Diversion Mechanism of the Criminal Produces in China

The establishment and development of the diversion mechanism of criminal procedure has become a global trend due to its significant value in improving judicial efficiency and promoting justice. The diversion of criminal produces embodies both depenalization and individualization of punishment. China has already applied such diversion mechanism in prosecution, trial, criminal reconciliation, and juvenile cases; however, there are still limitations and the mechanisms can be further improved

Fast Neighbor Discovery for Wireless Ad Hoc Network with Successive Interference Cancellation

Neighbor discovery (ND) is a key step in wireless ad hoc network, which directly affects the efficiency of wireless networking. Improving the speed of ND has always been the goal of ND algorithms. The classical ND algorithms lose packets due to the collision of multiple packets, which greatly affects the speed of the ND algorithms. Traditional methods detect packet collision and implement retransmission when encountering packet loss. However, they does not solve the packet collision problem and the performance improvement of ND algorithms is limited. In this paper, the successive interference cancellation (SIC) technology is introduced into the ND algorithms to unpack multiple collision packets by distinguishing multiple packets in the power domain. Besides, the multi-packet reception (MPR) is further applied to reduce the probability of packet collision by distinguishing multiple received packets, thus further improving the speed of ND algorithms. Six ND algorithms, namely completely random algorithm (CRA), CRA based on SIC (CRA-SIC), CRA based on SIC and MPR (CRA-SIC-MPR), scan-based algorithm (SBA), SBA based on SIC (SBA-SIC), and SBA based on SIC and MPR (SBA-SIC-MPR), are theoretically analyzed and verified by simulation. The simulation results show that SIC and MPR reduce the ND time of SBA by 69.02% and CRA by 66.03% averagely.Comment: 16 pages, 16 figure

LeCaRDv2: A Large-Scale Chinese Legal Case Retrieval Dataset

As an important component of intelligent legal systems, legal case retrieval plays a critical role in ensuring judicial justice and fairness. However, the development of legal case retrieval technologies in the Chinese legal system is restricted by three problems in existing datasets: limited data size, narrow definitions of legal relevance, and naive candidate pooling strategies used in data sampling. To alleviate these issues, we introduce LeCaRDv2, a large-scale Legal Case Retrieval Dataset (version 2). It consists of 800 queries and 55,192 candidates extracted from 4.3 million criminal case documents. To the best of our knowledge, LeCaRDv2 is one of the largest Chinese legal case retrieval datasets, providing extensive coverage of criminal charges. Additionally, we enrich the existing relevance criteria by considering three key aspects: characterization, penalty, procedure. This comprehensive criteria enriches the dataset and may provides a more holistic perspective. Furthermore, we propose a two-level candidate set pooling strategy that effectively identify potential candidates for each query case. It's important to note that all cases in the dataset have been annotated by multiple legal experts specializing in criminal law. Their expertise ensures the accuracy and reliability of the annotations. We evaluate several state-of-the-art retrieval models at LeCaRDv2, demonstrating that there is still significant room for improvement in legal case retrieval. The details of LeCaRDv2 can be found at the anonymous website https://github.com/anonymous1113243/LeCaRDv2

An Intent Taxonomy of Legal Case Retrieval

Legal case retrieval is a special Information Retrieval~(IR) task focusing on legal case documents. Depending on the downstream tasks of the retrieved case documents, users' information needs in legal case retrieval could be significantly different from those in Web search and traditional ad-hoc retrieval tasks. While there are several studies that retrieve legal cases based on text similarity, the underlying search intents of legal retrieval users, as shown in this paper, are more complicated than that yet mostly unexplored. To this end, we present a novel hierarchical intent taxonomy of legal case retrieval. It consists of five intent types categorized by three criteria, i.e., search for Particular Case(s), Characterization, Penalty, Procedure, and Interest. The taxonomy was constructed transparently and evaluated extensively through interviews, editorial user studies, and query log analysis. Through a laboratory user study, we reveal significant differences in user behavior and satisfaction under different search intents in legal case retrieval. Furthermore, we apply the proposed taxonomy to various downstream legal retrieval tasks, e.g., result ranking and satisfaction prediction, and demonstrate its effectiveness. Our work provides important insights into the understanding of user intents in legal case retrieval and potentially leads to better retrieval techniques in the legal domain, such as intent-aware ranking strategies and evaluation methodologies.Comment: 28 pages, work in proces

SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval

Legal case retrieval, which aims to find relevant cases for a query case, plays a core role in the intelligent legal system. Despite the success that pre-training has achieved in ad-hoc retrieval tasks, effective pre-training strategies for legal case retrieval remain to be explored. Compared with general documents, legal case documents are typically long text sequences with intrinsic logical structures. However, most existing language models have difficulty understanding the long-distance dependencies between different structures. Moreover, in contrast to the general retrieval, the relevance in the legal domain is sensitive to key legal elements. Even subtle differences in key legal elements can significantly affect the judgement of relevance. However, existing pre-trained language models designed for general purposes have not been equipped to handle legal elements. To address these issues, in this paper, we propose SAILER, a new Structure-Aware pre-traIned language model for LEgal case Retrieval. It is highlighted in the following three aspects: (1) SAILER fully utilizes the structural information contained in legal case documents and pays more attention to key legal elements, similar to how legal experts browse legal case documents. (2) SAILER employs an asymmetric encoder-decoder architecture to integrate several different pre-training objectives. In this way, rich semantic information across tasks is encoded into dense vectors. (3) SAILER has powerful discriminative ability, even without any legal annotation data. It can distinguish legal cases with different charges accurately. Extensive experiments over publicly available legal benchmarks demonstrate that our approach can significantly outperform previous state-of-the-art methods in legal case retrieval.Comment: 10 pages, accepted by SIGIR 202

Chemoresistance to gemcitabine in hepatoma cells induces epithelial-mesenchymal transition and involves activation of PDGF-D pathway

Hepatocellular carcinoma (HCC) is one of the common malignances in the world and has high mortality in part due to development of acquired drug resistance. Therefore, it is urgent to investigate the molecular mechanism of drug resistance in HCC. To explore the underlying mechanism of drug resistance in HCC, we developed gemcitabine-resistant (GR) HCC cells. We used multiple methods to achieve our goal including RT-PCR, Western blotting analysis, transfection, Wound-healing assay, migration and invasion assay. We observed that gemcitabine-resistant cells acquired epithelial-mesenchymal transition (EMT) phenotype. Moreover, we found that PDGF-D is highly expressed in GR cells. Furthermore, down-regulation of PDGF-D in GR cells led to partial reversal of the EMT phenotype. Our findings demonstrated that targeting PDGF-D could be a novel strategy to overcome gemcitabine resistance in HCC

The long noncoding RNA LINC15957 regulates anthocyanin accumulation in radish

Radish (Raphanus sativus L.) is an important root vegetable crop belonging to the Brassicaceae family. Anthocyanin rich radish varieties are popular among consumers because of their bright color and high nutritional value. However, the underlying molecular mechanism responsible for skin and flesh induce anthocyanin biosynthesis in transient overexpression, gene silencing and transcriptome sequencing were used to verify its function in radish anthocyanin accumulation, radish remains unclear. Here, we identified a long noncoding RNA LINC15957, overexpression of LINC15957 was significantly increased anthocyanin accumulation in radish leaves, and the expression levels of structural genes related to anthocyanin biosynthesis were also significantly increased. Anthocyanin accumulation and expression levels of anthocyanin biosynthesis genes were significantly reduced in silenced LINC15957 flesh when compared with control. By the transcriptome sequencing of the overexpressed LINC15957 plants and the control, 5,772 differentially expressed genes were identified. A total of 3,849 differentially expressed transcription factors were identified, of which MYB, bHLH, WD40, bZIP, ERF, WRKY and MATE were detected and differentially expressed in the overexpressed LINC15957 plants. KEGG enrichment analysis revealed the genes were significant enriched in tyrosine, L-Phenylalanine, tryptophan, phenylpropanol, and flavonoid biosynthesis. RT-qPCR analysis showed that 8 differentially expressed genes (DEGs) were differentially expressed in LINC15957-overexpressed plants. These results suggested that LINC15957 involved in regulate anthocyanin accumulation and provide abundant data to investigate the genes regulate anthocyanin biosynthesis in radish

Multiparametric MRI radiomics fusion for predicting the response and shrinkage pattern to neoadjuvant chemotherapy in breast cancer

PurposeDuring neoadjuvant chemotherapy (NACT), breast tumor morphological and vascular characteristics are usually changed. This study aimed to evaluate the tumor shrinkage pattern and response to NACT by preoperative multiparametric magnetic resonance imaging (MRI), including dynamic contrast-enhanced MRI (DCE-MRI), diffuse weighted imaging (DWI) and T2 weighted imaging (T2WI).MethodIn this retrospective analysis, female patients with unilateral unifocal primary breast cancer were included for predicting tumor pathologic/clinical response to NACT (n=216, development set, n=151 and validation set, n=65) and for discriminating the tumor concentric shrinkage (CS) pattern from the others (n=193; development set, n=135 and validation set, n=58). Radiomic features (n=102) of first-order statistical, morphological and textural features were calculated on tumors from the multiparametric MRI. Single- and multiparametric image-based features were assessed separately and were further combined to feed into a random forest-based predictive model. The predictive model was trained in the testing set and assessed on the testing dataset with an area under the curve (AUC). Molecular subtype information and radiomic features were fused to enhance the predictive performance.ResultsThe DCE-MRI-based model showed higher performance (AUCs of 0.919, 0.830 and 0.825 for tumor pathologic response, clinical response and tumor shrinkage patterns, respectively) than either the T2WI or the ADC image-based model. An increased prediction performance was achieved by a model with multiparametric MRI radiomic feature fusion.ConclusionsAll these results demonstrated that multiparametric MRI features and their information fusion could be of important clinical value for the preoperative prediction of treatment response and shrinkage pattern

Identification and expression analysis of EDR1-like genes in tobacco (Nicotiana tabacum) in response to Golovinomyces orontii

ENHANCED DISEASE RESISTANCE1 (EDR1) encodes a Raf-like mitogen-activated protein kinase, and it acts as a negative regulator of disease resistance and ethylene-induced senescence. Mutations in the EDR1 gene can enhance resistance to powdery mildew both in monocotyledonous and dicotyledonous plants. However, little is known about EDR1-like gene members from a genome-wide perspective in plants. In this study, the tobacco (Nicotiana tabacum) EDR1-like gene family was first systematically analyzed. We identified 19 EDR1-like genes in tobacco, and compared them to those from Arabidopsis, tomato and rice. Phylogenetic analyses divided the EDR1-like gene family into six clades, among them monocot and dicot plants were respectively divided into two sub-clades. NtEDR1-1A and NtEDR1-1B were classified into clade I in which the other members have been reported to negatively regulate plant resistance to powdery mildew. The expression patterns of tobacco EDR1-like genes were analyzed after plants were challenged by Golovinomyces orontii, and showed that several other EDR1-like genes were induced after infection, as well as NtEDR1-1A and NtEDR1-1B. Expression analysis showed that NtEDR1-13 and NtEDR1-16 had exclusively abundant expression patterns in roots and leaves, respectively, and the remaining NtEDR1-like members were actively expressed in most of the tissue/organ samples investigated. Our findings will contribute to further study of the physiological functions of EDR1-like genes in tobacco

Functional polymorphisms of the APOA1/C3/A4/A5-ZPR1-BUD13 gene cluster are associated with dyslipidemia in a sex-specific pattern

Background Dyslipidemia contributes to the risk of many diseases, including stroke, cardiovascular disease and metabolic-related diseases. Previous studies have indicated that single nucleotide polymorphisms (SNPs) are associated with different levels of serum lipid. Therefore, this study explored the relationship between the APOA1/C3/A4/A5-ZPR1-BUD13 gene cluster gene polymorphisms and dyslipidemia in the total sample population and stratified by genders in a northeast Chinese population. Methods A total of 3,850 participants from Jilin Province, China, were enrolled in our study, and their serum lipid levels were measured. Six functional SNPs (APOA1 rs5072, APOC3 rs5128, APOA4 rs5104, APOA5 rs651821, ZPR1 rs2075294 and BUD13 rs10488698) were genotyped using polymerase chain reaction and MALDI-TOF-MS. Logistic regression analysis was performed to explore the relationship of APOA1/C3/A4/A5-ZPR1-BUD13 gene cluster gene polymorphisms with dyslipidemia. Linkage disequilibrium and haplotype analyses were performed with the SNPStats program and Haploview software. Results All SNPs conformed to Hardy–Weinberg equilibrium. Logistic regression analysis revealed that rs5072, rs5128 and rs651821 were associated with hypertriglyceridemia, rs5104 and rs651821 were associated with low-HDL cholesterolemia in overall group. rs651821 was associated with hypertriglyceridemia and low-HDL cholesterolemia in both the male and female group. However, among females, rs5072 was observed to be associated with hypertriglyceridemia. Haplotype analysis showed that haplotypes TGCCGC and CAGCGC were associated with dyslipidemia in the overall, male and female groups. Conclusion SNPs in the APOA1/C3/A4/A5-ZPR1-BUD13 gene cluster were associated with dyslipidemia. Furthermore, the association of APOA1 rs5072 in this gene cluster with dyslipidemia differed between genders; thus, additional studies are needed to confirm this conclusion, and the mechanisms underlying these results warrant further exploration