Study on the Molecular Mechanisms of dlk1 Stimulated Lung Cancer Cell Proliferation

Background and objective The imprinted gene dlk1 has been recognized as a cancer related gene since it aberrantly expressed in a series of cancer tissues, but its role in lung cancer is still unknown. The aim of this study is to examine dlk1’s expression in non-small cell lung cancers (NSCLCs) and investigate the molecular mechanism by which dlk1 could accelerate the proliferation of the cells in lung cancer cell lines (H520). Methods The relative expression of dlk1 among 30 NSCLC specimens and their adjacent normal lung tissues were analyzed by RT-PCR. A cell model that stably expressed exogenous dlk1 was established following that the dlk1 gene was cloned into a eukaryotic expression vector and then transfected into the lung cancer cells H520. CCK8 analysis and colony forming assay were employed to investigate the effect of dlk1 on cell proliferation. The expression of CyclinB1 was detected by Western blot. Results dlk1 aberrantly expressed in 36.7% (11/30) of the tumor tissues of NSCLC compared with their adjacent cancer lung tissues. CCK8 analysis showed that overexpression of dlk1 could promote the proliferation of H520 cells (P < 0.05) and the results was further confirmed by colony forming assay. Western blot analysis found that over expression of dlk1 could up-regulate the expression of CyclinB1 (P < 0.05). Conclusion dlk1 aberrantly expressed in NSCLCs. The Overexpression of dlk1 could accelerate the proliferation of lung cancer cells H520 in vitro, probably through up-regulating the expression of cell cycle protein CyclinB1

Nonlinear Craig Interpolant Generation over Unbounded Domains by Separating Semialgebraic Sets

Interpolation-based techniques become popular in recent years, as they can improve the scalability of existing verification techniques due to their inherent modularity and local reasoning capabilities. Synthesizing Craig interpolants is the cornerstone of these techniques. In this paper, we investigate nonlinear Craig interpolant synthesis for two polynomial formulas of the general form, essentially corresponding to the underlying mathematical problem to separate two disjoint semialgebraic sets. By combining the homogenization approach with existing techniques, we prove the existence of a novel class of non-polynomial interpolants called semialgebraic interpolants. These semialgebraic interpolants subsume polynomial interpolants as a special case. To the best of our knowledge, this is the first existence result of this kind. Furthermore, we provide complete sum-of-squares characterizations for both polynomial and semialgebraic interpolants, which can be efficiently solved as semidefinite programs. Examples are provided to demonstrate the effectiveness and efficiency of our approach.Comment: 21 pages (with appendix); accepted by the 26th International Symposium on Formal Methods (FM2024

Evaluating land cover influences on model uncertainties—A case study of cropland carbon dynamics in the Mid-Continent Intensive Campaign region

tQuantifying spatial and temporal patterns of carbon sources and sinks and their uncertainties acrossagriculture-dominated areas remains challenging for understanding regional carbon cycles. Character-istics of local land cover inputs could impact the regional carbon estimates but the effect has not beenfully evaluated in the past. Within the North American Carbon Program Mid-Continent Intensive (MCI)Campaign, three models were developed to estimate carbon fluxes on croplands: an inventory-basedmodel, the Environmental Policy Integrated Climate (EPIC) model, and the General Ensemble biogeo-chemical Modeling System (GEMS) model. They all provided estimates of three major carbon fluxes oncropland: net primary production (NPP), net ecosystem production (NEP), and soil organic carbon (SOC)change. Using data mining and spatial statistics, we studied the spatial distribution of the carbon fluxesuncertainties and the relationships between the uncertainties and the land cover characteristics. Resultsindicated that uncertainties for all three carbon fluxes were not randomly distributed, but instead formedmultiple clusters within the MCI region. We investigated the impacts of three land cover characteristicson the fluxes uncertainties: cropland percentage, cropland richness and cropland diversity. The resultsindicated that cropland percentage significantly influenced the uncertainties of NPP and NEP, but noton the uncertainties of SOC change. Greater uncertainties of NPP and NEP were found in counties withsmall cropland percentage than the counties with large cropland percentage. Cropland species richnessand diversity also showed negative correlations with the model uncertainties. Our study demonstratedthat the land cover characteristics contributed to the uncertainties of regional carbon fluxes estimates.The approaches we used in this study can be applied to other ecosystem models to identify the areaswith high uncertainties and where models can be improved to reduce overall uncertainties for regionalcarbon flux estimates

MMGER: Multi-modal and Multi-granularity Generative Error Correction with LLM for Joint Accent and Speech Recognition

Despite notable advancements in automatic speech recognition (ASR), performance tends to degrade when faced with adverse conditions. Generative error correction (GER) leverages the exceptional text comprehension capabilities of large language models (LLM), delivering impressive performance in ASR error correction, where N-best hypotheses provide valuable information for transcription prediction. However, GER encounters challenges such as fixed N-best hypotheses, insufficient utilization of acoustic information, and limited specificity to multi-accent scenarios. In this paper, we explore the application of GER in multi-accent scenarios. Accents represent deviations from standard pronunciation norms, and the multi-task learning framework for simultaneous ASR and accent recognition (AR) has effectively addressed the multi-accent scenarios, making it a prominent solution. In this work, we propose a unified ASR-AR GER model, named MMGER, leveraging multi-modal correction, and multi-granularity correction. Multi-task ASR-AR learning is employed to provide dynamic 1-best hypotheses and accent embeddings. Multi-modal correction accomplishes fine-grained frame-level correction by force-aligning the acoustic features of speech with the corresponding character-level 1-best hypothesis sequence. Multi-granularity correction supplements the global linguistic information by incorporating regular 1-best hypotheses atop fine-grained multi-modal correction to achieve coarse-grained utterance-level correction. MMGER effectively mitigates the limitations of GER and tailors LLM-based ASR error correction for the multi-accent scenarios. Experiments conducted on the multi-accent Mandarin KeSpeech dataset demonstrate the efficacy of MMGER, achieving a 26.72% relative improvement in AR accuracy and a 27.55% relative reduction in ASR character error rate, compared to a well-established standard baseline

Molecular characterization of methicillin-resistant and -susceptible Staphylococcus aureus recovered from hospital personnel

Introduction Methicillin resistant Staphylococcus aureus (MRSA) is one of the major causes of hospital acquired infections. Over the past two decades MRSA has become ‘epidemic’ in many hospitals worldwide. However, little is known about the genetic background of S. aureus recovered from hospital personnel in China. Aim The aim of this study was to determine the genetic diversity of MRSA and methicillin susceptible S. aureus (MSSA) recovered from hospital personnel in Tianjin, North China. Methodology Three hundred and sixty-eight hand or nasal swabs were collected from 276 hospital personnel in four tertiary hospitals in Tianjin, North China between November 2017 and March 2019. In total, 535 gram-positive bacteria were isolated, of which 59 were identified as S. aureus. Staphylococcal cassette chromosome mec (SCCmec) typing, multi-locus sequence typing (MLST) and spa typing were performed to determine molecular characteristics of S. aureus. Results Thirty-one out of 276 (11%) hospital personnel were S. aureus carriers, whereas 11/276 (4%) carried MRSA. Fifty out of 59 (85%) of S. aureus isolates were resistant or intermediate resistant to erythromycin. The dominant genotypes of MRSA recovered from hospital personnel were ST398-t034-SCCmecIV/V, and ST630-t084/t2196, whereas major genotypes of MSSA included ST15-t078/t084/t346/t796/t8862/ t8945/t11653 and ST398-t189/t034/ t078/t084/t14014. Conclusion Although, the predominant genotypes of MRSA recovered from hospital personnel in this study were different from those main genotypes that have previously been reported to cause infections in Tianjin and in other geographic areas of China, the MRSA ST398-t034 genotype has previously been reported to be associated with livestock globally. The dominant MSSA genotypes recovered from hospital personnel were consistent with those previously reported MSSA genotypes recovered from the clinic. The diversity of S. aureus genotypes warrantee further surveillance and genomic studies to better understand the relatedness of these bacteria with those recovered from patients and community

Uncovering the burden of hidradenitis suppurativa misdiagnosis and underdiagnosis: a machine learning approach

Hidradenitis suppurativa (HS) is a chronic inflammatory follicular skin condition that is associated with significant psychosocial and economic burden and a diminished quality of life and work productivity. Accurate diagnosis of HS is challenging due to its unknown etiology, which can lead to underdiagnosis or misdiagnosis that results in increased patient and healthcare system burden. We applied machine learning (ML) to a medical and pharmacy claims database using data from 2000 through 2018 to develop a novel model to better understand HS underdiagnosis on a healthcare system level. The primary results demonstrated that high-performing models for predicting HS diagnosis can be constructed using claims data, with an area under the curve (AUC) of 81%–82% observed among the top-performing models. The results of the models developed in this study could be input into the development of an impact of inaction model that determines the cost implications of HS diagnosis and treatment delay to the healthcare system