The Injury and Therapy of Reactive Oxygen Species in Intracerebral Hemorrhage Looking at Mitochondria

Intracerebral hemorrhage is an emerging major health problem often resulting in death or disability. Reactive oxygen species (ROS) have been identified as one of the major damaging factors in ischemic stroke. However, there is less discussion about ROS in hemorrhage stroke. Metabolic products of hemoglobin, excitatory amino acids, and inflammatory cells are all sources of ROS, and ROS harm the central nervous system through cell death and structural damage, especially disruption of the blood-brain barrier. We have considered the antioxidant system of the CNS itself and the drugs aiming to decrease ROS after ICH, and we find that mitochondria are key players in all of these aspects. Moreover, when the mitochondrial permeability transition pore opens, ROS-induced ROS release, which leads to extensive liberation of ROS and mitochondrial failure, occurs. Therefore, the mitochondrion may be a significant target for elucidating the problem of ROS in ICH; however, additional experimental support is required

CodeTransOcean: A Comprehensive Multilingual Benchmark for Code Translation

Recent code translation techniques exploit neural machine translation models to translate source code from one programming language to another to satisfy production compatibility or to improve efficiency of codebase maintenance. Most existing code translation datasets only focus on a single pair of popular programming languages. To advance research on code translation and meet diverse requirements of real-world applications, we construct CodeTransOcean, a large-scale comprehensive benchmark that supports the largest variety of programming languages for code translation. CodeTransOcean consists of three novel multilingual datasets, namely, MultilingualTrans supporting translations between multiple popular programming languages, NicheTrans for translating between niche programming languages and popular ones, and LLMTrans for evaluating executability of translated code by large language models (LLMs). CodeTransOcean also includes a novel cross-framework dataset, DLTrans, for translating deep learning code across different frameworks. We develop multilingual modeling approaches for code translation and demonstrate their great potential in improving the translation quality of both low-resource and high-resource language pairs and boosting the training efficiency. We also propose a novel evaluation metric Debugging Success Rate@K for program-level code translation. Last but not least, we evaluate LLM ChatGPT on our datasets and investigate its potential for fuzzy execution predictions. We build baselines for CodeTransOcean and analyze challenges of code translation for guiding future research. The CodeTransOcean datasets and code are publicly available at https://github.com/WeixiangYAN/CodeTransOcean.Comment: Accepted by Findings of EMNLP 202

Highly Dispersed Rh/NbOx Invoking High Catalytic Performances for the Valorization of Lignin Monophenols and Lignin Oil into Aromatics

As fossil fuels are constantly depleted, valorization of lignocellulosic biomass into valuable aromatic compounds is of great significance but exceedingly challenging. In this work, the structure and catalytic performance of various Rh/Nb2O5 catalysts were studied in detail via the catalytic hydrodeoxygenation of a representative lignin monophenol compound 2-methoxy-4-propylphenol. The best catalytic performance was obtained over Rh/Nb2O5-400 (Nb2O5 calcined at 400 °C) with an exceptional 98% yield of propylbenzene under 0.5 MPa H2, which was mainly due to the cooperation between highly dispersed Rh metals and NbOx species, in which Rh was responsible for dissociation of H2 and NbOx for breaking of C−O bonds at the metal−support interface. Besides, the lignin oil obtained in depolymerization of raw pine wood was directly used as the substrate in the catalytic hydrodeoxygenation reaction over the Rh/Nb2O5-400 catalyst under 0.5 MPa H2. Encouragingly, the liquid products were identified and found that lignin oil was completely converted into C6−C10 hydrocarbons (\u3e99% selectivity) with an 80.1 mol % yield of aromatics. The results achieved in this work highlighted that high-value utilization of lignocellulosic biomass feedstocks to produce aromatic chemicals and liquid fuels could be achieved over Rh/Nb2O5 under a low hydrogen pressure

Enhancing Generation through Summarization Duality and Explicit Outline Control

Automatically open-ended long text generation poses significant challenges due to semantic incoherence and plot implausibility. Previous works usually alleviate this problem through outlines in the form of short phrases or abstractive signals by designing unsupervised tasks, which tend to be unstable and weakly interpretable. Assuming that a summary serves as a mature outline, we introduce a two-stage, summary-enhanced outline supervised generation framework. This framework leverages the dual characteristics of the summarization task to improve outline prediction, resulting in more explicit and plausible outlines. Furthermore, we identify an underutilization issue in outline-based generation with both standard pretrained language models (e.g., GPT-2, BART) and large language models (e.g., Vicuna, ChatGPT). To address this, we propose a novel explicit outline control method for more effective utilization of generated outlines.Comment: 14 page

A critical review of a computational fluid dynamics (CFD)-based explosion numerical analysis of offshore facilities

In oil and gas industries, the explosive hazards receive lots of attention to achieve a safety design of relevant facilities. As a part of the robust design for offshore structures, an explosion risk analysis is normally conducted to examine the potential hazards and the influence of them on structural members in a real explosion situation. Explosion accidents in the oil and gas industries are related to lots of parameters through complex interaction. Hence, lots of research and industrial projects have been carried out to understand physical mechanism of explosion accidents. Computational fluid dynamics-based explosion risk analysis method is frequently used to identify contributing factors and their interactions to understand such accidents. It is an effective method when modelled explosion phenomena including detailed geometrical features. This study presents a detailed review and analysis of Computational Fluid Dynamics-based explosion risk analysis that used in the offshore industries. The underlying issues of this method and current limitation are identified and analysed. This study also reviewed potential preventative measures to eliminate such limitation. Additionally, this study proposes the prospective research topic regarding computational fluid dynamics-based explosion risk analysis

Frequent copy number variations of PI3K/AKT pathway and aberrant protein expressions of PI3K subunits are associated with inferior survival in diffuse large B cell lymphoma

BACKGROUND: It has been reported that the PI3K/AKT signaling pathway is activated in diffuse large B-cell lymphoma (DLBCL), PI3K constitutive activation plays a crucial role in PI3K/AKT pathway. However, the copy number variations (CNVs) of PI3K subunits on gene level remain unknown in DLBCL. Therefore, the aim of the study is to investigate the CNV of PI3K subunits and their relationship with clinicopathological features exploring the possible mechanism underlying of PI3K activation in DLBCL. METHODS: CNV of 12 genes in the PI3K/AKT pathway was detected by NanoString nCounter in 60 de novo DLBCLs and 10 reactive hyperplasia specimens as controls. Meanwhile, immunohistochemistry (IHC) was performed to examine the expression of p110α, p110β, p110γ, p110δ, and pAKT on DLBCL tissue microarrays. RESULTS: All PI3K and AKT subunits, except for PIK3R1, had various CNVs in the form of copy number amplifications and copy number losses. Their rates were in the range of 8.3–20.0%. Of them PIK3CA and PIK3CB gene CNVs were significantly associated with decreased overall survival (P = 0.029 and P = 0.019, respectively). IHC showed that the frequency of strong positive expression of p110α, p110β, p110γ, and p110δ were 26.7%, 25.0%, 18.3%, and 25.0% respectively, and they were found to be associated with decreased survival (P = 0.022, P = 0.015, P = 0.015, and P = 0.008, respectively). Expression of p110α was not only significantly associated with CNVs of PIK3CA (P = 0.002) but also positively correlated with strong positive expression of pAKT (P = 0.026). CONCLUSIONS: CNV of PIK3CA is highly associated with aberrant p110α protein expression and subsequent activation of PI3K/AKT pathway. CNVs of PIK3CA and PIK3CB, and aberrant protein expression of p110 isoforms are of great important value for predicting inferior prognosis in DLBCL. Frequent CNVs of PI3K/AKT subunits may play an important role in the tumorigenesis of DLBCL

Automated diagnosis of pancreatic mucinous and serous cystic neoplasms with modality-fusion deep neural network using multi-modality MRIs

BackgroundPancreatic cystic neoplasms are increasingly diagnosed with the development of medical imaging technology and people’s self-care awareness. However, two of their sub-types, serous cystic neoplasms (SCN) and mucinous cystic neoplasms (MCN), are often misclassified from each other. Because SCN is primarily benign and MCN has a high rate of malignant transformation. Distinguishing SCN and MCN is challenging and essential.PurposeMRIs have many different modalities, complete with SCN and MCN diagnosis information. With the help of an artificial intelligence-based algorithm, we aimed to propose a multi-modal hybrid deep learning network that can efficiently diagnose SCN and MCN using multi-modality MRIs.MethodsA cross-modal feature fusion structure was innovatively designed, combining features of seven modalities to realize the classification of SCN and MCN. 69 Patients with multi-modalities of MRIs were included, and experiments showed performances of every modality.ResultsThe proposed method with the optimized settings outperformed all other techniques and human radiologists with high accuracy of 75.07% and an AUC of 82.77%. Besides, the proposed disentanglement method outperformed other fusion methods, and delayed contrast-enhanced T1-weighted MRIs proved most valuable in diagnosing SCN and MCN.ConclusionsThrough the use of a contemporary artificial intelligence algorithm, physicians can attain high performance in the complex challenge of diagnosing SCN and MCN, surpassing human radiologists to a significant degree

Mapping the Galactic disk with the LAMOST and Gaia Red clump sample: I: precise distances, masses, ages and 3D velocities of ∼\sim 140000 red clump stars

We present a sample of $\sim$ 140,000 primary red clump (RC) stars of spectral signal-to-noise ratios higher than 20 from the LAMOST Galactic spectroscopic surveys, selected based on their positions in the metallicity-dependent effective temperature--surface gravity and color--metallicity diagrams, supervised by high-quality $Kepler$ asteroseismology data. The stellar masses and ages of those stars are further determined from the LAMOST spectra, using the Kernel Principal Component Analysis method, trained with thousands of RCs in the LAMOST-$Kepler$ fields with accurate asteroseismic mass measurements. The purity and completeness of our primary RC sample are generally higher than 80 per cent. For the mass and age, a variety of tests show typical uncertainties of 15 and 30 per cent, respectively. Using over ten thousand primary RCs with accurate distance measurements from the parallaxes of Gaia DR2, we re-calibrate the $K_{\rm s}$ absolute magnitudes of primary RCs by, for the first time, considering both the metallicity and age dependencies. With the the new calibration, distances are derived for all the primary RCs, with a typical uncertainty of 5--10 per cent, even better than the values yielded by the Gaia parallax measurements for stars beyond 3--4 kpc. The sample covers a significant volume of the Galactic disk of $4 \leq R \leq 16$ kpc, $|Z| \leq 5$ kpc, and $-20 \leq \phi \leq 50^{\circ}$. Stellar atmospheric parameters, line-of-sight velocities and elemental abundances derived from the LAMOST spectra and proper motions of Gaia DR2 are also provided for the sample stars. Finally, the selection function of the sample is carefully evaluated in the color-magnitude plane for different sky areas. The sample is publicly available.Comment: 16 pages, 19 figures, 3 tables, accepted for publication in ApJ