General Incremental Learning with Domain-aware Categorical Representations

Continual learning is an important problem for achieving human-level intelligence in real-world applications as an agent must continuously accumulate knowledge in response to streaming data/tasks. In this work, we consider a general and yet under-explored incremental learning problem in which both the class distribution and class-specific domain distribution change over time. In addition to the typical challenges in class incremental learning, this setting also faces the intra-class stability-plasticity dilemma and intra-class domain imbalance problems. To address above issues, we develop a novel domain-aware continual learning method based on the EM framework. Specifically, we introduce a flexible class representation based on the von Mises-Fisher mixture model to capture the intra-class structure, using an expansion-and-reduction strategy to dynamically increase the number of components according to the class complexity. Moreover, we design a bi-level balanced memory to cope with data imbalances within and across classes, which combines with a distillation loss to achieve better inter- and intra-class stability-plasticity trade-off. We conduct exhaustive experiments on three benchmarks: iDigits, iDomainNet and iCIFAR-20. The results show that our approach consistently outperforms previous methods by a significant margin, demonstrating its superiority.Comment: Accepted by CVPR202

Multiple genetic analyses to investigate the polymorphisms of Chinese Mongolian population with an efficient short tandem repeat panel

Aim To determine allele frequencies and forensic statistics of 22 autosomal short tandem repeat loci in Chinese Mongolian population. Methods Blood specimens were collected from 134 unrelated healthy Mongolian individuals, and 22 short tandem repeat loci were co-amplified and genotyped. Allele frequencies and forensic parameters were calculated, and population genetic differences were analyzed among Mongolian population and other eight Chinese populations: Northern Han, Guangdong Han, Chengdu Han, Xinjiang Hui, Xinjiang Uygur, Hainan Li, Qinghai Tibetan, and Hainan Han. Results All the loci were in the Hardy-Weinberg equilibrium, and after Bonferroni correction there was no linkage disequilibrium between them. The allele frequencies of these 22 loci were between 0.0037 and 0.3657. This panel had high discriminating power and genetic polymorphism in the Mongolian population, with combined power of discrimination of 0.999999999999999999999999998399 and combined probability of exclusion of 0.9999999999566925. Structure analysis showed no evidence that these nine Chinese populations had different component distribution. However, genetic distance analysis showed significant differences among them (P < 0.05). Conclusion The combined application of these 22 loci could be useful for forensic purposes in the Mongolian population. Mongolian population had smaller genetic distances from the populations in northern China (Northern Han, Xinjiang Uygur, and Xinjiang Hui) than from the populations in Hainan province (Hainan Han and Hainan Li populations)

Lack of association between polymorphisms of MASP2 and susceptibility to SARS coronavirus infection

<p>Abstract</p> <p>Background</p> <p>The pathogenesis of severe acute respiratory disease syndrome (SARS) is not fully understood. One case-control study has reported an association between susceptibility to SARS and <it>mannan-binding lectin </it>(<it>MBL</it>) in China. As the downstream protein of <it>MBL</it>, variants of the <it>MBL</it>-associated serine protease-2 (<it>MASP2</it>) gene may be associated with SARS coronavirus (SARS-CoV) infection in the same population.</p> <p>Methods</p> <p>Thirty individuals with SARS were chosen for analysis of <it>MASP2 </it>polymorphisms by means of PCR direct sequencing. Tag single nucleotide polymorphisms (tagSNPs) were chosen using pairwise tagging algorithms. The frequencies of four tag SNPs (rs12711521, rs2261695, rs2273346 and rs7548659) were ascertained in 376 SARS patients and 523 control subjects, using the Beckman SNPstream Ultra High Throughput genotyping platform.</p> <p>Results</p> <p>There is no significant association between alleles or genotypes of the <it>MASP2 </it>tagSNP and susceptibility to SARS-CoV in both Beijing and Guangzhou populations. Diplotype (rs2273346 and rs12711521)were analyzed for association with susceptibility to SARS, no statistically significant evidence of association was observed. The Beijing and Guangzhou sample groups were homogeneous regarding demographic and genetic parameters, a joined analysis also showed no statistically significant evidence of association.</p> <p>Conclusion</p> <p>Our data do not suggest a role for <it>MASP2 </it>polymorphisms in SARS susceptibility in northern and southern China.</p

Lycium species and variety recognition technology based on electrochemical sensing of leaf signals

Identification of plant species and variety has important application value in the process of agricultural production. In this work, we try to use electrochemical fingerprinting technology to collect the electrochemical behavior of electrochemically active substances in plant leaf tissues. Twenty Lycium species and varieties were specifically selected to investigate the recognition ability of electrochemical fingerprinting. Two different extraction solvents and electrolytes were used to create different collection environments. The results show that different Lycium spp. can exhibit different electrochemical fingerprints. Different species of the same species exhibit relatively similar electrochemical fingerprints. After the second derivative processing, the electrochemical fingerprint of plants can be used for classification and recognition by different machine learning models. Partial least squares discriminant analysis (PLS-DA), k-nearest neighbor, (KNN), support vector machine (SVM), random forest (RF) and stochastic gradient boosting (SGB) were used to establish recognition model of Lycium spp. The results show that SGB has the best identification accuracy for electrochemical fingerprint after second derivative treatment

Characterization of miRNomes in Acute and Chronic Myeloid Leukemia Cell Lines

AbstractMyeloid leukemias are highly diverse diseases and have been shown to be associated with microRNA (miRNA) expression aberrations. The present study involved an in-depth miRNome analysis of two human acute myeloid leukemia (AML) cell lines, HL-60 and THP-1, and one human chronic myeloid leukemia (CML) cell line, K562, via massively parallel signature sequencing. mRNA expression profiles of these cell lines that were established previously in our lab facilitated an integrative analysis of miRNA and mRNA expression patterns. miRNA expression profiling followed by differential expression analysis and target prediction suggested numerous miRNA signatures in AML and CML cell lines. Some miRNAs may act as either tumor suppressors or oncomiRs in AML and CML by targeting key genes in AML and CML pathways. Expression patterns of cell type-specific miRNAs could partially reflect the characteristics of K562, HL-60 and THP-1 cell lines, such as actin filament-based processes, responsiveness to stimulus and phagocytic activity. miRNAs may also regulate myeloid differentiation, since they usually suppress differentiation regulators. Our study provides a resource to further investigate the employment of miRNAs in human leukemia subtyping, leukemogenesis and myeloid development. In addition, the distinctive miRNA signatures may be potential candidates for the clinical diagnosis, prognosis and treatment of myeloid leukemias

Searching for the nano-Hertz stochastic gravitational wave background with the Chinese Pulsar Timing Array Data Release I

Observing and timing a group of millisecond pulsars (MSPs) with high rotational stability enables the direct detection of gravitational waves (GWs). The GW signals can be identified from the spatial correlations encoded in the times-of-arrival of widely spaced pulsar-pairs. The Chinese Pulsar Timing Array (CPTA) is a collaboration aiming at the direct GW detection with observations carried out using Chinese radio telescopes. This short article serves as a `table of contents' for a forthcoming series of papers related to the CPTA Data Release 1 (CPTA DR1) which uses observations from the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Here, after summarizing the time span and accuracy of CPTA DR1, we report the key results of our statistical inference finding a correlated signal with amplitude \log A_{\rm c}= -14.4 \,^{+1.0}_{-2.8} for spectral index in the range of $\alpha\in [-1.8, 1.5]$ assuming a GW background (GWB) induced quadrupolar correlation. The search for the Hellings-Downs (HD) correlation curve is also presented, where some evidence for the HD correlation has been found that a 4.6-$\sigma$ statistical significance is achieved using the discrete frequency method around the frequency of 14 nHz. We expect that the future International Pulsar Timing Array data analysis and the next CPTA data release will be more sensitive to the nHz GWB, which could verify the current results.Comment: 18 pages, 6 figures, submitted to "Research in astronomy and astrophysics" 22nd March 202

A predictive energy management strategy for multi-mode plug-in hybrid electric vehicles based on multi neural networks

Online optimal energy management of plug-in hybrid electric vehicles has been continually investigated for better fuel economy. This paper proposed a predictive energy management strategy based on multi neural networks for a multi-mode plug-in hybrid electric vehicle. To attain it, firstly, the offline optimal results prepared for knowledge learning are derived by dynamic programming and Pontryagin’s minimum principle. Then, the mode recognition neural network is trained based on the optimal results of dynamic programming and the recurrent neural network is firstly exploited to realize online co-state estimation application. Consequently, the velocity prediction-based online model predictive control framework is established with the co-state correction and slacked constraints to solve the real-time optimal control sequence. A series of numerical simulation results validate that the optimal performance yielded from global optimal strategy can be exploited online to attain the satisfied cost reduction, compared with equivalent consumption minimum strategy, with the assistance of estimated real time co-state and slacked reference. In addition, the computation duration of proposed algorithm decreases by 23.40%, compared with conventional Pontryagin’s minimum principle-based model predictive control scheme, thereby proving its online application potential

Integrative omics reveals rapidly evolving regulatory sequences driving primate brain evolution

Although the continual expansion of the brain during primate evolution accounts for our enhanced cognitive capabilities, the drivers of brain evolution have scarcely been explored in these ancestral nodes. Here, we performed large-scale comparative genomic, transcriptomic, and epigenomic analyses to investigate the evolutionary alterations acquired by brain genes and provide comprehensive listings of innovatory genetic elements along the evolutionary path from ancestral primates to human. The regulatory sequences associated with brain-expressed genes experienced rapid change, particularly in the ancestor of the Simiiformes. Extensive comparisons of single-cell and bulk transcriptomic data between primate and nonprimate brains revealed that these regulatory sequences may drive the high expression of certain genes in primate brains. Employing in utero electroporation into mouse embryonic cortex, we show that the primate-specific brain-biased gene BMP7 was recruited, probably in the ancestor of the Simiiformes, to regulate neuronal proliferation in the primate ventricular zone. Our study provides a comprehensive listing of genes and regulatory changes along the brain evolution lineage of ancestral primates leading to human. These data should be invaluable for future functional studies that will deepen our understanding not only of the genetic basis of human brain evolution but also of inherited disease