Practical guidance and workflows for identifying fast evolving non-coding genomic elements using PhyloAcc.

Comparative genomics provides ample ways to study genome evolution and its relationship to phenotypic traits. By developing and testing alternate models of evolution throughout a phylogeny, one can estimate rates of molecular evolution along different lineages in a phylogeny and link these rates with observations in extant species, such as convergent phenotypes. Pipelines for such work can help identify when and where genomic changes may be associated with, or possibly influence, phenotypic traits. We recently developed a set of models called PhyloAcc, using a Bayesian framework to estimate rates of nucleotide substitution on different branches a phylogenetic tree and evaluate their association with pre-defined or estimated phenotypic traits PhyloAcc-ST and PhyloAcc-GT both allow users to define a priori a set of target lineages and then compare different models to identify loci accelerating in one or more target lineages. Whereas ST considers only one species tree across all input loci, GT considers alternate topologies for every locus. PhyloAcc-C simultaneously models molecular rates and rates of continuous trait evolution,allowing the user to ask whether the two are associated. Here we describe these models and provide tips and workflows on how to prepare the input data and run PhyloAcc.

CellWalker2: multi-omic discovery of hierarchical cell type relationships and their associations with genomic annotations.

CellWalker2 is a graph diffusion-based method for single-cell genomics data integration. It extends the CellWalker model by incorporating hierarchical relationships between cell types, providing estimates of statistical significance, and adding data structures for analyzing multi-omics data so that gene expression and open chromatin can be jointly modeled. Our open-source software enables users to annotate cells using existing ontologies and to probabilistically match cell types between two or more contexts, including across species. CellWalker2 can also map genomic regions to cell ontologies, enabling precise annotation of elements derived from bulk data, such as enhancers, genetic variants, and sequence motifs. Through simulation studies, we show that CellWalker2 performs better than existing methods in cell type annotation and mapping. We then use data from the brain and immune system to demonstrate CellWalker2's ability to discover cell type-specific regulatory programs and both conserved and divergent cell type relationships in complex tissues.

[Prediction Spatial Distribution of Soil Organic Matter Based on Improved BP Neural Network with Optimized Sparrow Search Algorithm].

Soil organic matter is an important indicator of soil fertility, and it is necessary to improve the accuracy of regional organic matter spatial distribution prediction. In this study, we analyzed the organic matter content of 1 690 soil surface layers (0-20 cm) and collected data on the natural environment and human activities in the Weining Plain of the Yellow River Basin. The SOM spatial distribution prediction model was established with 1 348 points using classical statistics, deterministic interpolation, geostatistical interpolation, and machine learning, respectively, and 342 sample points data were used as the test set to test and analyze the prediction accuracy of different models. The results showed that the average SOM content of the surface soil of the Weining Plain was 14.34 g·kg-1, and the average soil organic matter variation across 1 690 sampling points was 34.81%, indicating a medium degree of variability. The results also revealed a spatial distribution trend, with low soil organic matter content in the northeast and southwest, high soil organic matter on the left and right banks of the Yellow River in the middle, and relatively high soil organic matter in the sloping terrain of the Weining Plain. The four types of methods in order of high to low prediction accuracy were the machine learning method, geostatistical interpolation method, deterministic interpolation method, and classical statistical method. Through comparison, the BP neural network that was improved based on the optimized sparrow search algorithm had the best prediction accuracy, and the optimized sparrow search algorithm had better convergence accuracy, avoided falling into local optimization, prevented data overfitting, and had better prediction ability. This optimization algorithm can improve the accuracy of SOM prediction and has good application prospects in soil attribute prediction.

scATAnno: Automated Cell Type Annotation for single-cell ATAC Sequencing Data.

Recent advances in single-cell epigenomic techniques have created a growing demand for scATAC-seq analysis. One key analysis task is to determine cell type identity based on the epigenetic data. We introduce scATAnno, a python package designed to automatically annotate scATAC-seq data using large-scale scATAC-seq reference atlases. This workflow generates the reference atlases from publicly available datasets enabling accurate cell type annotation by integrating query data with reference atlases, without the use of scRNA-seq data. To enhance annotation accuracy, we have incorporated KNN-based and weighted distance-based uncertainty scores to effectively detect cell populations within the query data that are distinct from all cell types in the reference data. We compare and benchmark scATAnno against 7 other published approaches for cell annotation and show superior performance in multiple data sets and metrics. We showcase the utility of scATAnno across multiple datasets, including peripheral blood mononuclear cell (PBMC), Triple Negative Breast Cancer (TNBC), and basal cell carcinoma (BCC), and demonstrate that scATAnno accurately annotates cell types across conditions. Overall, scATAnno is a useful tool for scATAC-seq reference building and cell type annotation in scATAC-seq data and can aid in the interpretation of new scATAC-seq datasets in complex biological systems.

Deep flanking sequence engineering for efficient promoter design using DeepSEED.

Designing promoters with desirable properties is essential in synthetic biology. Human experts are skilled at identifying strong explicit patterns in small samples, while deep learning models excel at detecting implicit weak patterns in large datasets. Biologists have described the sequence patterns of promoters via transcription factor binding sites (TFBSs). However, the flanking sequences of cis-regulatory elements, have long been overlooked and often arbitrarily decided in promoter design. To address this limitation, we introduce DeepSEED, an AI-aided framework that efficiently designs synthetic promoters by combining expert knowledge with deep learning techniques. DeepSEED has demonstrated success in improving the properties of Escherichia coli constitutive, IPTG-inducible, and mammalian cell doxycycline (Dox)-inducible promoters. Furthermore, our results show that DeepSEED captures the implicit features in flanking sequences, such as k-mer frequencies and DNA shape features, which are crucial for determining promoter properties.

PhyloAcc-GT: A Bayesian Method for Inferring Patterns of Substitution Rate Shifts on Targeted Lineages Accounting for Gene Tree Discordance.

An important goal of evolutionary genomics is to identify genomic regions whose substitution rates differ among lineages. For example, genomic regions experiencing accelerated molecular evolution in some lineages may provide insight into links between genotype and phenotype. Several comparative genomics methods have been developed to identify genomic accelerations between species, including a Bayesian method called PhyloAcc, which models shifts in substitution rate in multiple target lineages on a phylogeny. However, few methods consider the possibility of discordance between the trees of individual loci and the species tree due to incomplete lineage sorting, which might cause false positives. Here, we present PhyloAcc-GT, which extends PhyloAcc by modeling gene tree heterogeneity. Given a species tree, we adopt the multispecies coalescent model as the prior distribution of gene trees, use Markov chain Monte Carlo (MCMC) for inference, and design novel MCMC moves to sample gene trees efficiently. Through extensive simulations, we show that PhyloAcc-GT outperforms PhyloAcc and other methods in identifying target lineage-specific accelerations and detecting complex patterns of rate shifts, and is robust to specification of population size parameters. PhyloAcc-GT is usually more conservative than PhyloAcc in calling convergent rate shifts because it identifies more accelerations on ancestral than on terminal branches. We apply PhyloAcc-GT to two examples of convergent evolution: flightlessness in ratites and marine mammal adaptations, and show that PhyloAcc-GT is a robust tool to identify shifts in substitution rate associated with specific target lineages while accounting for incomplete lineage sorting.

Joint Particle Swarm Optimization of Power and Phase Shift for IRS-Aided D2D Underlaying Cellular Systems.

Device-to-device (D2D) communication is a promising wireless communication technology which can effectively reduce the traffic load of the base station and improve the spectral efficiency. The application of intelligent reflective surfaces (IRS) in D2D communication systems can further improve the throughput, but the problem of interference suppression becomes more complex and challenging due to the introduction of new links. Therefore, how to perform effective and low-complexity optimal radio resource allocation is still a problem to be solved in IRS-assisted D2D communication systems. To this end, a low-complexity power and phase shift joint optimization algorithm based on particle swarm optimization is proposed in this paper. First, a multivariable joint optimization problem for the uplink cellular network with IRS-assisted D2D communication is established, where multiple DUEs are allowed to share a CUE's sub-channel. However, the proposed problem considering the joint optimization of power and phase shift, with the objective of maximizing the system sum rate and the constraints of the minimum user signal-to-interference-plus-noise ratio (SINR), is a non-convex non-linear model and is hard to solve. Different from the existing work, instead of decomposing this optimization problem into two sub-problems and optimizing the two variables separately, we jointly optimize them based on Particle Swarm Optimization (PSO). Then, a fitness function with a penalty term is established, and a penalty value priority update scheme is designed for discrete phase shift optimization variables and continuous power optimization variables. Finally, the performance analysis and simulation results show that the proposed algorithm is close to the iterative algorithm in terms of sum rate, but lower in power consumption. In particular, when the number of D2D users is four, the power consumption is reduced by 20%. In addition, compared with PSO and distributed PSO, the sum rate of the proposed algorithm increases by about 10.2% and 38.3%, respectively, when the number of D2D users is four.

[Spatiotemporal Pattern and Driving Force Analysis of Ecological Environmental Quality in Typical Ecological Areas of the Yellow River Basin from 1990 to 2020].

Scientific evaluation of ecological environmental quality is the premise of realizing regional ecological sustainable development. Taking Landsat series satellite images from 1990 to 2020 as the data source, on the basis of the entropy remote sensing ecological index (E-RSEI), combining the Mann-Kendall significance test, Theil-Sen Median analysis, Hurst exponent, and stability analysis, the spatial-temporal variation characteristics of ecological environmental quality in typical ecological areas of the Yellow River Basin were analyzed in the context of multi-spatiotemporal scales. In addition, the effects of eight environmental and human factors on the change in E-RSEI were quantified using a geodetector. The results showed that:① in the past 31 years, the average value of E-RSEI was 67.5%, which showed an increasing trend on the time scale, with an average increase of 0.066·(10 a)-1. On the spatial scale, E-RSEI was higher in the west and the south lower in the east and the north. ② The ecological environmental quality will continue to improve in the future, but 9.33% of the areas have potential risks of degradation. ③ Precipitation was the dominant environmental factor that affected the spatial distribution of E-RSEI in this area, and the influence of human factors was low. Compared with that of single factors, the interaction of factors had a stronger impact on ecological environmental quality, and the interaction between precipitation and other factors played a leading role. The results of this study can provide a scientific reference for the sustainable development of ecological environmental quality in the ecological zone of the Yellow River Basin.

Carbon Quantum Dot/Chitosan-Derived Hydrogels with Photo-stress-pH Multiresponsiveness for Wearable Sensors.

In recent years, hydrogels have attracted extensive attention in smart sensing owing to their biocompatibility and high elasticity. However, it is still a challenge to develop hydrogels with excellent multiple responsiveness for smart wearable sensors. In this paper, a facile synthesis of carbon quantum dots (CQDs)-doped cross-linked chitosan quaternary/carboxymethylcellulose hydrogels (CCCDs) is presented. Designing of dual network hydrogels decorated with CQDs provides abundant crosslinking and improves the mechanical properties of the hydrogels. The hydrogel-based strain sensor exhibits excellent sensitivity (gauge factor: 9.88), linearity (R2 : 0.97), stretchable ability (stress: 0.67 MPa; strain: 404%), good cyclicity, and durability. The luminescent properties are endowed by the CQDs further broaden the application of hydrogels for realizing flexible electronics. More interestingly, the strain sensor based on CCCDs hydrogel demonstrates photo responsiveness (ΔR/R0 ≈20%) and pH responsiveness (pH range ≈4-7) performance. CCCDs hydrogels can be used for gesture recognition and light sensing switch. As a proof-of-concept, a smart wearable sensor is designed for monitoring human activities and detecting pH variation in human sweat during exercise. This study reveals new possibilities for further applications in wearable health monitoring.

Enhancing Strain-Sensing Properties of the Conductive Hydrogel by Introducing PVDF-TrFE.

Conductive hydrogels have attracted attention because of their wide application in wearable devices. However, it is still a challenge to achieve conductive hydrogels with high sensitivity and wide frequency band response for smart wearable strain sensors. Here, we report a composite hydrogel with piezoresistive and piezoelectric sensing for flexible strain sensors. The composite hydrogel consists of cross-linked chitosan quaternary ammonium salt (CHACC) as the hydrogel matrix, poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT: PSS) as the conductive filler, and poly(vinylidene fluoride-co-trifluoroethylene) (PVDF-TrFE) as the piezoelectric filler. A one-pot thermoforming and solution exchange method was used to synthesize the CHACC/PEDOT: PSS/PVDF-TrFE hydrogel. The hydrogel-based strain sensor exhibits very high sensitivity (GF: 19.3), fast response (response time: 63.2 ms), and wide frequency range (response frequency: 5-25 Hz), while maintaining excellent mechanical properties (elongation at break up to 293%). It can be concluded that enhanced strain-sensing properties of the hydrogel are contributed to both greater change in the relative resistance under stress and wider response to dynamic and static stimulus by adding PVDF-TrFE. This has a broad application in monitoring human motion, detecting subtle movements, and identifying object contours and a hydrogel-based array sensor. This work provides an insight into the design of composite hydrogels based on piezoelectric and piezoresistive sensing with applications for wearable sensors.

Temperature-Stress Bimodal Sensing Conductive Hydrogel-Liquid Metal by Facile Synthesis for Smart Wearable Sensor.

Conductive hydrogels have attracted great attention due to their promising applications in wearable sensors. However, developing conductive hydrogels with excellent sensor properties and multiple stimuli responsiveness for smart wearable devices is still a challenge. This paper presents a facile synthetic method of a crosslinked chitosan quaternary ammonium salt and liquid metal (CHACC-LM) composite hydrogel with temperature-stress bimodal sensing for smart wearable sensor. LM as liquid fillers toughen the hydrogel matrix (stress: 1.11 MPa) and enhance the hydrogel extensibility (strain: 233%). The CHACC-LM hydrogel exhibits conductivity , excellent antibacterial properties (> 99%), an electrical self-healing property, and strain sensitivity (GF = 1.6). In addition, the CHACC-LM hydrogel can be used as wearable flexible sensors with the ability of monitoring human activities directly and the distinguished ability of discerning subtle motions (handwriting). It also shows sensitivity in the external environment such as low temperature, thermal response, and water solution. Importantly, the composite hydrogel simultaneous response to different stress and temperature stimuli. Furthermore, the CHACC-LM hydrogel can be used for gesture recognition and to control the manipulator in human-computer interaction. All these properties provide a great scope for researchers to achieve practical advances in smart wearable sensors.

[Spatial-temporal differentiation and driving factors of vegetation coverage in Ningxia Helan Mountain based on geodetector]. / åŸºäºŽåœ°ç†æŽ¢æµ‹å™¨çš„å®å¤è´ºå °å±±æ¤è¢«è¦†ç›–åº¦æ—¶ç©ºåˆ†å¼‚åŠé©±åŠ¨å› å­.

Monitoring the regional changes in vegetation coverage and analyzing its driving factors are beneficial to realizing the sustainable development of ecological environment. Based on Landsat 5/8 remote sensing images from 1989 to 2021, vegetation coverage of Helan Mountain in Ningxia was estimated by pixel dichotomy model. In addition, the influence of 10 factors, including environmental factors and human factors, on the spatial-temporal variations of vegetation coverage was quantified by geodetector. The results showed that average vegetation coverage was 35.8% in the study area from 1989 to 2021. On the temporal scale, it showed an increasing trend, with an average increasing rate of 0.043·(10 a)-1. On the spatial scale, vegetation coverage presented a distribution characteristic of decreasing from southwest to northeast. 58.1% of vegetation coverage in the study area would continue to improve in the future, but 30.7% of vegetation would have the potential risk of degradation. Precipitation was the dominant environmental factor driving the distribution of vegetation. Compared with single factor, the interaction between environmental factors and human factors had a stronger impact on vegetation coverage, while the interaction between precipitation and other factors played a leading role.

A RIPK1-regulated inflammatory microglial state in amyotrophic lateral sclerosis.

Microglial-derived inflammation has been linked to a broad range of neurodegenerative and neuropsychiatric conditions, including amyotrophic lateral sclerosis (ALS). Using single-cell RNA sequencing, a class of Disease-Associated Microglia (DAMs) have been characterized in neurodegeneration. However, the DAM phenotype alone is insufficient to explain the functional complexity of microglia, particularly with regard to regulating inflammation that is a hallmark of many neurodegenerative diseases. Here, we identify a subclass of microglia in mouse models of ALS which we term RIPK1-Regulated Inflammatory Microglia (RRIMs). RRIMs show significant up-regulation of classical proinflammatory pathways, including increased levels of Tnf and Il1b RNA and protein. We find that RRIMs are highly regulated by TNFα signaling and that the prevalence of these microglia can be suppressed by inhibiting receptor-interacting protein kinase 1 (RIPK1) activity downstream of the TNF receptor 1. These findings help to elucidate a mechanism by which RIPK1 kinase inhibition has been shown to provide therapeutic benefit in mouse models of ALS and may provide an additional biomarker for analysis in ongoing phase 2 clinical trials of RIPK1 inhibitors in ALS.

Reduction of mNAT1/hNAT2 Contributes to Cerebral Endothelial Necroptosis and Aß Accumulation in Alzheimer's Disease.

The contribution and mechanism of cerebrovascular pathology in Alzheimer's disease (AD) pathogenesis are still unclear. Here, we show that venular and capillary cerebral endothelial cells (ECs) are selectively vulnerable to necroptosis in AD. We identify reduced cerebromicrovascular expression of murine N-acetyltransferase 1 (mNat1) in two AD mouse models and hNat2, the human ortholog of mNat1 and a genetic risk factor for type-2 diabetes and insulin resistance, in human AD. mNat1 deficiency in Nat1-/- mice and two AD mouse models promotes blood-brain barrier (BBB) damage and endothelial necroptosis. Decreased mNat1 expression induces lysosomal degradation of A20, an important regulator of necroptosis, and LRP1ß, a key component of LRP1 complex that exports Aß in cerebral ECs. Selective restoration of cerebral EC expression of mNAT1 delivered by adeno-associated virus (AAV) rescues cerebromicrovascular levels of A20 and LRP1ß, inhibits endothelial necroptosis and activation, ameliorates mitochondrial fragmentation, reduces Aß deposits, and improves cognitive function in the AD mouse model.

SIMPLEs: a single-cell RNA sequencing imputation strategy preserving gene modules and cell clusters variation.

A main challenge in analyzing single-cell RNA sequencing (scRNA-seq) data is to reduce technical variations yet retain cell heterogeneity. Due to low mRNAs content per cell and molecule losses during the experiment (called 'dropout'), the gene expression matrix has a substantial amount of zero read counts. Existing imputation methods treat either each cell or each gene as independently and identically distributed, which oversimplifies the gene correlation and cell type structure. We propose a statistical model-based approach, called SIMPLEs (SIngle-cell RNA-seq iMPutation and celL clustErings), which iteratively identifies correlated gene modules and cell clusters and imputes dropouts customized for individual gene module and cell type. Simultaneously, it quantifies the uncertainty of imputation and cell clustering via multiple imputations. In simulations, SIMPLEs performed significantly better than prevailing scRNA-seq imputation methods according to various metrics. By applying SIMPLEs to several real datasets, we discovered gene modules that can further classify subtypes of cells. Our imputations successfully recovered the expression trends of marker genes in stem cell differentiation and can discover putative pathways regulating biological processes.

Convergent regulatory evolution and loss of flight in paleognathous birds.

A core question in evolutionary biology is whether convergent phenotypic evolution is driven by convergent molecular changes in proteins or regulatory regions. We combined phylogenomic, developmental, and epigenomic analysis of 11 new genomes of paleognathous birds, including an extinct moa, to show that convergent evolution of regulatory regions, more so than protein-coding genes, is prevalent among developmental pathways associated with independent losses of flight. A Bayesian analysis of 284,001 conserved noncoding elements, 60,665 of which are corroborated as enhancers by open chromatin states during development, identified 2355 independent accelerations along lineages of flightless paleognaths, with functional consequences for driving gene expression in the developing forelimb. Our results suggest that the genomic landscape associated with morphological convergence in ratites has a substantial shared regulatory component.

Bayesian Detection of Convergent Rate Changes of Conserved Noncoding Elements on Phylogenetic Trees.

Conservation of DNA sequence over evolutionary time is a strong indicator of function, and gain or loss of sequence conservation can be used to infer changes in function across a phylogeny. Changes in evolutionary rates on particular lineages in a phylogeny can indicate shared functional shifts, and thus can be used to detect genomic correlates of phenotypic convergence. However, existing methods do not allow easy detection of patterns of rate variation, which causes challenges for detecting convergent rate shifts or other complex evolutionary scenarios. Here we introduce PhyloAcc, a new Bayesian method to model substitution rate changes in conserved elements across a phylogeny. The method assumes several categories of substitution rate for each branch on the phylogenetic tree, estimates substitution rates per category, and detects changes of substitution rate as the posterior probability of a category switch. Simulations show that PhyloAcc can detect genomic regions with rate shifts in multiple target species better than previous methods and has a higher accuracy of reconstructing complex patterns of substitution rate changes than prevalent Bayesian relaxed clock models. We demonstrate the utility of PhyloAcc in two classic examples of convergent phenotypes: loss of flight in birds and the transition to marine life in mammals. In each case, our approach reveals numerous examples of conserved nonexonic elements with accelerations specific to the phenotypically convergent lineages. Our method is widely applicable to any set of conserved elements where multiple rate changes are expected on a phylogeny.

A novel epigenetic AML1-ETO/THAP10/miR-383 mini-circuitry contributes to t(8;21) leukaemogenesis.

DNA methylation patterns are frequently deregulated in t(8;21) acute myeloid leukaemia (AML), but little is known of the mechanisms by which specific gene sets become aberrantly methylated. Here, we found that the promoter DNA methylation signature of t(8;21)+ AML blasts differs from that of t(8;21)- AMLs. This study demonstrated that a novel hypermethylated zinc finger-containing protein, THAP10, is a target gene and can be epigenetically suppressed by AML1-ETO at the transcriptional level in t(8;21) AML. Our findings also show that THAP10 is a bona fide target of miR-383 that can be epigenetically activated by the AML1-ETO recruiting co-activator p300. In this study, we demonstrated that epigenetic suppression of THAP10 is the mechanistic link between AML1-ETO fusion proteins and tyrosine kinase cascades. In addition, we showed that THAP10 is a nuclear protein that inhibits myeloid proliferation and promotes differentiation both in vitro and in vivo Altogether, our results revealed an unexpected and important epigenetic mini-circuit of AML1-ETO/THAP10/miR-383 in t(8;21) AML, in which epigenetic suppression of THAP10 predicts a poor clinical outcome and represents a novel therapeutic target.

3CPET: finding co-factor complexes from ChIA-PET data using a hierarchical Dirichlet process.

Various efforts have been made to elucidate the cooperating proteins involved in maintaining chromatin interactions; however, many are still unknown. Here, we present 3CPET, a tool based on a non-parametric Bayesian approach, to infer the set of the most probable protein complexes involved in maintaining chromatin interactions and the regions that they may control, making it a valuable downstream analysis tool in chromatin conformation studies. 3CPET does so by combining data from ChIA-PET, transcription factor binding sites, and protein interactions. 3CPET results show biologically significant and accurate predictions when validated against experimental and simulation data.

Small C-terminal Domain Phosphatase 3 Dephosphorylates the Linker Sites of Receptor-regulated Smads (R-Smads) to Ensure Transforming Growth Factor ß (TGFß)-mediated Germ Layer Induction in Xenopus Embryos.

Germ layer induction is one of the earliest events shortly after fertilization that initiates body formation of vertebrate embryos. In Xenopus, the maternally deposited transcriptional factor VegT promotes the expression of zygotic Nodal/Activin ligands that further form a morphogen gradient along the vegetal-animal axis and trigger the induction of the three germ layers. Here we found that SCP3 (small C-terminal domain phosphatase 3) is maternally expressed and vegetally enriched in Xenopus embryos and is essential for the timely induction of germ layers. SCP3 is required for the full activation of Nodal/Activin and bone morphogenetic protein signals and functions via dephosphorylation in the linker regions of receptor-regulated Smads. Consistently, the linker regions of receptor-regulated Smads are heavily phosphorylated in fertilized eggs, and this phosphorylation is gradually removed when embryos approach the midblastula transition. Knockdown of maternal SCP3 attenuates these dephosphorylation events and the activation of Nodal/Activin and bone morphogenetic protein signals after midblastula transition. This study thus suggested that the maternal SCP3 serves as a vegetally enriched, intrinsic factor to ensure a prepared status of Smads for their activation by the upcoming ligands during germ layer induction of Xenopus embryos.