Deep Learning Topological Invariants of Band Insulators

In this work we design and train deep neural networks to predict topological invariants for one-dimensional four-band insulators in AIII class whose topological invariant is the winding number, and two-dimensional two-band insulators in A class whose topological invariant is the Chern number. Given Hamiltonians in the momentum space as the input, neural networks can predict topological invariants for both classes with accuracy close to or higher than 90%, even for Hamiltonians whose invariants are beyond the training data set. Despite the complexity of the neural network, we find that the output of certain intermediate hidden layers resembles either the winding angle for models in AIII class or the solid angle (Berry curvature) for models in A class, indicating that neural networks essentially capture the mathematical formula of topological invariants. Our work demonstrates the ability of neural networks to predict topological invariants for complicated models with local Hamiltonians as the only input, and offers an example that even a deep neural network is understandable.Comment: 8 pages, 5 figure

Relationship between learning flow and academic performance among students: a systematic evaluation and meta-analysis

IntroductionThe concept of “flow experience,” characterized by a state of immersive enjoyment and profound engagement, pertains to individuals’ deep involvement in intriguing and pleasant tasks. In the field of study, individuals are in a state of flow when encountering challenging tasks, which matters considerably in completing the tasks. Therefore, learning flow is considered a hotspot in education that may be related to improving academic performance. Nonetheless, there remains contention regarding the extent of learning flow’s impact on academic performance. To this end, meta-learning was hereby used to provide evidenced on the relationship between them.MethodsA systematic review was conducted under the guidance of PRISMA to examine the evidence of learning flow and academic performance, check the potential mechanism and evaluate the current evidence. Clinical research or empirical research on the influence of learning flow on academic achievement was collected by searching four databases. The literature retrieval spanned from each database’s inception until June 2023, specifically covering the PubMed (2000–2023.6), Embase (1974–2023.6), Cochrane Library (1993–2023.6), and the Web of Science (1807–2023.6), with particular attention to the period between 2000 and 2023.ResultsThirteen RCTs were included, the total sample size used in the study was 3,253. Using the NOS evaluation tool of queue study, the average evaluation score of the included literatures was 7.46, indicating that the overall literature was above average. Besides, the data software StataSE was used to test the heterogeneity of the data, and the correlation coefficient and 95% confidence interval effect were found to be 0.43 (0.28, 0.57).DiscussionOur research indicates a link between learning flow and academic performance, that is, students with high learning flow levels tend to have better academic performance. At the same time, this conclusion needs to be verified by more high-quality literature and larger sample data.Systematic review registrationhttps://inplasy.com, identifier INPLASY202360079

RES-Scanner:a software package for genome-wide identification of RNA-editing sites

BACKGROUND: High-throughput sequencing (HTS) provides a powerful solution for the genome-wide identification of RNA-editing sites. However, it remains a great challenge to distinguish RNA-editing sites from genetic variants and technical artifacts caused by sequencing or read-mapping errors. RESULTS: Here we present RES-Scanner, a flexible and efficient software package that detects and annotates RNA-editing sites using matching RNA-seq and DNA-seq data from the same individuals or samples. RES-Scanner allows the use of both raw HTS reads and pre-aligned reads in BAM format as inputs. When inputs are HTS reads, RES-Scanner can invoke the BWA mapper to align reads to the reference genome automatically. To rigorously identify potential false positives resulting from genetic variants, we have equipped RES-Scanner with sophisticated statistical models to infer the reliability of homozygous genotypes called from DNA-seq data. These models are applicable to samples from either single individuals or a pool of multiple individuals if the ploidy information is known. In addition, RES-Scanner implements statistical tests to distinguish genuine RNA-editing sites from sequencing errors, and provides a series of sophisticated filtering options to remove false positives resulting from mapping errors. Finally, RES-Scanner can improve the completeness and accuracy of editing site identification when the data of multiple samples are available. CONCLUSION: RES-Scanner, as a software package written in the Perl programming language, provides a comprehensive solution that addresses read mapping, homozygous genotype calling, de novo RNA-editing site identification and annotation for any species with matching RNA-seq and DNA-seq data. The package is freely available. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13742-016-0143-4) contains supplementary material, which is available to authorized users

SHP2 phosphatase as a novel therapeutic target for melanoma treatment

Melanoma ranks among the most aggressive and deadly human cancers. Although a number of targeted therapies are available, they are effective only in a subset of patients and the emergence of drug resistance often reduces durable responses. Thus there is an urgent need to identify new therapeutic targets and develop more potent pharmacological agents for melanoma treatment. Herein we report that SHP2 levels are frequently elevated in melanoma, and high SHP2 expression is significantly associated with more metastatic phenotype and poorer prognosis. We show that SHP2 promotes melanoma cell viability, motility, and anchorage-independent growth, through activation of both ERK1/2 and AKT signaling pathways. We demonstrate that SHP2 inhibitor 11a-1 effectively blocks SHP2-mediated ERK1/2 and AKT activation and attenuates melanoma cell viability, migration and colony formation. Most importantly, SHP2 inhibitor 11a-1 suppresses xenografted melanoma tumor growth, as a result of reduced tumor cell proliferation and enhanced tumor cell apoptosis. Taken together, our data reveal SHP2 as a novel target for melanoma and suggest SHP2 inhibitors as potential novel therapeutic agents for melanoma treatment

The role of Hippo pathway in ovarian development

The follicle is the functional unit of the ovary, whereby ovarian development is largely dependent on the development of the follicles themselves. The activation, growth, and progression of follicles are modulated by a diverse range of factors, including reproductive endocrine system and multiple signaling pathways. The Hippo pathway exhibits a high degree of evolutionary conservation between both Drosophila and mammalian systems, and is recognized for its pivotal role in regulating cellular proliferation, control of organ size, and embryonic development. During the process of follicle development, the components of the Hippo pathway show temporal and spatial variations. Recent clinical studies have shown that ovarian fragmentation can activate follicles. The mechanism is that the mechanical signal of cutting triggers actin polymerization. This process leads to the disruption of the Hippo pathway and subsequently induces the upregulation of downstream CCN and apoptosis inhibitors, thereby promoting follicle development. Thus, the Hippo pathway plays a crucial role in both the activation and development of follicles. In this article, we focused on the development and atresia of follicles and the function of Hippo pathway in these processes. Additionally, the physiological effects of Hippo pathway in follicle activation are also explored

A survey of overlooked viral infections in biological experiment systems

It is commonly accepted that there are many unknown viruses on the planet. For the known viruses, do we know their prevalence, even in our experimental systems? Here we report a virus survey using recently published small (s)RNA sequencing datasets. The sRNA reads were assembled and contigs were screened for virus homologues against the NCBI nucleotide (nt) database using the BLASTn program. To our surprise, approximately 30% (28 out of 94) of publications had highly scored viral sequences in their datasets. Among them, only two publications reported virus infections. Though viral vectors were used in some of the publications, virus sequences without any identifiable source appeared in more than 20 publications. By determining the distributions of viral reads and the antiviral RNA interference (RNAi) pathways using the sRNA profiles, we showed evidence that many of the viruses identified were indeed infecting and generated host RNAi responses. As virus infections affect many aspects of host molecular biology and metabolism, the presence and impact of viruses needs to be actively investigated in experimental systems

Boundary-aware Decoupled Flow Networks for Realistic Extreme Rescaling

Recently developed generative methods, including invertible rescaling network (IRN) based and generative adversarial network (GAN) based methods, have demonstrated exceptional performance in image rescaling. However, IRN-based methods tend to produce over-smoothed results, while GAN-based methods easily generate fake details, which thus hinders their real applications. To address this issue, we propose Boundary-aware Decoupled Flow Networks (BDFlow) to generate realistic and visually pleasing results. Unlike previous methods that model high-frequency information as standard Gaussian distribution directly, our BDFlow first decouples the high-frequency information into semantic high-frequency that adheres to a Boundary distribution and non-semantic high-frequency counterpart that adheres to a Gaussian distribution. Specifically, to capture semantic high-frequency parts accurately, we use Boundary-aware Mask (BAM) to constrain the model to produce rich textures, while non-semantic high-frequency part is randomly sampled from a Gaussian distribution. Comprehensive experiments demonstrate that our BDFlow significantly outperforms other state-of-the-art methods while maintaining lower complexity. Notably, our BDFlow improves the PSNR by 4.4 dB and the SSIM by 0.1 on average over GRAIN, utilizing only 74% of the parameters and 20% of the computation. The code will be available at https://github.com/THU-Kingmin/BAFlow

Caste-specific RNA editomes in the leaf-cutting ant <i>Acromyrmex echinatior</i>

Eusocial insects have evolved the capacity to generate adults with distinct morphological, reproductive and behavioural phenotypes from the same genome. Recent studies suggest that RNA editing might enhance the diversity of gene products at the post-transcriptional level, particularly to induce functional changes in the nervous system. Using head samples from the leaf-cutting ant Acromyrmex echinatior, we compare RNA editomes across eusocial castes, identifying ca. 11,000 RNA editing sites in gynes, large workers and small workers. Those editing sites map to 800 genes functionally enriched for neurotransmission, circadian rhythm, temperature response, RNA splicing and carboxylic acid biosynthesis. Most A. echinatior editing sites are species specific, but 8–23% are conserved across ant subfamilies and likely to have been important for the evolution of eusociality in ants. The level of editing varies for the same site between castes, suggesting that RNA editing might be a general mechanism that shapes caste behaviour in ants