Miracle-dz/UniFAR-FTUs huggingface dataset:
Dissecting the genetic basis of the heterosis of Y900, an elite super-hybrid rice

Y900 is one of the top hybrid rice varieties with a yield exceeding 15 t/hm2. To dissect the mechanism of heterosis, the male parent line R900 and female parent line Y58S were sequenced using long-read and Hi-C technology. High-quality reference genomes of sizes of 396.41 Mb and 398.24 Mb were obtained for R900 and Y58S, respectively. Genome-wide variations between the parents were systematically identified, including 1,367,758 SNPs and 299,149 Indels. No megabase level structural variations exist. >75% of genes exhibited variation between the two parents. Compared with other two-line hybrids sharing the same female parent, the Geng/japonica-type genetic components from different male parents showed an increasing trend from phase 2-4 super-hybrid rice; Transcriptome analysis revealed that additive and dominance effects are the main genetic effects that constitute the heterosis of Y900. Allele-specific expression patterns and expression regulation patterns are quite dynamic in different tissues. For young panicle tissues, cis-regulation is dominant, while trans-regulation is more popular in leaf issues. Overdominance is more likely regulated by the trans-regulation mechanism. The differential gene expression and regulation pattern are closely related to Geng/japonica introgression. Additionally, R900 contained several excellent japonica haplotypes, such as NAL1, OsSPL13, Ghd8, OsBRI1, and DTH2, which make a good complement to Y58S. The fine tune mechanism through dynamic expression or regulation pattern change, especially on some key functional genes, is the base for heterosis.
Genome Assembly of the Polyclad Flatworm Prostheceraeus crozieri

Polyclad flatworms are widely thought to be one of the least derived of the flatworm classes and, as such, are well placed to investigate evolutionary and developmental features such as spiral cleavage and larval diversification lost in other platyhelminths. Prostheceraeus crozieri, formerly Maritigrella crozieri, is an emerging model polyclad flatworm that already has some useful transcriptome data but, to date, no sequenced genome. We have used high molecular weight DNA extraction and long read PacBio sequencing to assemble the highly repetitive (67.9%) P. crozieri genome (2.07 Gb). We have annotated 43,325 genes, with 89.7% BUSCO completeness. Perhaps reflecting its large genome, introns were considerably larger than other free-living flatworms, but evidence of abundant transposable elements suggests genome expansion has been principally via transposable elements activity. This genome resource will be of great use for future developmental and phylogenomic research.
Cortical polarity ensures its own asymmetric inheritance in the stomatal lineage to pattern the leaf surface

Asymmetric cell divisions (ACDs) specify differential cell fates across kingdoms. In metazoans, preferential inheritance of fate determinants into one daughter cell frequently depends on polarity-cytoskeleton interactions (1, 2). Despite the prevalence of ACDs during plant development, evidence for analogous mechanisms that segregate fate determinants during ACD remain elusive. Here, we describe a mechanism in the Arabidopsis thaliana leaf epidermis that ensures unequal inheritance of a fate-enforcing polarity domain during the creation of stomata, essential two-celled valves that mediate gas exchange between the plant and environment. Formation of a plasma membrane-associated polarity domain, defined by BREAKING OF ASYMMETRY IN THE STOMATAL LINEAGE (BASL), overrides default division patterns in stomatal precursors. The polarity domain exerts this control by constraining formation of the preprophase band of microtubules that mark the cortical division site and are a hallmark of plant mitosis. Experimentally uncoupling preprophase band establishment from the polarity domain results in aberrant polarity inheritance and subsequent fate errors. Mechanistically, our analyses of the interactions between microtubules and BASL in native and heterologous contexts revealed that the stomatal lineage polarity domain locally depletes cortical microtubules by altering microtubule stability. As the inherited cortical BASL crescent scaffolds a MAPK cascade to suppress progenitor identity in one daughter post-division, we propose that BASL-microtubule interactions represent a novel strategy to link cell identity to division orientation. Together, our data highlight how a common biological module, coupling the cytoskeleton to fate segregation via cell polarity, has been configured to accommodate the unique features of plant development.
Visual and motor signatures of locomotion dynamically shape a population code for visual features in Drosophila

Natural vision is dynamic: as an animal moves, its visual input changes dramatically. How can the visual system reliably extract local features from an input dominated by self-generated signals? In Drosophila, diverse local visual features are represented by a group of projection neurons with distinct tuning properties. Here we describe a connectome-based volumetric imaging strategy to measure visually evoked neural activity across this population. We show that local visual features are jointly represented across the population, and that a shared gain factor improves trial-to-trial coding fidelity. A subset of these neurons, tuned to small objects, is modulated by two independent signals associated with self-movement, a motor-related signal and a visual motion signal. These two inputs adjust the sensitivity of these feature detectors across the locomotor cycle, selectively reducing their gain during saccades and restoring it during intersaccadic intervals. This work reveals a strategy for reliable feature detection during locomotion.
Effects of Long-Term Alcohol Consumption on Behavior in the P301S (Line PS19) Tauopathy Mouse Model

Alcohol consumption and misuse remain prevalent public health issues with recent reports of increased heavy consumption in older populations in the United States. Several studies have identified alcohol consumption as a risk factor for developing multiple forms of dementia. The behavioral and psychological symptoms of dementia (BPSD), such as changes in affective behavior (e.g. anxiety), precede and coincide with cognitive decline. While many studies have characterized the intersection of alcohol use and affective behaviors, little is known regarding alcohol consumption and BPSD. This study characterizes the impact of long-term alcohol consumption on various behaviors in the P301S (line PS19) tauopathy mouse model. Male and female P301S and littermate control mice underwent two-bottle choice intermittent access to alcohol for sixteen weeks starting at 12 weeks of age. There were no significant differences in total ethanol consumption between wildtype and P301S mice of the same sex; however, drinking behavior differed among genotypes, and female mice of each genotype drank significantly more ethanol than males of each genotype. Following drinking studies, mice were run through a battery of behavioral tests during a period of forced abstinence, including approach/avoidance assays, social behavior tests, and memory and cognition tests. Across these tests we observed differences between groups due to genotype, alcohol history, and interactions between alcohol exposure and genotype. These differences were not always consistent between the sexes. In total, this study reveals significant alcohol-tauopathy interactions in subsequent behavior, which may have implications for understanding how alcohol may impact BPSD in conditions associated with tauopathy like Alzheimer’s disease and frontotemporal dementia.
Peptide-binding specificity prediction using fine-tuned protein structure prediction networks

Peptide binding proteins play key roles in biology, and predicting their binding specificity is a long-standing challenge. While considerable protein structural information is available, the most successful current methods use sequence information alone, in part because it has been a challenge to model the subtle structural changes accompanying sequence substitutions. Protein structure prediction networks such as AlphaFold model sequence-structure relationships very accurately, and we reasoned that if it were possible to specifically train such networks on binding data, more generalizable models could be created. We show that placing a classifier on top of the AlphaFold network and fine-tuning the combined network parameters for both classification and structure prediction accuracy leads to a model with strong generalizable performance on a wide range of Class I and Class II peptide-MHC interactions that approaches the overall performance of the state-of-the-art NetMHCpan sequence-based method. The peptide-MHC optimized model shows excellent performance in distinguishing binding and non-binding peptides to SH3 and PDZ domains. This ability to generalize well beyond the training set far exceeds that of sequence only models, and should be particularly powerful for systems where less experimental data is available. Significance statement Peptide binding proteins carry out a variety of biological functions in cells and predicting their binding specificity could significantly improve our understanding of molecular pathways. Deep neural networks have achieved high structure prediction accuracy, but are not trained to predict binding specificity. Here we describe an approach to extending such networks to jointly predict protein structure and binding specificity. We incorporate AlphaFold into this approach, and fine-tune its parameters on peptide-MHC Class I and II structural and binding data. The fine-tuned model approaches state-of-the-art classification accuracy on peptide-MHC specificity prediction and generalizes to other peptide-binding systems such as the PDZ and SH3 domains.
Rapid protein stability prediction using deep learning representations

Predicting the thermodynamic stability of proteins is a common and widely used step in protein engineering, and when elucidating the molecular mechanisms behind evolution and disease. Here, we present RaSP, a method for making rapid and accurate predictions of changes in protein stability by leveraging deep learning representations. RaSP performs on-par with biophysics-based methods and enables saturation mutagenesis stability predictions in less than a second per residue. We use RaSP to calculate ∼ 300 million stability changes for nearly all single amino acid changes in the human proteome, and examine variants observed in the human population. We find that variants that are common in the population are substantially depleted for severe destabilization, and that there are substantial differences between benign and pathogenic variants, highlighting the role of protein stability in genetic diseases. RaSP is freely available—including via a Web interface—and enables large-scale analyses of stability in experimental and predicted protein structures.
A hybrid approach for predicting transcription factors

Transcription factors (TFs) are essential DNA-binding proteins that regulate the rate of transcription of several genes and controls the expression of genes inside a cell. The prediction of TFs with high precision is important for understanding number of biological processes such as cell-differentiation, intracellular signaling, cell-cycle control. In this study, we developed a hybrid method that combine alignment-based and alignment-free methods for predicting transcription factors with higher accuracy. All models have been trained, tested and evaluated on a large dataset that contain 19406 TFs and 523560 non-TFs protein sequences. In order to avoid biasness in evaluation, dataset is divided in training and validation/independent dataset, where 80% data was used for training and remaining 20% for external validation. In case of alignment-free methods, models are developed based on machine learning techniques using compositional features of a protein. Our best alignment-free model obtained AUC 0.97 on independent dataset. In case of alignment-based method, we used BLAST at different cut-off to predict transcription factors. Though alignment-based method shows excellent performance but unable to cover all transcription factor due to no-hits. In order to combine power of both, we developed a hybrid method that combine alignment-free and alignment-based method; achieved maximum AUC of 0.99 on independent dataset. The method proposed in this study perform better than existing methods. We incorporated the best models in the webserver/standalone package “TransFacPred” (https://webs.iiitd.edu.in/raghava/transfacpred). Key Points Transcription factors (TFs) are vital DNA-binding proteins. A hybrid method for the prediction of TFs using sequence information. Computer-aided model were developed using machine-learning algorithm to predict TFs. Alignment-based and alignment-free approaches were used for the prediction. A user-friendly webserver, python- and Perl-based standalone package available.
Structure and dynamic association of an assembly platform subcomplex of the bacterial type II secretion system

Type II secretion systems (T2SS) allow diderm bacteria to secrete hydrolytic enzymes, adhesins or toxins important for growth and virulence. In T2SS, secretion of folded proteins from the periplasm to the cell surface requires assembly of periplasmic filaments called pseudopili. Like the related type IV pili, pseudopili are polymerized in the inner membrane through addition of subunits at the filament base, mediated by the essential assembly platform (AP). To understand the structure and molecular role of the AP, we focused on its components PulL and PulM from the Klebsiella oxytoca T2SS. By combining biophysical methods, NMR and X-ray crystallography we studied the structure and associations of their periplasmic domains. We describe the first structure of the heterodimer complex formed by the PulL and PulM ferredoxin-like domains and show how their structural complementarity and plasticity favor their association during the secretion process. Cysteine scanning and cross-linking of transmembrane segments provided additional constraints to build a structural model of the PulL–PulM complex and assembly in the cellular context. Together with the relative abundance of PulL, PulM and their partners our findings suggest a model of the AP as a dynamic hub that orchestrates pseudopilus polymerization.
Yttrium-Iron Garnet Magnetometer in MEG: Advance towards Multi-Channel Arrays

Recently, a new kind of sensor applicable in magnetoencephalography (MEG) has been presented: a solid-state yttrium-iron garnet magnetometer (YIGM). The feasibility of YIGM was proved in alpha-rhythm registration experiment. In the current paper, we propose the analysis of lead-field matrices for different possible YIGM multichannel on-scalp sensor layouts with respect to the information theory. We use real noise level of the new sensor to compute signal power, signal-to-noise ration (SNR) and total information capacity, and compare them with corresponding metrics that can be obtained with well-established MEG systems: based on superconducting quantum interference devices (SQUIDs) and optically-pumped magnetometers (OPMs). This simulation study is aimed to shed some light on the direction for further development of YIGM sensor, namely creation of multi-channel YIG-MEG.
Identification of Leukemia-Associated Immunophenotypes by Databaseguided Flow Cytometry Provides a Highly Sensitive and Reproducible Strategy for the Study of Measurable Residual Disease in Acute Myeloblastic Leukemia

Background Multiparametric Flow Cytometry (MFC) is an essential tool to study the involved cell lineages, the aberrant differentiation/maturation patterns and the expression of aberrant antigens in acute myeloid leukemia (AML). The characterization of leukemia-associated immunophenotypes (LAIPs) at the moment of diagnosis is critical to establish reproducible strategies for the study of measurable residual disease using MFC (MFC-MRD). Methods In this study, we identified and characterized LAIPs by comparing the leukemic populations of 145 AML patients, using the EuroFlow AML/ MDS MFC panel, with 6 databases of normal myeloid progenitors (MPCs). Principal component analysis was used to identify and characterize the LAIPs, which were then used to generate individual profiles for MFC-MRD monitoring. Furthermore, we investigated the relationship between the expression patterns of LAIPs and the different subtypes of AML. The MFC-MRD study was performed by identifying residual AML populations that matched with the LAIPs at diagnosis. To further validate this approach, the presence of MRD was also assessed by qPCR (qPCR-MRD). Finally, we studied the association between MFC-MRD and progression-free survival (PFS). Results The strategy used in this study allowed us to describe more than 300 different LAIPs and facilitated the association of specific phenotypes with certain subtypes of AML. The MFC-MRD monitoring based on LAIPs with good/strong specificity was applicable to virtually all patients and showed a good correlation with qPCR-MRD and PFS. Conclusions The described methodology provides an objective method to identify and characterize LAIPs. Furthermore, it provides a theoretical basis to develop highly sensitive MFC-MRD strategies.
scDREAMER: atlas-level integration of single-cell datasets using deep generative model paired with adversarial classifier

Integration of heterogeneous single-cell sequencing datasets generated across multiple tissue locations, time and conditions is essential for a comprehensive understanding of the cellular states and expression programs underlying complex biological systems. Here, we present scDREAMER (https://github.com/Zafar-Lab/scDREAMER), a novel data integration framework that employs a novel adversarial variational autoencoder for learning lower-dimensional cellular embeddings and a batch classifier neural network for the removal of batch effects. Using five real benchmarking datasets, we demonstrated that scDREAMER can overcome critical challenges including the presence of skewed cell types among batches, nested batch effects, large number of batches and conservation of development trajectory across different batches. Moreover, our benchmarking demonstrated that scDREAMER outperformed state-of-the-art methods in batch-correction and conservation of biological variation. Using a 1 million cells dataset, we further showed that scDREAMER is scalable and can perform atlas-level integration across species (e.g., human and mouse) while being faster than other deep-learning-based methods.
Current Tolerance Capability Calculation Model of Transmission Lines and its Application in Overload Protection

Abstract Traditional overload protection depends on pre-set values and cannot reflect the current tolerance capability of the line. If the current of a line exceeds the setting value of its overload protection, then it will be subsequently tripped off and cause power flow transferring, which may trigger the cascade tripping of power grid. So it is of significant importance for the overload protection to accurately evaluate the real-time safety status of transmission line and coordinate with power system control to eliminate the overload. In this article, starting from the mechanism analysis of conductor heat transfer, a thermal network model is established by focusing on radial and circumferential heat transfer paths. The temperature rise response of the network node is obtained through nonlinear iteration. The calculation model, which is verified by a test platform, is better than standard models. Then, the current tolerance capability calculation model of the whole line is constructed on the basis of the thermal network and applied to improve the overload protection. The proposed multi-parameter overload protection can effectively prevent unnecessary tripping to avoid the expansion of accidents. Finally, the validity of the proposed protection is verified by the New England 10-machine 39-bus test system.
A New In-Silico Approach for PROTAC Design and Quantitative Rationalization of PROTAC mediated Ternary Complex Formation

Proteolysis-Targeting Chimeric Molecules (PROTAC) is a rapidly emerging technology for drug target protein degradation and drugging undruggable drug targets. There is a growing literature on in-silico approaches to the complex problem of PROTAC design, specifically the advantages of AI/ in-silico methods in the PROTAC Design Make Test Analyze (DMTA) cycle. Our work presented here aims to contribute to the growing literature of in-silico approaches to PROTAC design by incorporating and demonstrating incremental advancement over previously published methods. We use AI based generative methods for PRTOAC design and Molecular Dynamics to evaluate the stability of the ternary complex formed and ability of the PROTAC to hold the target protein and E3 ligase together stably. To quantify the performance of the PROTAC candidate, we also estimate computationally the PROTAC performance metrics routinely measured by the experimentalists in PROTAC assays. We use highly accurate absolute binding free energy calculations used traditionally in protein-ligand space for the PROTAC system. We calculate (Gibbs free energy change) ΔG for binary complex formation and ternary complex formation mediated by the PROTAC using Free Energy Perturbation - Thermodynamics Integration (FEP-TI) method which is benchmarked in literature with a root mean square error of 0.8 kcal/mol. We calculate ΔG for ternary and binary complexes and estimate whether ΔG for ternary is lower than the ΔG estimated for binary complexes. When the ΔG for ternary is lower than the binary it is inferred that ternary complexation is favoured over binary. Therefore, through these methods we can theoretical estimate ΔG measured by experimentalists in PROTAC assays such as Isothermal titration calorimetry (ITC) and Surface plasmon resonance (SPR) which capture the ΔG for ternary and binary complex formation mediated by the PROTAC. This method will help reduce time as well as costs of the PROTAC DMTA cycle and will accelerate early stage PROTAC drug discovery. As an illustrative application of our in-silico PROTAC design approach, we chose the target Fibroblast growth factor receptor 1(FGFR-1) which is a target approved drug for colorectal cancer. We report the findings and conclude with future research directions.