Miracle-dz/UniFAR-FTUs huggingface dataset:
CRISPR Activation/Inhibition Experiments Reveal that Expression of Intronic MicroRNA miR-335 Depends on the Promoter Activity of its Host Gene Mest

MicroRNAs are small non-coding RNAs that act as rheostats to modulate gene expression during development, physiology, and disease. Approximately half of mammalian microRNAs are intronic. It is unknown whether intronic miRNA transcription depends on their host gene or a microRNA-specific promoter. Here, we show that CRISPR inhibition of host gene Mest downregulated hosted miR-335 in mouse embryonic stem cells and brain organoids. Reciprocally, CRISPR transactivation of Mest upregulated miR-335. By contrast, activation of miR-335 predicted promoter had no effect. Thus, intronic miR-335 expression depends on the promoter activity of its host gene. This approach could serve to map microRNA promoters.
A model of preferential pairing between epithelial and dendritic cells in thymic antigen transfer

Medullary thymic epithelial cells (mTECs) which produce and present self-antigens are essential for the establishment of central tolerance. Since mTEC numbers are limited, their function is complemented by thymic dendritic cells (DCs), which transfer mTEC-produced self-antigens via cooperative antigen transfer (CAT). While CAT is required for effective T cell selection, many aspects remain enigmatic. Given the recently described heterogeneity of mTECs and DCs, it is unclear whether the antigen acquisition from a particular TEC subset is mediated by preferential pairing with specific subset of DCs. Using several relevant Cre-based mouse models controlling the expression of fluorescent proteins, we found that in regards to CAT, each subset of thymic DCs preferentially targets distinct mTEC subset(s) and importantly, XCR1+ activated DCs represented the most potent subset in CAT. Interestingly, one thymic DC can acquire antigen repetitively and of these, monocyte-derived DCs (moDC) were determined to be the most efficient in repetitive CAT. moDCs also represented the most potent DC subset in the acquisition of antigen from other DCs. These findings suggest a preferential pairing model for the distribution of mTEC-derived antigens among distinct populations of thymic DCs.
Injury-induced Erk1/2 signaling enhances Ca2+ activity and is necessary for regeneration of spinal cord and skeletal muscle

The transition of stem cells from quiescence to proliferation enables tissues to self-repair. The signaling mechanisms driving these stem-cell-status decisions are still unclear. Ca2+ and the extracellular signal-regulated kinase (Erk1/2) are two signaling pathways that have the potential to coordinate multiple signals to promote a specific cellular response. They both play important roles during development but their roles during regeneration are not fully deciphered. Here we show in Xenopus laevis larvae that both Ca2+ and Erk1/2 signaling pathways are activated after tail amputation. In response to injury, we find that Erk1/2 signaling is activated in neural and muscle stem cells and is necessary for spinal cord and skeletal muscle regeneration. Finally, we show in vivo that Erk1/2 action is necessary for an injury-induced increase in intracellular store-dependent Ca2+ dynamics in skeletal muscle-associated tissues but that in spinal cord, injury increases Ca2+ influx-dependent Ca2+ activity independent of Erk1/2 signaling. This study suggests that precise temporal and tissue-specific activation of Ca2+ and Erk1/2 pathways is essential for regulating tissue regeneration.
MAGI-MS: multiple seed-centric module discovery

Complex disorders manifest by the interaction of multiple genetic and environmental factors. Through the construction of genetic modules that consist of highly co-expressed genes, it is possible to identify genes that participate in common biological pathways relevant to specific phenotypes. We have previously developed tools MAGI and MAGI-S for genetic module discovery by incorporating co-expression and protein-interaction networks. Here we introduce an extension to MAGI-S, denoted as Merging Affected Genes into Integrated Networks - Multiple Seeds (MAGI-MS), that permits the user to further specify a disease pathway of interest by selecting multiple seed genes likely to function in the same molecular mechanism. By providing MAGI-MS with pairs of seed genes involved in processes underlying certain classes of neurodevelopmental disorders, such as epilepsy, we demonstrate that MAGI-MS can reveal modules enriched in genes relevant to chemical synaptic transmission, glutamatergic synapse, and other functions associated with the provided seed genes. Availability and implementation MAGI-MS is free and is available at: https://github.com/jchow32/MAGI-MS
Predictors of human-infective RNA virus discovery in the United States, China, and Africa, an ecological study

Background The variation in the pathogen type as well as the spatial heterogeneity of predictors make the generality of any associations with pathogen discovery debatable. Our previous work confirmed that the association of a group of predictors differed across different types of RNA viruses, yet there have been no previous comparisons of the specific predictors for RNA virus discovery in different regions. The aim of the current study was to close the gap by investigating whether predictors of discovery rates within three regions—the United States, China and Africa—differ from one another and from those at the global level. Methods Based on a comprehensive list of human-infective RNA viruses, we collated published data on first discovery of each species in each region. We used a Poisson boosted regression tree (BRT) model to examine the relationship between virus discovery and 33 predictors representing climate, socio-economics, land use, and biodiversity across each region separately. The discovery probability in three regions in 2010–2019 was mapped using the fitted models and historical predictors. Results The numbers of human-infective virus species discovered in the United States, China and Africa up to 2019 were 95, 80 and 107 respectively, with China lagging behind the other two regions. In each region, discoveries were clustered in hotspots. BRT modelling suggested that in all three regions RNA virus discovery was best predicted by land use and socio- economic variables, followed by climatic variables and biodiversity, though the relative importance of these predictors varied by region. Map of virus discovery probability in 2010– 2019 indicated several new hotspots outside historical high-risk areas. Most new virus species since 2010 in each region (6/6 in the United States, 19/19 in China, 12/19 in Africa) were discovered in high risk areas as predicted by our model. Conclusions The drivers of spatiotemporal variation in virus discovery rates vary in different regions of the world. Within regions virus discovery is driven mainly by land-use and socio- economic variables; climate and biodiversity variables are consistently less important predictors than at a global scale. Potential new discovery hotspots in 2010–2019 are identified. Results from the study could guide active surveillance for new human-infective viruses in local high risk areas. Funding Darwin Trust of Edinburgh; European Union.
Potential Key Genes Associated with Stroke types and its subtypes: A Computational Approach

To investigate prospective key genes and pathways associated with the pathogenesis and prognosis of stroke types along with subtypes. Human genes using genome assembly build 38 patch release 13 with known gene symbols through NCBI gene database (https://www.ncbi.nlm.nih.gov/gene) were fetched. PubMed advanced queries were constructed using stroke-related keywords and associations were calculated using Normalized pointwise mutual information (nPMI) between each gene symbol and queries. Genes related with stroke risk within their types and subtypes were investigated in order to discover genetic markers to predict individuals who are at the risk of developing stroke with their subtypes. A total of 2,785 (9.4%) genes were found to be linked to the risk of stroke. Based on stroke types, 1,287 (46.2%) and 376 (13.5%) genes were found to be related with IS and HS respectively. Further stratification of IS based on TOAST classification, 86 (6.6%) genes were confined to Large artery atherosclerosis; 131 (10.1%) and 130 (10%) genes were related with the risk of small vessel disease and Cardioembolism subtypes of IS. Besides, a prognostic panel of 9 genes signature consisting of CYP4A11, ALOX5P, NOTCH, NINJ2, FGB, MTHFR, PDE4D, HDAC9, and ZHFX3 can be treated as a diagnostic marker to predict individuals who are at the risk of developing stroke with their subtypes.
High-quality genome assembly of Cinnamomum burmannii (chvar. Borneol) provides insights into the natural borneol biosynthesis

Cinnamomum burmannii (chvar. Borneol) is a well-known medicinal and industrial plant cultivated in the Lingnan region of China. It is the key source from organism of natural borneol (D-borneol), one of the precious and widely used Chinese herbal medicines with a variety of medicinal effects. Here, we report a high-quality chromosome-scale genome assembly of C. burmannii (chvar. Borneol) using Pacbio single-molecule sequencing and Hi-C technology. The assembled genome size was 1.14 GB with a scaffold N50 of 94.30 Mb, while 98.77% of the assembled sequences were anchored on 12 pseudochromosomes including 41549 protein-coding genes. Genomic evolution analysis revealed C. burmannii and C. micranthum shared two Lauraceae unique ancestral whole-genome duplication (WGD) events. Likewise, comparative genomic analysis showed strong collinearity between these two species. Besides, the analysis for Long Terminal Repeat Retrotransposons (LTR-RTs) indicated the outbreak of LTR-RTs insertion made a great contribution to the size difference of genomes between C. burmannii and C. micranthum. Furthermore, the candidate genes in pathway associated with natural borneol synthesis were identified on the genome and their differential expressions were analyzed in various biological tissues. We considered that several of genes in Mevalonate (MVA) Methylerythritol Phosphate (MEP) pathways or in downstream pathway have the potential to be the key factors in the biosynthesis of D-borneol. We also constructed the genome database (CAMD; http://www.cinnamomumdatabase.com/) of Cinnamomum species for a better data utilization in the future. All these results will enrich the genomic data of Lauraceae plants and facilitate genetic improvement of this commercially important plant.
Non-linear Functional Brain Co-activations in Short-Term Memory Distortion Tasks

Recent works shed light on the neural correlates of true and false recognition and the influence of time of day on cognitive performance. The current study aimed to investigate the modulation of the false memory formation by the time of day using a non-linear correlation analysis originally designed for fMRI resting-state data. Fifty-four young and healthy participants (32 females, mean age: 24.17 y.o., SD: 3.56 y.o.) performed in MR scanner the modified Deese-Roediger-McDermott paradigm in short-term memory during one session in the morning and another in the evening. Subjects’ responses were modeled with a general linear model, which includes as a predictor the non-linear correlations of regional BOLD activity with the stimuli, separately for encoding and retrieval phases. The results show the dependence of the non-linear correlations measures with the time of day and the type of the probe. In addition, the results indicate differences in the correlations measures with hippocampal regions between positive and lure probes. Besides confirming previous results on the influence of time-of-day on cognitive performance, the study demonstrates the effectiveness of the non-linear correlation analysis method for the characterization of fMRI task paradigms.
Glial Nrf2 signaling mediates the neuroprotection exerted by Gastrodia elata Blume in Lrrk2-G2019S Parkinson’s disease

The most frequent missense mutations in familial Parkinson’s disease (PD) occur in the highly conserved LRRK2/PARK8 gene with G2019S mutation. We previously established a fly model of PD carrying the LRRK2-G2019S mutation that exhibited the parkinsonism-like phenotypes. An herbal medicine, Gastrodia elata Blume (GE), has been reported to have neuroprotective effects in toxin-induced PD models. However, the underpinning molecular mechanisms of GE beneficiary to G2019S-induced PD remain unclear. Here, we show that these G2019S flies treated with water extracts of GE (WGE) and its bioactive compounds, gastrodin and 4-HBA, displayed locomotion improvement and dopaminergic neuron protection. WGE suppressed the accumulation and hyperactivation of G2019S proteins in dopaminergic neurons and activated the antioxidation and detoxification factor Nrf2 mostly in the astrocyte-like and ensheathing glia. Glial activation of Nrf2 antagonizes G2019S-induced Mad/Smad signaling. Moreover, we treated LRRK2-G2019S transgenic mice with WGE and found that the locomotion declines, the loss of dopaminergic neurons, and the number of hyperactive microglia were restored. WGE also suppressed the hyperactivation of G2019S proteins and regulated the Smad2/3 pathways in the mice brains. We conclude that WGE prevents locomotion defects and the neuronal loss induced by G2019S mutation via glial Nrf2/Mad signaling, unveiling a potential therapeutic avenue for PD.
MRDA-MGFSNet: Network Based on a Multi-Rate Dilated Attention Mechanism and Multi-Granularity Feature Sharer for Image-Based Butterflies Fine-Grained Classification

Aiming at solving the problems of high background complexity of some butterfly images and the difficulty in identifying them caused by their small inter-class variance, we propose a new fine-grained butterfly classification architecture, called Network based on Multi-rate Dilated Attention Mechanism and Multi-granularity Feature Sharer (MRDA-MGFSNet). First, in this network, in order to effectively identify similar patterns between butterflies and suppress the information that is similar to the butterfly’s features in the background but is invalid, a Multi-rate Dilated Attention Mechanism (MRDA) with a symmetrical structure which assigns different weights to channel and spatial features is designed. Second, fusing the multi-scale receptive field module with the depthwise separable convolution module, a Multi-granularity Feature Sharer (MGFS), which can better solve the recognition problem of a small inter-class variance and reduce the increase in parameters caused by multi-scale receptive fields, is proposed. In order to verify the feasibility and effectiveness of the model in a complex environment, compared with the existing methods, our proposed method obtained a mAP of 96.64%, and an F1 value of 95.44%, which showed that the method proposed in this paper has a good effect on the fine-grained classification of butterflies.
Solar Ultraviolet Bursts in the Joint Footpoints of Multiple Transition Region Loops

Solar Ultraviolet bursts (UBs) associated with flux emergence are expected to help understand the physical processes of the flux emergence itself. In the present study, we analyse imaging and spectroscopic observations of a special group of UBs (including twelve of them) occurring in the joint footpoint regions of multiple transition region loops above the flux emerging regions. Consistent with previous studies of common UBs, we found that the spectral characteristics of this group of UBs are varied. Our results show that the responses of UBs in Ni ii, NUV continuum, Mg ii h and O i are originated from locations differ from that emits Si iv. The imaging data show that UBs have connections with the dynamics in the transition region loops. Brightenings starting from UB-regions and propagating along loops can be seen in SJ 1400/1330 Å and AIA 304 Å images and the corresponding time-space images. The apparent velocities are tens of kilometers per second in AIA 304 Å. For symmetry, the brightenings can propagate from the UB-regions towards opposite directions with similar apparent velocities in some cases. Given that these UBs are magnetic reconnection phenomena, we suggest that the propagating brightenings are the signals of the plasma flows resulted from heatings in the UB-regions.
TGSL-Dependent Feature Selection for Boosting the Visual Sentiment Classification

The automatic recognition of the emotions in still images is inherently more challenging than other visual recognition tasks, such as scene recognition, object classification and semantic image classification, as it involves a higher level of abstraction in the human cognition perspective. Symmetry can be found in many objects in the nature and can be used for many purposes such as object detection and recognition. Furthermore, rotating and flipping of the image is employed based on symmetry for training the classifier for the most accurate classification. Hence, there is a need to handle effectively large intra-class variance, scalability and subjectivity during recognition, and it is inherently ambiguous as an image can evoke multiple emotions. To address these issues, many of the existing works focus on improving the image representations. It is motivated by the observation that both global distributions and local image regions carry massive sentiments. In this research, three different pre-trained architectural models are implemented, and the classification performance of binary sentiment classification is examined on five widely-used effective datasets. Moreover, the features from the pre-trained models are selected optimally using the proposed Teaching Gaining Sharing Learning (TGSL) algorithm, which is the major contribution of the research. Extensive experiment results on the five datasets demonstrate that the proposed Visual sentiment analysis based on the TGSL algorithm with data augmentation achieved an improved performance compared to all other conventional techniques. The proposed framework uses the pre-trained model and never utilized any hand-crafted features, boosting the mean accuracy, sensitivity, and specificity to 99.11%, 99.31%, and 99.22%, respectively, for abstract dataset.
A Systematic Review on the Interaction between Emotion and Pseudoneglect

Background: A large body of research has shown brain asymmetries in spatial attention. Specifically, there is an attention-processing advantage for the left visual field in healthy, right-handed subjects, known as “pseudoneglect. ” Several studies have revealed that emotions modulate this basic spatial phenomenon, but the direction of the effect is still unclear. Here we systematically review empirical evidence on the behavioral effects of emotion on pseudoneglect. Methods: We searched through Pubmed, Scopus, PsycINFO, and PsychArticles. Original peer-reviewed articles published until February 2021 were included if they (1) were written in English; (2) were conducted on adults; (3) included at least one task to measure pseudoneglect, and (4) included at least one task with emotional stimuli or employed a measure of emotional state/trait, as they relate to pseudoneglect. Results: Fifteen studies were included, and 784 healthy participants took part in all studies reviewed. Discussion: The results show some evidence of emotion modulation of pseudoneglect, but evidence on the direction of the effect is mixed. We discuss the role of methodological factors that could account for the available findings and the implications for emotion asymmetry hypotheses such as the right-hemisphere hypothesis, the valence-specific hypothesis, as well as neural and arousal frameworks of attention–emotion interactions.
Relationship between Asymmetries Measured on Different Levels in Elite Basketball Players

In this study, we investigated the association of magnitude and agreement in direction between asymmetries measured on single-joint (hip and trunk), complex movement (jumping), and skill (change of direction (CoD)) levels. The study sample comprised 43 junior- and senior-level (age = 20.5 ± 6.0 years; height = 194.5 ± 7.2 cm; body mass = 86.8 ± 10.1 kg) elite male basketball players. Both limbs/sides were tested in hip and trunk isometric strength; passive range of motion (RoM); unilateral, horizontal, and vertical jumping; and CoD tests, from which asymmetry indexes were calculated. The associations between asymmetry magnitudes were calculated with Spearman’s ρ correlation coefficient. The agreement between the direction of asymmetries on different levels was calculated with Cohen’’s Kappa (κ) coefficient. The average magnitude of asymmetry varied substantially (2.9–40.3%). Most associations between asymmetry magnitudes measured on different levels were small and statistically non-significant, with a few exceptions of moderate and large associations. Asymmetry in single-leg countermovement jump parameters was strongly associated with hip abduction maximal strength (ρ = 0.58 and 0.50, p < 0.01). Agreement between asymmetry directions was slight to fair, with a few moderate exceptions. Results indicate that multiple tests are needed to obtain a comprehensive picture of athletes’ asymmetries and that universal thresholds and golden standard tests for return to play should be reconsidered and reinvestigated.
Speech Enhancement for Hearing Impaired Based on Bandpass Filters and a Compound Deep Denoising Autoencoder

Deep neural networks have been applied for speech enhancements efficiently. However, for large variations of speech patterns and noisy environments, an individual neural network with a fixed number of hidden layers causes strong interference, which can lead to a slow learning process, poor generalisation in an unknown signal-to-noise ratio in new inputs, and some residual noise in the enhanced output. In this paper, we present a new approach for the hearing impaired based on combining two stages: (1) a set of bandpass filters that split up the signal into eight separate bands each performing a frequency analysis of the speech signal; (2) multiple deep denoising autoencoder networks, with each working for a small specific enhancement task and learning to handle a subset of the whole training set. To evaluate the performance of the approach, the hearing-aid speech perception index, the hearing aid sound quality index, and the perceptual evaluation of speech quality were used. Improvements in speech quality and intelligibility were evaluated using seven subjects of sensorineural hearing loss audiogram. We compared the performance of the proposed approach with individual denoising autoencoder networks with three and five hidden layers. The experimental results showed that the proposed approach yielded higher quality and was more intelligible compared with three and five layers.
Hotspot Temperature Prediction of Dry-Type Transformers Based on Particle Filter Optimization with Support Vector Regression

Both poor cooling methods and complex heat dissipation lead to prominent asymmetry in transformer temperature distribution. Both the operating life and load capacity of a power transformer are closely related to the winding hotspot temperature. Realizing accurate prediction of the hotspot temperature of transformer windings is the key to effectively preventing thermal faults in transformers, thus ensuring the reliable operation of transformers and accurately predicting transformer operating lifetimes. In this paper, a hot spot temperature prediction method is proposed based on the transformer operating parameters through the particle filter optimization support vector regression model. Based on the monitored transformer temperature, load rate, transformer cooling type, and ambient temperature, the hotspot temperature of a dry-type transformer can be predicted by a support vector regression method. The hyperparameters of the support vector regression are dynamically optimized here according to the particle filter to improve the optimization accuracy. The validity and accuracy of the proposed method are verified by comparing the proposed method with a traditional support vector regression method based on the real operating data of a 35 kV dry-type transformer.