AIDA: Legal Judgment Predictions for Non-Professional Fact Descriptions via Partial-and-Imbalanced Domain Adaptation

In this paper, we study the problem of legal domain adaptation problem from an imbalanced source domain to a partial target domain. The task aims to improve legal judgment predictions for non-professional fact descriptions. We formulate this task as a partial-and-imbalanced domain adaptation problem. Though deep domain adaptation has achieved cutting-edge performance in many unsupervised domain adaptation tasks. However, due to the negative transfer of samples in non-shared classes, it is hard for current domain adaptation model to solve the partial-and-imbalanced transfer problem. In this work, we explore large-scale non-shared but related classes data in the source domain with a hierarchy weighting adaptation to tackle this limitation. We propose to embed a novel pArtial Imbalanced Domain Adaptation technique (AIDA) in the deep learning model, which can jointly borrow sibling knowledge from non-shared classes to shared classes in the source domain and further transfer the shared classes knowledge from the source domain to the target domain. Experimental results show that our model outperforms the state-of-the-art algorithms.Comment: 13 pages, 15 figure

Study ofof weld morphology on thin Hastelloy C-276 sheet of Study weld morphology on thin Hastelloy C-276 sheet of pulsed laser welding pulsed laser welding

AbstractIn this paper, it was indicated that the laser welding was well suitable to joining of thin Hastelloy C-276 sheet (0.5 mm thickness), and also the fine grain were observed in welding zone with invisible HAZ (heat affected zone). In addition, the smooth weld joint could be controlled by means of the laser parameter adjustment. On the other hand, it’s proposed that Ni–Cr–Co–Mo and austenite CFe15.1 cubic face-centered crystal structure should be existed in as-received and welding samples, as well as the cause of FWHM (Full Width at Half Maximum) widened and peak offset of joined samples were analyzed

Dynamics of Gut Microbiome in Giant Panda Cubs Reveal Transitional Microbes and Pathways in Early Life

Adult giant pandas (Ailuropoda melanoleuca) express transitional characteristics in that they consume bamboos, despite their carnivore-like digestive tracts. Their genome contains no cellulolytic enzymes; therefore, understanding the development of the giant panda gut microbiome, especially in early life, is important for decoding the rules underlying gut microbial formation, inheritance and dietary transitions. With deep metagenomic sequencing, we investigated the gut microbiomes of two newborn giant panda brothers and their parents living in Macao, China, from 2016 to 2017. Both giant panda cubs exhibited progressive increases in gut microbial richness during growth, particularly from the 6th month after birth. Enterobacteriaceae dominated the gut microbial compositions in both adult giant pandas and cubs. A total of 583 co-abundance genes (CAGs) and about 79 metagenomic species (MGS) from bacteria or viruses displayed significant changes with age. Seven genera (Shewanella, Oblitimonas, Helicobacter, Haemophilus, Aeromonas, Listeria, and Fusobacterium) showed great importance with respect to gut microbial structural determination in the nursing stage of giant panda cubs. Furthermore, 10 orthologous gene functions and 44 pathways showed significant changes with age. Of the significant pathways, 16 from Escherichia, Klebsiella, Propionibacterium, Lactobacillus, and Lactococcus displayed marked differences between parents and their cubs at birth, while 29 pathways from Escherichia, Campylobacter and Lactobacillus exhibited significant increase in cubs from 6 to 9 months of age. In addition, oxidoreductases, transferases, and hydrolases dominated the significantly changed gut microbial enzymes during the growth of giant panda cubs, while few of them were involved in cellulose degradation. The findings indicated diet-stimulated gut microbiome transitions and the important role of Enterobacteriaceae in the guts of giant panda in early life

Oocyte stage-specific effects of MTOR determine granulosa cell fate and oocyte quality in mice.

MTOR (mechanistic target of rapamycin) is a widely recognized integrator of signals and pathways key for cellular metabolism, proliferation, and differentiation. Here we show that conditional knockout (cKO) of Mtor in either primordial or growing oocytes caused infertility but differentially affected oocyte quality, granulosa cell fate, and follicular development. cKO of Mtor in nongrowing primordial oocytes caused defective follicular development leading to progressive degeneration of oocytes and loss of granulosa cell identity coincident with the acquisition of immature Sertoli cell-like characteristics. Although Mtor was deleted at the primordial oocyte stage, DNA damage accumulated in oocytes during their later growth, and there was a marked alteration of the transcriptome in the few oocytes that achieved the fully grown stage. Although oocyte quality and fertility were also compromised when Mtor was deleted after oocytes had begun to grow, these occurred without overtly affecting folliculogenesis or the oocyte transcriptome. Nevertheless, there was a significant change in a cohort of proteins in mature oocytes. In particular, down-regulation of PRC1 (protein regulator of cytokinesis 1) impaired completion of the first meiotic division. Therefore, MTOR-dependent pathways in primordial or growing oocytes differentially affected downstream processes including follicular development, sex-specific identity of early granulosa cells, maintenance of oocyte genome integrity, oocyte gene expression, meiosis, and preimplantation developmental competence. Proc Natl Acad Sci U S A 2018 Jun 5; 115(23):E5326-E5333

Fractured morphology of femoral head associated with subsequent femoral neck fracture: Injury analyses of 2D and 3D models of femoral head fractures with computed tomography

Background: The injury of femoral head varies among femoral head fractures (FHFs). In addition, the injury degree of the femoral head is a significant predictor of femoral neck fracture (FNF) incidence in patients with FHFs. However, the exact measurement methods have yet been clearly defined based on injury models of FHFs. This study aimed to design a new measurement for the injury degree of the femoral head on 2D and 3D models with computed tomography (CT) images and investigate its association with FHFs with FNF.Methods: A consecutive series of 209 patients with FHFs was assessed regarding patient characteristics, CT images, and rate of FNF. New parameters for injury degree of femoral head, including percentage of maximum defect length (PMDL) in the 2D CT model and percentage of fracture area (PFA) in the 3D CT-reconstruction model, were respectively measured. Four 2D parameters included PMDLs in the coronal, cross-sectional and sagittal plane and average PMDL across all three planes. Reliability tests for all parameters were evaluated in 100 randomly selected patients. The PMDL with better reliability and areas under curves (AUCs) was finally defined as the 2D parameter. Factors associated with FNF were determined by binary logistic regression analysis. The sensitivity, specificity, likelihood ratios, and positive and negative predictive values for different cut-off values of the 2D and 3D parameters were employed to test the diagnostic accuracy for FNF prediction.Results: Intra- and inter-class coefficients for all parameters were ≥0.887. AUCs of all parameters ranged from 0.719 to 0.929 (p < 0.05). The average PMDL across all three planes was defined as the 2D parameter. The results of logistic regression analysis showed that average PMDL across all three planes and PFA were the significant predictors of FNF (p < 0.05). The cutoff values of the average PMDL across all three planes and PFA were 91.65% and 29.68%. The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, predictive positive value and negative predictive value of 2D (3D) parameters were 91.7% (83.3%), 93.4% (58.4%), 13.8 (2.0), 0.09 (0.29), 45.83% (10.87%), and 99.46% (98.29%).Conclusion: The new measurement on 2D and 3D injury models with CT has been established to assess the fracture risk of femoral neck in patients with FHFs in the clinic practice. 2D and 3D parameters in FHFs were a feasible adjunctive diagnostic tool in identifying FNFs. In addition, this finding might also provide a theoretic basis for the investigation of the convenient digital-model in complex injury analysis

Baiji genomes reveal low genetic variability and new insights into secondary aquatic adaptations

The baiji, or Yangtze River dolphin (Lipotes vexillifer), is a flagship species for the conservation of aquatic animals and ecosystems in the Yangtze River of China; however, this species has now been recognized as functionally extinct. Here we report a high-quality draft genome and three re-sequenced genomes of L. vexillifer using Illumina short-read sequencing technology. Comparative genomic analyses reveal that cetaceans have a slow molecular clock and molecular adaptations to their aquatic lifestyle. We also find a significantly lower number of heterozygous single nucleotide polymorphisms in the baiji compared to all other mammalian genomes reported thus far. A reconstruction of the demographic history of the baiji indicates that a bottleneck occurred near the end of the last deglaciation, a time coinciding with a rapid decrease in temperature and the rise of eustatic sea level

Search for the decay J/ψ→γ+invisibleJ/\psi\to\gamma + \rm {invisible}

We search for $J/\psi$ radiative decays into a weakly interacting neutral particle, namely an invisible particle, using the $J/\psi$ produced through the process $\psi(3686)\to\pi^+\pi^-J/\psi$ in a data sample of $(448.1\pm2.9)\times 10^6$ $\psi(3686)$ decays collected by the BESIII detector at BEPCII. No significant signal is observed. Using a modified frequentist method, upper limits on the branching fractions are set under different assumptions of invisible particle masses up to 1.2 $\mathrm{\ Ge\kern -0.1em V}/c^2$. The upper limit corresponding to an invisible particle with zero mass is 7.0$\times 10^{-7}$ at the 90\% confidence level