Lattice score based data cleaning for phrase-based statistical machine translation

Statistical machine translation relies heavily on parallel corpora to train its models for translation tasks. While more and more bilingual corpora are readily available, the quality of the sentence pairs should be taken into consideration. This paper presents a novel lattice score-based data cleaning method to select proper sentence pairs from the ones extracted from a bilingual corpus by the sentence alignment methods. The proposed method is carried out as follows: firstly, an initial phrasebased model is trained on the full sentencealigned corpus; then for each of the sentence pairs in the corpus, word alignments are used to create anchor pairs and sourceside lattices; thirdly, based on the translation model, target-side phrase networks are expanded on the lattices and Viterbi searching is used to find approximated decoding results; finally, BLEU score thresholds are used to filter out the low-score sentence pairs for the data cleaning purpose. Our experiments on the FBIS corpus showed improvements of BLEU score from 23.78 to 24.02 in Chinese-English

Hürthle cell carcinoma: current perspectives.

Hürthle cell carcinoma (HCC) can present either as a minimally invasive or as a widely invasive tumor. HCC generally has a more aggressive clinical behavior compared with the other differentiated thyroid cancers, and it is associated with a higher rate of distant metastases. Minimally invasive HCC demonstrates much less aggressive behavior; lesions <4 cm can be treated with thyroid lobectomy alone, and without radioactive iodine (RAI). HCC has been observed to be less iodine-avid compared with other differentiated thyroid cancers; however, recent data have demonstrated improved survival with RAI use in patients with HCC >2 cm and those with nodal and distant metastases. Patients with localized iodine-resistant disease who are not candidates for a wait-and-watch approach can be treated with localized therapies. Systemic therapy is reserved for patients with progressive, widely metastatic HCC

Social-LLM: Modeling User Behavior at Scale using Language Models and Social Network Data

The proliferation of social network data has unlocked unprecedented opportunities for extensive, data-driven exploration of human behavior. The structural intricacies of social networks offer insights into various computational social science issues, particularly concerning social influence and information diffusion. However, modeling large-scale social network data comes with computational challenges. Though large language models make it easier than ever to model textual content, any advanced network representation methods struggle with scalability and efficient deployment to out-of-sample users. In response, we introduce a novel approach tailored for modeling social network data in user detection tasks. This innovative method integrates localized social network interactions with the capabilities of large language models. Operating under the premise of social network homophily, which posits that socially connected users share similarities, our approach is designed to address these challenges. We conduct a thorough evaluation of our method across seven real-world social network datasets, spanning a diverse range of topics and detection tasks, showcasing its applicability to advance research in computational social science.Comment: 10 pages, 5 figures, 2 table

Regulation of cAMP responses by the G12/13 pathway converges on adenylyl cyclase VII

Regulation of intracellular cyclic adenosine 3’, 5’-monophosphate (cAMP) by multiple pathways enables differential function of this ubiquitous second messenger in a context dependent manner. Modulation of Gs-stimulated intracellular cAMP has long been known to be modulated by the Gi and Gq/Ca2+ pathways. Recently, the G13 pathway was also shown to facilitate cAMP responses in murine macrophage cells. We report here that this synergistic regulation of cAMP synthesis by the Gs and G13 pathways is mediated by a specific isoform of adenylyl cyclase, AC7. Furthermore, this signaling paradigm exists in several hematopoietic lineages and can be recapitulated by exogenous expression of AC7 in HEK 293 cells. Mechanistic characterization of this synergistic interaction indicates that it occurs downstream of receptor activation and it can be mediated by the alpha subunit of either G12 or G13. Our results demonstrate that AC7 is a specific downstream effector of the G12/13 pathway

LGR5 is associated with tumor aggressiveness in papillary thyroid cancer.

PurposeLeucine-rich repeat-containing G-protein-coupled receptor 5 (LGR5) is a cancer stem cell marker and a down-stream target in Wnt/β-catenin signaling. In human papillary thyroid cancer (PTC), over activation of Wnt/β-catenin has been associated with tumor aggressiveness.Patients and methodsUsing established human cell lines (TPC-1, KTC-1, Nthy-ori-3-1), we report LGR5 and R-spondin (RSPO1-3) overexpression in PTC and manipulate LGR5 and Wnt/β-catenin signaling via both pharmacologic and genetic interventions. We test the association of LGR5 tumor expression with markers of PTC aggressiveness using a Discovery Cohort (n = 26 patients) and a Validation Cohort (n = 157 patients). Lastly, we explore the association between LGR5 and the BRAFV600E mutation (n = 33 patients).ResultsOur results reveal that LGR5 and its ligand, RSPO, are overexpressed in human PTC, whereby Wnt/β-catenin signaling regulates LGR5 expression and promotes cellular migration. In two separate cohorts of patients, LGR5 and RSPO2 were associated with markers of tumor aggressiveness including: lymph node metastases, vascular invasion, increased tumor size, aggressive histology, advanced AJCC TNM stage, microscopic extra thyroidal extension, capsular invasion, and macroscopic invasion. As a biomarker, LGR5 positivity predicts lymph node metastasis with 95.5% sensitivity (95% CI 88.8%-98.7%) and 61% specificity (95% CI: 48.4%-72.4%) and has a negative predictive value (NPV) of 91.3% (95% CI 79.2%-97.5%) for lymph node metastatic disease. In human PTC, LGR5 is also strongly associated with the BRAFV600E mutation (p = 0.005).ConclusionWe conclude that overexpression of LGR5 is associated with markers of tumor aggressiveness in human PTC. LGR5 may serve as a future potential biomarker for patient risk stratification and loco regional metastases in PTC

Understanding lamin proteins and their roles in aging and cardiovascular diseases

The occurrence of cardiovascular diseases increases with age independent of other risk factors, and the percentage of senescent cells is significantly elevated in vascular cells at atherosclerotic sites. Patients with accelerated aging syndromes caused by mutant lamin A protein, a structural component in nuclear lamina, also share many similarities with normal aged people, including the propensity to develop atherosclerosis. Recent studies have revealed the accumulation of prelamin A in normal aged vascular cells, and that lamin A participated as a mechanosensitive molecule in regulating various cellular events. These findings suggest that the ectopic expression of mutant lamin A or lamin A precursor (prelamin A) not only causes defects in cell mechanics, but it also disturbs stress-induced mechanotransduction pathways involving lamin A, both of which may contribute to vascular dysregulation. This review summarizes the current understanding of how lamin proteins are involved in vascular cell during aging, with a particular focus on the effect of mechanical stresses from blood flow on nuclear lamina of endothelial cells. Related studies are clarifying the role of lamin A in the progression of atherosclerosis, which will aid in the development of potential therapies for those suffering from lamin A-associated accelerated aging syndromes

Retweet-BERT: Political Leaning Detection Using Language Features and Information Diffusion on Social Networks

Estimating the political leanings of social media users is a challenging and ever more pressing problem given the increase in social media consumption. We introduce Retweet-BERT, a simple and scalable model to estimate the political leanings of Twitter users. Retweet-BERT leverages the retweet network structure and the language used in users' profile descriptions. Our assumptions stem from patterns of networks and linguistics homophily among people who share similar ideologies. Retweet-BERT demonstrates competitive performance against other state-of-the-art baselines, achieving 96%-97% macro-F1 on two recent Twitter datasets (a COVID-19 dataset and a 2020 United States presidential elections dataset). We also perform manual validation to validate the performance of Retweet-BERT on users not in the training data. Finally, in a case study of COVID-19, we illustrate the presence of political echo chambers on Twitter and show that it exists primarily among right-leaning users. Our code is open-sourced and our data is publicly available.Comment: 11 pages, 3 figures, 4 tables. arXiv admin note: text overlap with arXiv:2103.1097

Aluminum with dispersed nanoparticles by laser additive manufacturing.

While laser-printed metals do not tend to match the mechanical properties and thermal stability of conventionally-processed metals, incorporating and dispersing nanoparticles in them should enhance their performance. However, this remains difficult to do during laser additive manufacturing. Here, we show that aluminum reinforced by nanoparticles can be deposited layer-by-layer via laser melting of nanocomposite powders, which enhance the laser absorption by almost one order of magnitude compared to pure aluminum powders. The laser printed nanocomposite delivers a yield strength of up to 1000 MPa, plasticity over 10%, and Young's modulus of approximately 200 GPa, offering one of the highest specific Young's modulus and specific yield strengths among structural metals, as well as an improved specific strength and thermal stability up to 400 °C compared to other aluminum-based materials. The improved performance is attributed to a high density of well-dispersed nanoparticles, strong interfacial bonding between nanoparticles and Al matrix, and ultrafine grain sizes