The Greenhouse Gas Emission from Portland Cement Concrete Pavement Construction in China.

This study proposes an inventory analysis method to evaluate the greenhouse gas (GHG) emissions from Portland cement concrete pavement construction, based on a case project in the west of China. The concrete pavement construction process was divided into three phases, namely raw material production, concrete manufacture and pavement onsite construction. The GHG emissions of the three phases are analyzed by a life cycle inventory method. The CO₂e is used to indicate the GHG emissions. The results show that for 1 km Portland cement concrete pavement construction, the total CO₂e is 8215.31 tons. Based on the evaluation results, the CO₂e of the raw material production phase is 7617.27 tons, accounting for 92.7% of the total GHG emissions; the CO₂e of the concrete manufacture phase is 598,033.10 kg, accounting for 7.2% of the total GHG emissions. Lastly, the CO₂e of the pavement onsite construction phase is 8396.59 kg, accounting for only 0.1% of the total GHG emissions. The main greenhouse gas is CO₂ in each phase, which accounts for more than 98% of total emissions. N₂O and CH₄ emissions are relatively insignificant

Functional conservation and divergence of Miscanthus lutarioriparius GT43 gene family in xylan biosynthesis

Background: Xylan is the most abundant un-cellulosic polysaccharides of plant cell walls. Much progress in xylan biosynthesis has been gained in the model plant species Arabidopsis. Two homologous pairs Irregular Xylem 9 (IRX9)/9L and IRX14/14L from glycosyltransferase (GT) family 43 have been proved to play crucial roles in xylan backbone biosynthesis. However, xylan biosynthesis in grass such as Miscanthus remains poorly understood

Composition, variation, expression and evolution of low-molecular-weight glutenin subunit genes in Triticum urartu

BACKGROUND: Wheat (AABBDD, 2n = 6x = 42) is a major dietary component for many populations across the world. Bread-making quality of wheat is mainly determined by glutenin subunits, but it remains challenging to elucidate the composition and variation of low-molecular-weight glutenin subunits (LMW-GS) genes, the major components for glutenin subunits in hexaploid wheat. This problem, however, can be greatly simplified by characterizing the LMW-GS genes in Triticum urartu, the A-genome donor of hexaploid wheat. In the present study, we exploited the high-throughput molecular marker system, gene cloning, proteomic methods and molecular evolutionary genetic analysis to reveal the composition, variation, expression and evolution of LMW-GS genes in a T. urartu population from the Fertile Crescent region. RESULTS: Eight LMW-GS genes, including four m-type, one s-type and three i-type, were characterized in the T. urartu population. Six or seven genes, the highest number at the Glu-A3 locus, were detected in each accession. Three i-type genes, each containing more than six allelic variants, were tightly linked because of their co-segregation in every accession. Only 2-3 allelic variants were detected for each m- and s-type gene. The m-type gene, TuA3-385, for which homologs were previously characterized only at Glu-D3 locus in common wheat and Aegilops tauschii, was detected at Glu-A3 locus in T. urartu. TuA3-460 was the first s-type gene identified at Glu-A3 locus. Proteomic analysis showed 1-4 genes, mainly i-type, expressed in individual accessions. About 62% accessions had three active i-type genes, rather than one or two in common wheat. Southeastern Turkey might be the center of origin and diversity for T. urartu due to its abundance of LMW-GS genes/genotypes. Phylogenetic reconstruction demonstrated that the characterized T. urartu might be the direct donor of the Glu-A3 locus in common wheat varieties. CONCLUSIONS: Compared with the Glu-A3 locus in common wheat, a large number of highly diverse LMW-GS genes and active genes were characterized in T. urartu, demonstrating that this progenitor might provide valuable genetic resources for LMW-GS genes to improve the quality of common wheat. The phylogenetic analysis provided molecular evidence and confirmed that T. urartu was the A-genome donor of hexaploid wheat. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12870-014-0322-3) contains supplementary material, which is available to authorized users

Wasserstein Differential Privacy

Differential privacy (DP) has achieved remarkable results in the field of privacy-preserving machine learning. However, existing DP frameworks do not satisfy all the conditions for becoming metrics, which prevents them from deriving better basic private properties and leads to exaggerated values on privacy budgets. We propose Wasserstein differential privacy (WDP), an alternative DP framework to measure the risk of privacy leakage, which satisfies the properties of symmetry and triangle inequality. We show and prove that WDP has 13 excellent properties, which can be theoretical supports for the better performance of WDP than other DP frameworks. In addition, we derive a general privacy accounting method called Wasserstein accountant, which enables WDP to be applied in stochastic gradient descent (SGD) scenarios containing sub-sampling. Experiments on basic mechanisms, compositions and deep learning show that the privacy budgets obtained by Wasserstein accountant are relatively stable and less influenced by order. Moreover, the overestimation on privacy budgets can be effectively alleviated. The code is available at https://github.com/Hifipsysta/WDP.Comment: Accepted by AAAI 202

ASXL1 interacts with the cohesin complex to maintain chromatid separation and gene expression for normal hematopoiesis

ASXL1 is frequently mutated in a spectrum of myeloid malignancies with poor prognosis. Loss of Asxl1 leads to myelodysplastic syndrome-like disease in mice; however, the underlying molecular mechanisms remain unclear. We report that ASXL1 interacts with the cohesin complex, which has been shown to guide sister chromatid segregation and regulate gene expression. Loss of Asxl1 impairs the cohesin function, as reflected by an impaired telophase chromatid disjunction in hematopoietic cells. Chromatin immunoprecipitation followed by DNA sequencing data revealed that ASXL1, RAD21, and SMC1A share 93% of genomic binding sites at promoter regions in Lin-cKit+ (LK) cells. We have shown that loss of Asxl1 reduces the genome binding of RAD21 and SMC1A and alters the expression of ASXL1/cohesin target genes in LK cells. Our study underscores the ASXL1-cohesin interaction as a novel means to maintain normal sister chromatid separation and regulate gene expression in hematopoietic cells

The Impact of Merkel Cell Polyomavirus Positivity on Prognosis of Merkel Cell Carcinoma: A Systematic Review and Meta-Analysis

INTRODUCTION: There are numerous findings over the past decade have indicated that Merkel cell carcinoma (MCC) may have two pathways of pathogenesis: one related to ultraviolet irradiation and the other to the Merkel cell polyomavirus (MCPyV). However, the predictive and clinicopathological value of MCPyV positivity in MCC patients is still debatable. This article aims to examine the most recent data regarding this issue. METHODS: The thorough literature searches were conducted in the Medline Ovid, PubMed, Web of Science, the Cochrane CENTRAL Databases, and Embase Databases until December 31, 2021. The associations between overall survival (OS), Merkel cell carcinoma-specific survival (MSS), recurrence-free survival (RFS), progression-free survival (PFS), clinicopathologic features, and MCPyV positivity were examined in our meta-analysis. RESULTS: This meta-analysis included a total of 14 studies involving 1595 patients. Our findings demonstrated a significant correlation between MCPyV positivity and improved OS (HR=0.61, 95%CI:0.39-0.94, P=0.026) and improved PFS (HR=0.61, 95% CI: 0.45-0.83, P=0.002). MCPyV positivity did not, however, appear to be associated with either MSS (HR=0.61, 95%CI: 0.28-1.32, P=0.209) or RFS (HR= 0.93, 95%CI: 0.37-2.34, P=0.873). Pooled results revealed a correlation between MCPyV positivity with gender (male vs. female, OR=0.606, 95%CI: 0.449-0.817, P=0.001), histopathological stage (AJCC I-II vs. III-IV, OR=1.636, 95%CI: 1.126-2.378, P=0.010) and primary site (head and neck vs. other sites, OR=0.409, 95%CI: 0.221-0.757, P=0.004). CONCLUSION: These results imply that MCPyV positivity may present a promising predictive biomarker for human MCC and call for further study

Model and Evaluation: Towards Fairness in Multilingual Text Classification

Recently, more and more research has focused on addressing bias in text classification models. However, existing research mainly focuses on the fairness of monolingual text classification models, and research on fairness for multilingual text classification is still very limited. In this paper, we focus on the task of multilingual text classification and propose a debiasing framework for multilingual text classification based on contrastive learning. Our proposed method does not rely on any external language resources and can be extended to any other languages. The model contains four modules: multilingual text representation module, language fusion module, text debiasing module, and text classification module. The multilingual text representation module uses a multilingual pre-trained language model to represent the text, the language fusion module makes the semantic spaces of different languages tend to be consistent through contrastive learning, and the text debiasing module uses contrastive learning to make the model unable to identify sensitive attributes' information. The text classification module completes the basic tasks of multilingual text classification. In addition, the existing research on the fairness of multilingual text classification is relatively simple in the evaluation mode. The evaluation method of fairness is the same as the monolingual equality difference evaluation method, that is, the evaluation is performed on a single language. We propose a multi-dimensional fairness evaluation framework for multilingual text classification, which evaluates the model's monolingual equality difference, multilingual equality difference, multilingual equality performance difference, and destructiveness of the fairness strategy. We hope that our work can provide a more general debiasing method and a more comprehensive evaluation framework for multilingual text fairness tasks

An Effective Deployment of Contrastive Learning in Multi-label Text Classification

The effectiveness of contrastive learning technology in natural language processing tasks is yet to be explored and analyzed. How to construct positive and negative samples correctly and reasonably is the core challenge of contrastive learning. It is even harder to discover contrastive objects in multi-label text classification tasks. There are very few contrastive losses proposed previously. In this paper, we investigate the problem from a different angle by proposing five novel contrastive losses for multi-label text classification tasks. These are Strict Contrastive Loss (SCL), Intra-label Contrastive Loss (ICL), Jaccard Similarity Contrastive Loss (JSCL), Jaccard Similarity Probability Contrastive Loss (JSPCL), and Stepwise Label Contrastive Loss (SLCL). We explore the effectiveness of contrastive learning for multi-label text classification tasks by the employment of these novel losses and provide a set of baseline models for deploying contrastive learning techniques on specific tasks. We further perform an interpretable analysis of our approach to show how different components of contrastive learning losses play their roles. The experimental results show that our proposed contrastive losses can bring improvement to multi-label text classification tasks. Our work also explores how contrastive learning should be adapted for multi-label text classification tasks.Comment: Accepted by ACL-Findings 2023, 13 page

CL-XABSA: Contrastive Learning for Cross-lingual Aspect-based Sentiment Analysis

As an extensive research in the field of Natural language processing (NLP), aspect-based sentiment analysis (ABSA) is the task of predicting the sentiment expressed in a text relative to the corresponding aspect. Unfortunately, most languages lack of sufficient annotation resources, thus more and more recent researchers focus on cross-lingual aspect-based sentiment analysis (XABSA). However, most recent researches only concentrate on cross-lingual data alignment instead of model alignment. To this end, we propose a novel framework, CL-XABSA: Contrastive Learning for Cross-lingual Aspect-Based Sentiment Analysis. Specifically, we design two contrastive strategies, token level contrastive learning of token embeddings (TL-CTE) and sentiment level contrastive learning of token embeddings (SL-CTE), to regularize the semantic space of source and target language to be more uniform. Since our framework can receive datasets in multiple languages during training, our framework can be adapted not only for XABSA task, but also for multilingual aspect-based sentiment analysis (MABSA). To further improve the performance of our model, we perform knowledge distillation technology leveraging data from unlabeled target language. In the distillation XABSA task, we further explore the comparative effectiveness of different data (source dataset, translated dataset, and code-switched dataset). The results demonstrate that the proposed method has a certain improvement in the three tasks of XABSA, distillation XABSA and MABSA. For reproducibility, our code for this paper is available at https://github.com/GKLMIP/CL-XABSA

FlattenQuant: Breaking Through the Inference Compute-bound for Large Language Models with Per-tensor Quantization

Large language models (LLMs) have demonstrated state-of-the-art performance across various tasks. However, the latency of inference and the large GPU memory consumption of LLMs restrict their deployment performance. Recently, there have been some efficient attempts to quantize LLMs, yet inference with large batch size or long sequence still has the issue of being compute-bound. Fine-grained quantization methods have showcased their proficiency in achieving low-bit quantization for LLMs, while requiring FP16 data type for linear layer computations, which is time-consuming when dealing with large batch size or long sequence. In this paper, we introduce a method called FlattenQuant, which significantly reduces the maximum value of the tensor by flattening the large channels in the tensor, to achieve low bit per-tensor quantization with minimal accuracy loss. Our experiments show that FlattenQuant can directly use 4 bits to achieve 48.29% of the linear layer calculation in LLMs, with the remaining layers using 8 bits. The 4-bit matrix multiplication introduced in the FlattenQuant method can effectively address the compute-bound caused by large matrix calculation. Our work achieves up to 2$\times$ speedup and 2.3$\times$ memory reduction for LLMs with negligible loss in accuracy