Enabling CMF Estimation in Data-Constrained Scenarios: A Semantic-Encoding Knowledge Mining Model

Precise estimation of Crash Modification Factors (CMFs) is central to evaluating the effectiveness of various road safety treatments and prioritizing infrastructure investment accordingly. While customized study for each countermeasure scenario is desired, the conventional CMF estimation approaches rely heavily on the availability of crash data at given sites. This not only makes the estimation costly, but the results are also less transferable, since the intrinsic similarities between different safety countermeasure scenarios are not fully explored. Aiming to fill this gap, this study introduces a novel knowledge-mining framework for CMF prediction. This framework delves into the connections of existing countermeasures and reduces the reliance of CMF estimation on crash data availability and manual data collection. Specifically, it draws inspiration from human comprehension processes and introduces advanced Natural Language Processing (NLP) techniques to extract intricate variations and patterns from existing CMF knowledge. It effectively encodes unstructured countermeasure scenarios into machine-readable representations and models the complex relationships between scenarios and CMF values. This new data-driven framework provides a cost-effective and adaptable solution that complements the case-specific approaches for CMF estimation, which is particularly beneficial when availability of crash data or time imposes constraints. Experimental validation using real-world CMF Clearinghouse data demonstrates the effectiveness of this new approach, which shows significant accuracy improvements compared to baseline methods. This approach provides insights into new possibilities of harnessing accumulated transportation knowledge in various applications.Comment: 39 pages, 9 figure

4-[(9-Ethyl-9H-carbazol-3-yl)imino­meth­yl]phenol

In the title compound, C21H18N2O, the dihedral angle between the phenol ring and the carbazole system is 39.34 (2)°. Inter­molecular O—H⋯N hydrogen bonds and C—H⋯π and π–π inter­actions [centroid–centroid distances = 3.426 (2) and 3.768 (2) Å] stabilize the crystal structure

EvEval: A Comprehensive Evaluation of Event Semantics for Large Language Models

Events serve as fundamental units of occurrence within various contexts. The processing of event semantics in textual information forms the basis of numerous natural language processing (NLP) applications. Recent studies have begun leveraging large language models (LLMs) to address event semantic processing. However, the extent that LLMs can effectively tackle these challenges remains uncertain. Furthermore, the lack of a comprehensive evaluation framework for event semantic processing poses a significant challenge in evaluating these capabilities. In this paper, we propose an overarching framework for event semantic processing, encompassing understanding, reasoning, and prediction, along with their fine-grained aspects. To comprehensively evaluate the event semantic processing abilities of models, we introduce a novel benchmark called EVEVAL. We collect 8 datasets that cover all aspects of event semantic processing. Extensive experiments are conducted on EVEVAL, leading to several noteworthy findings based on the obtained results

A potential anti-tumor herbal medicine, Corilagin, inhibits ovarian cancer cell growth through blocking the TGF-β signaling pathways

BACKGROUND: Phyllanthus niruri L. is a well-known hepatoprotective and antiviral medicinal herb. Recently, we identified Corilagin as a major active component with anti-tumor activity in this herbal medicine. Corilagin is a member of the tannin family that has been discovered in many medicinal plants and has been used as an anti-inflammatory agent. However, there have been few reports of the anti-tumor effects of Corilagin, and its anti-tumor mechanism has not been investigated clearly. The aim of the present study is to investigate the anticancer properties of Corilagin in ovarian cancer cells. METHODS: The ovarian cancer cell lines SKOv3ip, Hey and HO-8910PM were treated with Corilagin and analyzed by Sulforhodamine B (SRB) cell proliferation assay, flow cytometry, and reverse phase protein array (RPPA). Corilagin was delivered intraperitoneally to mice bearing SKOv3ip xenografts. RESULTS: Corilagin inhibited the growth of the ovarian cancer cell lines SKOv3ip and Hey, with IC50 values of less than 30 μM, while displaying low toxicity against normal ovarian surface epithelium cells, with IC50 values of approximately 160 μM. Corilagin induced cell cycle arrest at the G2/M stage and enhanced apoptosis in ovarian cancer cells. Immunoblotting assays demonstrated that Cyclin B1, Myt1, Phospho-cdc2 and Phospho-Weel were down-regulated after Corilagin treatment. Xenograft tumor growth was significantly lower in the Corilagin-treated group compared with the untreated control group (P <0.05). More interestingly, Corilagin inhibited TGF-β secretion into the culture supernatant of all tested ovarian cancer cell lines and blocked the TGF-β-induced stabilization of Snail. In contrast, a reduction of TGF-β secretion was not observed in cancer cells treated with the cytotoxic drug Paclitaxel, suggesting that Corilagin specifically targets TGF-β secretion. Corilagin blocked the activation of both the canonical Smad and non-canonical ERK/AKT pathways. CONCLUSIONS: Corilagin extracted from Phyllanthus niruri L. acts as a natural, effective therapeutic agent against the growth of ovarian cancer cells via targeted action against the TGF-β/AKT/ERK/Smad signaling pathways

(E)-3-[4-(Dimethyl­amino)phen­yl]-1-(2-pyrid­yl)prop-2-en-1-one

In the title mol­ecule, C16H16N2O, the pyridine ring and non-H atoms of the =CH—C(=O)— unit are coplaner, the largest deviation being 0.045 (2) Å for the O atom. The dihedral angle between this plane and the benzene ring is 2.79 (2)°. The mol­ecular structure is stabilized by inter­molecular C—H⋯π and inter­actions

Potential sites of CFTR activation by tyrosine kinases

The CFTR chloride channel is tightly regulated by phosphorylation at multiple serine residues. Recently it has been proposed that its activity is also regulated by tyrosine kinases, however the tyrosine phosphorylation sites remain to be identified. In this study we examined 2 candidate tyrosine residues near the boundary between the first nucleotide binding domain and the R domain, a region which is important for channel function but devoid of PKA consensus sequences. Mutating tyrosines at positions 625 and 627 dramatically reduced responses to Src or Pyk2 without altering the activation by PKA, suggesting they may contribute to CFTR regulation

Rapid Identification of Major QTLS Associated With Near- Freezing Temperature Tolerance in Saccharomyces cerevisiae

Temperatures had a strong effect on many life history traits, including growth, development and reproduction. At near-freezing temperatures (0–4°C), yeast cells could trigger series of biochemical reactions to respond and adapt to the stress, protect them against sever cold and freeze injury. Different Saccharomyces cerevisiae strains vary greatly in their ability to grow at near-freezing temperatures. However, the molecular mechanisms that allow yeast cells to sustain this response are not yet fully understood and the genetic basis of tolerance and sensitivity to near-freeze stress remains unclear. Uncovering the genetic determinants of this trait is, therefore, of is of significant interest. In order to investigate the genetic basis that underlies near-freezing temperature tolerance in S. cerevisiae, we mapped the major quantitative trait loci (QTLs) using bulk segregant analysis (BSA) in the F2 segregant population of two Chinese indigenous S. cerevisiae strains with divergent tolerance capability at 4°C. By genome-wide comparison of single-nucleotide polymorphism (SNP) profiles between two bulks of segregants with high and low tolerance to near-freezing temperature, a hot region located on chromosome IV was identified tightly associated with the near-freezing temperature tolerance. The Reciprocal hemizygosity analysis (RHA) and gene deletion was used to validate the genes involved in the trait, showed that the gene NAT1 plays a role in the near-freezing temperature tolerance. This study improved our understanding of the genetic basis of the variability of near-freezing temperature tolerance in yeasts. The superior allele identified could be used to genetically improve the near-freezing stress adaptation of industrial yeast strains