphotropic virus type 1 (HTLV-1) trans-activator/oncoprotein, Tax, is thought to promote cell proliferation and transformation. Paradoxically, expression of Tax in most cells leads to drastic up-regulation of cyclin-dependent kinase inhibitors, p21CIP1/WAF1 and p27KIP1, which cause p53-/pRb-independent cellular senescence. Here we demonstrate that p21CIP1/WAF1-/p27KIP1-mediated senescence constitutes a checkpoint against IKK/NF-κB hyper-activation. Senescence induced by Tax in HeLa cells is attenuated by mutations in Tax that reduce IKK/NF-κB activation and prevented by blocking NF-κB using a degradation-resistant mutant of I-κBα despite constitutive IKK activation. Small hairpin RNA-mediated knockdown indicates that RelA induces this senescence program by acting upstream of the anaphase promoting complex and RelB to stabilize p27KIP1 protein and p21CIP1/WAF1 mRNA respectively. Finally, we show that down-regulation of NF-κB by the HTLV-1 anti-sense protein, HBZ, delay or prevent the onset of Tax-induced senescence. We propose that the balance between Tax and HBZ expression determines the outcome of HTLV-1 infection. Robust HTLV-1 replication and elevated Tax expression drive IKK/NF-κB hyper-activation and trigger senescence. HBZ, however, modulates Tax-mediated viral replication and NF-κB activation, thus allowing HTLV-1-infected cells to proliferate, persist, and evolve. Finally, inactivation of the senescence checkpoint can facilitate persistent NF-κB activation and leukemogenesis

A quantitative model for estimating risk from multiple interacting natural hazards: an application to northeast Zhejiang, China

Multi-hazard risk assessment is a major concern in risk analysis, but most approaches do not consider all hazard interactions when calculating possible losses. We address this problem by developing an improved quantitative model - Model for multi-hazard Risk assessment with a consideration of Hazard Interaction (MmhRisk-HI). This model calculates the possible loss caused by multiple hazards, with an explicit consideration of interaction between those hazards. There are two main components to the model. In the first, based on the hazard-forming environment, relationships among hazards are classified into four types for calculation of the exceedance probability of multiple hazards occurrence. In the second, a Bayesian network is used to calculate possible loss caused by multiple hazards with different exceedance probabilities. A multi-hazard risk map can then be drawn addressing the probability of multi-hazard occurrence and corresponding loss. This model was applied in northeast Zhejiang, China and validated by comparison against an observed multi-hazard sequence. The validation results show that the model can more effectively represent the real world, and that the modelled outputs, possible loss caused by multiple hazards, are reliable. The outputs can additionally help to identify areas at greatest risk, and allows a determination of the factors that contribute to that risk, and hence the model can provide useful further information for planners and decision-makers concerned with risk mitigation