Maximum Entropy Based Lexical Reordering Model for Hierarchical Phrase-based Machine Translation

Abstract. The hierarchical phrase-based (HPB) model on the basis of a synchronous context-free grammar (SCFG) is prominent in solving global reorderings. However, the HPB model is inadequate to supervise the reordering process so that sometimes positions of dif-ferent lexicons are switched due to the incorrect SCFG rules. In this paper, we consider the order of two lexicons as a classification problem and propose a novel lexical reorder-ing model based on a maximum entropy classifier. Our model employs the word alignment and translation during the decoding process. Experimental results on the Chinese-to-English task showed that our method outperformed the baseline system in BLEU score significantly. Moreover, the translation results further proved the effectiveness of our approach

Machine Learning Model in Predicting Sarcopenia in Crohn’s Disease Based on Simple Clinical and Anthropometric Measures

Sarcopenia is associated with increased morbidity and mortality in Crohn’s disease. The present study is aimed at investigating the different diagnostic performance of different machine learning models in identifying sarcopenia in Crohn’s disease. Patients diagnosed with Crohn’s disease at our center provided clinical, anthropometric, and radiological data. The cross-sectional CT slice at L3 was used for segmentation and the calculation of body composition. The prevalence of sarcopenia was calculated, and the clinical parameters were compared. A total of 167 patients were included in the present study, of which 127 (76.0%) were male and 40 (24.0%) were female, with an average age of 36.1 ± 14.3 years old. Based on the previously defined cut-off value of sarcopenia, 118 (70.7%) patients had sarcopenia. Seven machine learning models were trained with the randomly allocated training cohort (80%) then evaluated on the validation cohort (20%). A comprehensive comparison showed that LightGBM was the most ideal diagnostic model, with an AUC of 0.933, AUCPR of 0.970, sensitivity of 72.7%, and specificity of 87.0%. The LightGBM model may facilitate a population management strategy with early identification of sarcopenia in Crohn’s disease, while providing guidance for nutritional support and an alternative surveillance modality for long-term patient follow-up

CPT1C-mediated fatty acid oxidation facilitates colorectal cancer cell proliferation and metastasis

Fatty acid oxidation (FAO) has been proven to be an accomplice in tumor progression. Carnitine palmitoyltransferase 1C (CPT1C), a rate-limiting enzyme in FAO, mainly functions to catalyze fatty acid carnitinylation and guarantee subsequent entry into the mitochondria for FAO in colorectal cancer (CRC). Gene expression data and clinical information extracted from The Cancer Genome Atlas (TCGA) database show significantly higher expression of CPT1C in patients with metastatic CRC ( P=0.005). Moreover, overexpression of CPT1C is correlated with worse relapse-free survival in CRC (HR 2.1, P=0.0006), while no statistical significance is indicated for CPT1A and CPT1B. Further experiments demonstrate that downregulation of CPT1C expression leads to a decrease in the FAO rate, suppression of cell proliferation, cell cycle arrest and repression of cell migration in CRC, whereas opposite results are obtained when CPT1C is overexpressed. Furthermore, an FAO inhibitor almost completely reverses the enhanced cell proliferation and migration induced by CPT1C overexpression. In addition, analysis of TCGA data illustrates a positive association between CPT1C expression and HIF1α level, suggesting that CPT1C is a transcriptional target of HIF1α. In conclusion, CPT1C overexpression indicates poor relapse-free survival of patients with CRC, and CPT1C is transcriptionally activated by HIF1α, thereby promoting the proliferation and migration of CRC cells

Role of Carbon Interstitials in Transition Metal Substrates on Controllable Synthesis of High-Quality Large-Area Two-Dimensional Hexagonal Boron Nitride Layers

Reliable and controllable synthesis of two-dimensional (2D) hexagonal boron nitride (h-BN) layers is highly desirable for their applications as 2D dielectric and wide bandgap semiconductors. In this work, we demonstrate that the dissolution of carbon into cobalt (Co) and nickel (Ni) substrates can facilitate the growth of h-BN and attain large-area 2D homogeneity. The morphology of the h-BN film can be controlled from 2D layer-plus-3D islands to homogeneous 2D few-layers by tuning the carbon interstitial concentration in the Co substrate through a carburization process prior to the h-BN growth step. Comprehensive characterizations were performed to evaluate structural, electrical, optical, and dielectric properties of these samples. Single-crystal h-BN flakes with an edge length of ∼600 μm were demonstrated on carburized Ni. An average breakdown electric field of 9 MV/cm was achieved for an as-grown continuous 3-layer h-BN on carburized Co. Density functional theory calculations reveal that the interstitial carbon atoms can increase the adsorption energy of B and N atoms on the Co(111) surface and decrease the diffusion activation energy and, in turn, promote the nucleation and growth of 2D h-BN