Miracle-dz/UniFAR-FTUs huggingface dataset:
Massive colonization of protein-coding exons by selfish genetic elements in Paramecium germline genomes

Ciliates are unicellular eukaryotes with both a germline genome and a somatic genome in the same cytoplasm. The somatic macronucleus (MAC), responsible for gene expression, is not sexually transmitted but develops from a copy of the germline micronucleus (MIC) at each sexual generation. In the MIC genome of Paramecium tetraurelia, genes are interrupted by tens of thousands of unique intervening sequences, called Internal Eliminated Sequences (IESs), that have to be precisely excised during the development of the new MAC to restore functional genes. To understand the evolutionary origin of this peculiar genomic architecture, we sequenced the MIC genomes of nine Paramecium species (from ∼100 Mb in P. aurelia species to > 1.5 Gb in P. caudatum). We detected several waves of IES gains, both in ancestral and in more recent lineages. While the vast majority of IESs are single-copy in present-day genomes, we identified several families of mobile IESs, including non-autonomous elements acquired via horizontal transfer, that generated tens to thousands of new copies. These observations provide the first direct evidence that transposable elements can account for the massive proliferation of IESs in Paramecium. The comparison of IESs of different evolutionary ages indicates that, over time, IESs shorten and diverge rapidly in sequence while they acquire features that allow them to be more efficiently excised. We nevertheless identified rare cases of IESs that are under strong purifying selection across the aurelia clade. The cases examined contain or overlap cellular genes that are inactivated by excision during development, suggesting conserved regulatory mechanisms. Similar to the evolution of introns in eukaryotes, the evolution of Paramecium IESs highlights the major role played by selfish genetic elements in shaping the complexity of genome architecture and gene expression.
Infrared spectral markers for the nephroprotective effects of Ficus deltoidea in streptozotocin-induced diabetic rats

Fourier Transform Infrared (FTIR) is an established analytical technique to elucidate new discriminatory biomarkers. Our previous study showed that Ficus deltoidea (Ficus: Moraceae) is capable of increasing insulin secretion and improving tissue regeneration by reducing oxidative stress in diabetic rats. However, the assessment of treatment response is limited by the paucity of biomarkers. We aimed to evaluate the potential use of FTIR for assessing the nephroprotective effects of Ficus deltoidea (Ficus: Moraceae) in diabetic rats. A rat model of diabetes was induced using a single intraperitoneal injection of streptozotocin (STZ) (60 mg/kg body weight). Methanolic extract of F. deltoidea was administered orally at a dose of 1000 mg/kg body weight for eight weeks. Fasting blood glucose, serum insulin and kidney function parameters were examined. The kidneys were subsequently subjected to FTIR and histological analyses. Enzyme-linked immunosorbent assays (ELISA) assessed the levels of oxidative stress, antioxidant, and apoptosis-related proteins in the kidney tissue. The results show, for the first time, that there is a good agreement between changes in kidney and FTIR peaks. The IR peaks (1545 cm−1 and 1511 cm-1) corresponding to amide II were restored by treatment with F. deltoidea. Multivariate analysis demonstrated that the diabetic rats treated with F. deltoidea had similar clustering pattern that of the normal animals. Biochemical and histological examination further confirmed the nephroprotective effect of F. deltoidea. Thus, demonstrating how FTIR spectroscopy could be used for the diagnosis of diabetes kidney disease.
Distinguish virulent and temperate phage-derived sequences in metavirome data with a deep learning approach

Background Prokaryotic viruses referred to as phages can be divided into virulent and temperate phages. Distinguishing virulent and temperate phage-derived sequences in metavirome data is important for their role in interactions with bacterial hosts and regulations of microbial communities. However there is no experimental or computational approach to classify sequences of these two in culture-independent metavirome effectively, we present a new computational method DeePhage, which can directly and rapidly judge each read or contig as a virulent or temperate phage-derived fragment. Findings DeePhage utilizes a “one-hot” encoding form to have an overall and detailed representation of DNA sequences. Sequence signatures are detected via a deep learning algorithm, namely a convolutional neural network to extract valuable local features. DeePhage makes better performance than the most related method PHACTS. The accuracy of DeePhage on five-fold validation reach as high as 88%, nearly 30% higher than PHACTS. Evaluation on real metavirome shows DeePhage annotated 54.4% of reliable contigs while PHACTS annotated 44.5%. While running on the same machine, DeePhage reduces computational time than PHACTS by 810 times. Besides, we proposed a new strategy to explore phage transformations in the microbial community by direct detection of the temperate viral fragments from metagenome and metavirome. The detectable transformation of temperate phages provided us a new insight into the potential treatment for human disease. Conclusions DeePhage is the first tool that can rapidly and efficiently identify two kinds of phage fragments especially for metagenomics analysis with satisfactory performance. DeePhage is freely available via http://cqb.pku. edu.cn/ZhuLab/DeePhage or https://github.com/shufangwu/DeePhage.
Associative memory networks for graph-based abstraction

Our cognition relies on the ability of the brain to segment hierarchically structured events on multiple scales. Recent evidence suggests that the brain performs this event segmentation based on the structure of state-transition graphs behind sequential experiences. However, the underlying circuit mechanisms are only poorly understood. In this paper, we propose an extended attractor network model for the graph-based hierarchical computation, called as Laplacian associative memory. This model generates multiscale representations for communities (clusters) of associative links between memory items, and the scale is regulated by heterogenous modulation of inhibitory circuits. We analytically and numerically show that these representations correspond to graph Laplacian eigenvectors, a popular method for graph segmentation and dimensionality reduction. Finally, we demonstrate that our model with asymmetricity exhibits chunking resembling to hippocampal theta sequences. Our model connects graph theory and the attractor dynamics to provide a biologically plausible mechanism for abstraction in the brain.
Haplotype and population structure inference using neural networks in whole-genome sequencing data

Accurate inference of population structure is important in many studies of population genetics. Here we present, HaploNet, a method for performing dimensionality reduction and clustering of genetic data. The method is based on local clustering of phased haplotypes using neural networks from whole-genome sequencing or dense genotype data. By utilizing Gaussian mixtures in a variational autoencoder framework, we are able to learn a low-dimensional latent space in which we cluster haplotypes along the genome in a highly scalable manner. We demonstrate that we can use haplotype clusters in the latent space to infer global population structure utilizing haplotype information by exploiting the generative properties of our framework. Based on fitted neural networks and its latent haplotype clusters, we can perform principal component analysis and estimate ancestry proportions based on a maximum likelihood framework. Using sequencing data from simulations and closely related human populations, we demonstrate that our approach is better at distinguishing closely related populations than standard admixture and principal component analysis software. We further show that HaploNet is fast and highly scalable by applying it to genotype array data of the UK Biobank.
Myofiber / pro-inflammatory macrophage interplay controls muscle damage in mdx mice

Duchenne Muscular Dystrophy is a genetic muscle disease characterized by chronic inflammation and fibrosis, which is mediated by a pro-fibrotic macrophage population expressing pro-inflammatory markers. The aim of this study was to characterize cellular events leading to the alteration of macrophage properties, and to modulate macrophage inflammatory status using the gaseous mediator H2S. We first analyzed the relationship between myofibers and macrophages in the mdx mouse model of Duchenne Muscular Dystrophy using coculture experiments. We showed that normal myofibers derived from mdx mice strongly skewed the polarization of resting macrophages towards a pro-inflammatory phenotype. Treatment of mdx mice with NaHS, an H2S donor, reduced the number of pro-inflammatory macrophages in skeletal muscle, which was associated with a decrease in the number of nuclei per fiber, a reduction of myofiber branching and a reduced fibrosis. These results identify an interplay between myofibers and macrophages where dystrophic myofibers contribute to the maintenance of a highly inflammatory environment that skews the macrophage status, which in turn favors myofiber damage, myofiber branching and fibrosis establishment. They also identify H2S donors as a potential therapeutic strategy to improve dystrophic muscle phenotype by modulating macrophage inflammatory status.
Proteoforms of the SARS-CoV-2 nucleocapsid protein are primed to proliferate the virus and attenuate the antibody response

The SARS-CoV-2 nucleocapsid (N) protein is the most immunogenic of the structural proteins and plays essential roles in several stages of the virus lifecycle. It is comprised of two major structural domains: the RNA binding domain, which interacts with viral and host RNA, and the oligomerization domain which assembles to form the viral core. Here, we investigate the assembly state and RNA binding properties of the full-length nucleocapsid protein using native mass spectrometry. We find that dimers, and not monomers, of full-length N protein bind RNA, implying that dimers are the functional unit of ribonucleoprotein assembly. In addition, we find that N protein binds RNA with a preference for GGG motifs which are known to form short stem loop structures. Unexpectedly, we found that N undergoes proteolytic processing within the linker region, separating the two major domains. This process results in the formation of at least five proteoforms that we sequenced using electron transfer dissociation, higher-energy collision induced dissociation and corroborated by peptide mapping. The cleavage sites identified are in highly conserved regions leading us to consider the potential roles of the resulting proteoforms. We found that monomers of N-terminal proteoforms bind RNA with the same preference for GGG motifs and that the oligomeric state of a C-terminal proteoform (N156-419) is sensitive to pH. We then tested interactions of the proteoforms with the immunophilin cyclophilin A, a key component in coronavirus replication. We found that N1-209 and N1-273 bind directly to cyclophilin A, an interaction that is abolished by the approved immunosuppressant drug cyclosporin A. In addition, we found the C-terminal proteoform N156-419 generated the highest antibody response in convalescent plasma from patients >6 months from initial COVID-19 diagnosis when compared to the other proteoforms. Overall, the different interactions of N proteoforms with RNA, cyclophilin A, and human antibodies have implications for viral proliferation and vaccine development.
Remdesivir-ivermectin combination displays synergistic interaction with improved in vitro antiviral activity against SARS-CoV-2

A key element for the prevention and management of COVID-19 is the development of effective therapeutics. Drug combination strategies of repurposed drugs offer several advantages over monotherapies, including the potential to achieve greater efficacy, the potential to increase the therapeutic index of drugs and the potential to reduce the emergence of drug resistance. Here, we report on the in vitro synergistic interaction between two FDA approved drugs, remdesivir and ivermectin resulting in enhanced antiviral activity against SARS-CoV-2. These findings warrant further investigations into the clinical potential of this combination, together with studies to define the underlying mechanism.
RNA polymerase II assembly and mRNA decay regulation are mediated and interconnected via CTD Ser5P phosphatase Rtr1 in Saccharomyces cerevisiae

Rtr1 is an RNA pol II CTD-phosphatase that influences gene expression by acting during the transition from transcription initiation to elongation, and during transcription termination. Rtr1 has been proposed as an RNA pol II import factor in RNA pol II biogenesis, and participating in mRNA decay by autoregulating the turnover of its own mRNA. In addition, the interaction of Rtr1 with RNA pol II depends on the phosphorylation state of CTD, which also influences Rpb4/7 dissociation during transcription. In this work, we demonstrate that Rtr1 acts in RNA pol II assembly, likely in a final cytoplasmic RNA pol II biogenesis step, and mediates the Rpb4 association with the rest of the enzyme, However, we do not rule out discard a role in the Rpb4 association with RNA pol II in the nucleus. This role of Rtr1 interplays RNA pol II biogenesis and mRNA decay regulation. In fact, RTR1 deletion alters RNA pol II assembly and leads to the chromatin association of RNA pol II lacking Rpb4, in addition to whole RNA pol II, decreasing mRNA-Rpb4 imprinting and, consequently, increasing mRNA stability. Notably, the RPB5 overexpression that overcomes RNA pol II assembly and the defect in Rpb4 binding to chromatin-associated RNA pol II partially suppresses the mRNA stability defect of rtr1Δ cells. Our data also indicate that Rtr1 mediates mRNA decay regulation more broadly than previously proposed in cooperation with Rpb4 and Dhh1. Interestingly, these data include new layers in the crosstalk between mRNA synthesis and decay.
Styrene and ethylbenzene absorption in ionic liquids: comparing DFT affinity calculations with experimental data

Styrene is a widely used bulk chemical produced by dehydrogenation of ethylbenzene (EB). Purification of styrene to contain < 100 ppm EB is not cost-effective by conventional separation methods. One separation method is extractive distillation with an ionic liquid (IL) as a binding agent for one of the components, thereby lowering the vapour pressure of this component. In this study, using quantum density functional theory (DFT), we have simulated 22 IL anion–cation pairs, styrene and EB affinities to them, and ion-pair dimer affinities of the ILs. These are compared with experimental liquid–liquid equilibrium studies of M.T. G. Jongmans, B. Schuur, and A.B. de Haan, Ind. Eng. Chem. Res. 50 (2011), pp. 10800–10810. It is shown that experimental selectivity and distribution coefficients of styrene and EB in the ILs are related to computed gas phase anion–cation stabilisation energies and ion-pair–ion-pair dimer affinities. The inverse of molar volume is found to strongly correlate with the selectivity. The computational results also qualitatively correlate with molar volume, and consequently, it is possible to use DFT calculations as a qualitative prediction tool in screening of ILs for this separation process. This tool does not account for effects caused by long alkyl chains, as the length does not seem to affect dimer stabilisation energy beyond ethyl group.
Biophysical controls on CO2 fluxes of three Northern forests based on long-term eddy covariance data

Six to nine years of net ecosystem carbon exchange (NEE) data from forests in Hyytiälä in Finland, Sorø in Denmark and Norunda in Sweden were used to evaluate the interannual variation in the carbon balance. For half-monthly periods, average NEE was calculated for the night-time data. For the daytime data parameters were extracted for the relationship to photosynthetic active radiation (PAR). The standard deviation of the parameters was highest for Norunda where it typically was around 25% of the mean, while it was ca. 15% for Hyytiälä and Sorø. Temperature was the main controller of respiration and photosynthetic capacity in autumn, winter and spring but explained very little of the interannual variation in summer. A strong correlation between respiration and photosynthesis was also revealed. The start, end and length of the growing season were estimated by four different criteria. The start date could explain some of the variation in yearly total NEE and gross primary productivity (GPP) in Hyytiälä and Sorø, but the average maximum photosynthetic capacity in summer explained more of the variation in annual GPP for all sites than start, end or length of the growing season.
A spatial-spectral clustering-based algorithm for endmember extraction and hyperspectral unmixing

ABSTRACT Spectral variability existing in hyperspectral (HS) images reduces the accuracy of unmixing. To mitigate the effect of spectral variability on unmixing, we propose a spatial-spectral clustering-based method (called SP2C) for endmember extraction and HS unmixing. The proposed SP2C adopts a local spatial-spectral clustering strategy to obtain a set of spatially homogenous regions and exploits a spectral purity index calculation strategy to choose pure pixels on the regions, where the average spectra of chosen pure pixels on the regions are taken as endmember candidates to alleviate local spectral variability. Then, an adjusted k-means plus (called AD k-means++) algorithm is employed to cluster candidate endmembers to alleviate global spectral variability, and the final endmembers are achieved by matching the cluster centers and the reference endmembers obtained by the vertex component analysis (VCA)-like methods. Our experiment results, conducted using real HS datasets, confirm that the proposed method considerably improves the HS unmixing performance compared to the state-of-the-art techniques.
Prediction of Density and Solubility Parameter of Heavy Oils and SARA Fractions Using Cubic Equations of State

Abstract In this research, the density of the components of some crude oil samples, analyzed based on the Saturates, Aromatics, Resins, and Asphaltenes (SARA) method, has been estimated by Peng-Robinson (PR), Soave-Redlikh-Kwong (SRK), and new equations of state (EOS). The obtained results from the New EOS have been compared with those values obtained by PR and SRK, which showed better compatibility with the experimental data available. Then, the density and the solubility parameters of a different crude oil sample were estimated by the New EOS.
Self-supervised optical flow derotation network for rotation estimation of a spherical camera

ABSTRACT In this paper, we propose a self-supervised optical flow-based approach to learn the rotation of an arbitrarily moving spherical camera. Nowadays, deep learning has enabled efficient learning of camera rotation efficiently. However, most approaches are fully supervised and require large datasets with ground-truth labels of the rotation, and these labels are difficult to acquire. We attempt to solve this problem by using a derotation operation of the spherical optical flow on a unit sphere. This operation decouples the camera rotation from the mixture of translational and rotational components, removing the effect of 3D information for rotation estimation. Therefore, we integrate a derotation layer into a convolutional neural network for regressing the camera rotation. This layer can be adopted for only spherical cameras, which can capture all-round information, and thus enables the network to be learned the camera rotation without using labeled training datasets. We experimentally demonstrate that our approach achieves the comparable performance for the rotation estimation to that of a fully supervised approach and that it outperforms a previously proposed approach. Moreover, transfer learning is conducted in new environments to confirm the benefit of the self-supervised learning. GRAPHICAL ABSTRACT
Prevalence and patterns of physical activity among school aged adolescents in Pakistan: a systematic review and meta-analysis

ABSTRACT Less than one fifth of Pakistani adolescents, aged 13–15, years achieve recommended activity levels. This systematic review and meta-analysis was conducted to determine a pooled estimate of the prevalence of PA, and to systematically evaluate the literature available on PA among adolescents in Pakistan. A systemic search of databases was conducted. In addition, hand search of references of all the included relevant publications was performed. Random effects meta-analysis was used to get weighted prevalence of PA among adolescents. Quality of undertaken studies was assessed using New-Castle Ottawa Scale. After removing duplicates, reviewing titles and abstracts and screening full texts, 15 articles were included for analysis. All studies were conducted in school setting, with a total sample size of 10,651. Weighted pooled prevalence of PA among adolescents was 36.0% with high heterogeneity (99.28%). Most of the studies met study quality assessment criteria except for comparability of subjects in different outcome groups and assessment of outcome. Prevalence of PA among adolescents is low in Pakistan. Formal strategies are needed for promoting PA among adolescents for their improved health and for reducing future burden of NCDs.
Remote sensing versus the area sampling frame method in paddy rice acreage estimation in Indramayu regency, West Java province, Indonesia

ABSTRACT With a population of 267 million, Indonesia faces the significant challenge of inaccurate rice production data leading to a flawed national rice import policy and supply problems. Its 2018 rice production and harvest area data were generated through the Area Sampling Frame (ASF) method which incurred high labour and financial costs as well as failing to optimize accuracy; hence, an alternative method needs exploration. This study compares ASF and remote sensing Synthetic Aperture Radar (SAR) methods to calculate rice growth stages (RGS) using Indramayu Regency, the highest rice producer in West Java Province, as the study area. The SAR-based method used time-series of Vertical Horizontal (VH) polarization of Sentinel-1A data that employed a combination of k-means clustering, hierarchical cluster analysis (HCA), a visual interpretation and support vector machine (SVM) classifier. Both SAR and ASF methods can generate results on a monthly basis, although remote sensing satellite time revisits can be shortened (every 12 days). Whilst the ASF, a basic technique for collecting agricultural statistics, was easy to implement in large-scale areas its accuracy depended on the quantity and representativeness of the samples. This study applied the ASF by simulating a sample size of 1.7%, 3.3% and 5% of a rice field area with unmanned aerial vehicles (UAVs) data as a reference. Whilst remote sensing SAR methods involve complex data processing the image classification process can be conducted automatically and cost-effectively (data and its software are free of charge). Moreover, it yields not only statistical data on RGS but also determines the spatial planting patterns and the RGS distribution at 10 m pixel resolution. This method showed more accurate results with overall accuracy of image classification of 81.89% and a kappa coefficient (κ) of 0.73. The comparative result was relatively small, i.e., 4,094.89 ha more than the ASF results (3.5% difference), since this study covered a limited research area. Nonetheless, with evidence of more accurate results remote sensing holds the potential for replication across the country’s 416 regencies, so enabling government to develop more appropriate policies that minimize the risks of either a surplus or shortage in the nation’s most important food supply.