Miracle-dz/UniFAR-FTUs huggingface dataset:
The Plasminogen-Apple-Nematode (PAN) domain suppresses JA/ET defense pathways in plants

Suppression of immune response is a phenomenon that enables biological processes such as gamete fertilization, cell growth, cell proliferation, endophyte recruitment, parasitism, and pathogenesis. Here, we show for the first time that the Plasminogen-Apple-Nematode (PAN) domain present in G-type lectin receptor-like kinases is essential for immunosuppression in plants. Defense pathways involving jasmonic acid and ethylene are critical for plant immunity against microbes, necrotrophic pathogens, parasites, and insects. Using two Salix purpurea G-type lectin receptor kinases, we demonstrated that intact PAN domains suppress jasmonic acid and ethylene signaling in Arabidopsis and tobacco. Variants of the same receptors with mutated residues in this domain could trigger induction of both defense pathways. Assessment of signaling processes revealed significant differences between receptors with intact and mutated PAN domain in MAPK phosphorylation, global transcriptional reprogramming, induction of downstream signaling components, hormone biosynthesis and resistance to Botrytis cinerea. Further, we demonstrated that the domain is required for oligomerization, ubiquitination, and proteolytic degradation of these receptors. These processes were completely disrupted when conserved residues in the domain were mutated. Additionally, we have tested the hypothesis in recently characterized Arabidopsis mutant which has predicted PAN domain and negatively regulates plant immunity against root nematodes. ern1.1 mutant complemented with mutated PAN shows triggered immune response with elevated WRKY33 expression, hyperphosphorylation of MAPK and resistant to necrotrophic fungus Botrytis cinerea. Collectively, our results suggest that ubiquitination and proteolytic degradation mediated by the PAN domain plays a role in receptor turn-over to suppress jasmonic acid and ethylene defense signaling in plants.
Heat-evolved algal symbionts enhance bleaching tolerance of adult corals without trade-off against growth

Ocean warming has caused coral mass bleaching and mortality worldwide and the persistence of symbiotic reef-building corals requires rapid acclimation or adaptation. Experimental evolution of the coral’s microalgal symbionts followed by their introduction into coral is one potential method to enhance coral thermotolerance. Heat-evolved microalgal symbionts of the generalist species, Cladocopium proliferum (strain SS8), were exposed to elevated temperature (31°C) for ∼10 years, and were introduced into chemically bleached adult fragments of the scleractinian coral, Galaxea fascicularis. The new symbionts persisted for the five months of the experiment and enhanced adult coral thermotolerance compared with corals that were inoculated with the wild-type C. proliferum strain. Thermotolerance of SS8-corals was similar to that of coral fragments from the same colony hosting the homologous symbiont, Durusdinium sp. , which is naturally heat-tolerant. However, SS8-coral fragments exhibited faster growth and recovered cell density and photochemical efficiency more quickly following chemical bleaching and inoculation under ambient temperature relative to Durusdinium-corals. Mass spectrometry imaging suggests that algal pigments involved in photobiology and oxidative stress were the greatest contributors to the thermotolerance differences between coral hosting heat-evolved versus wild-type C. proliferum. These pigments may have increased photoprotection in the heat-evolved symbionts. Our findings show that adult coral thermotolerance can be enhanced via the uptake of exogenously supplied, heat-evolved symbionts, without a trade-off against growth under ambient temperature. Heat-evolved C. proliferum remains in the corals in moderate abundance two years after its first inoculation, suggesting long-term stability of this novel symbiosis.
A functional single nucleotide polymorphism upstream of the collagen type III gene may contribute to catastrophic fracture risk in Thoroughbred horses

Fractures caused by bone overloading are a leading cause of euthanasia in Thoroughbred racehorses. The risk of fatal fracture has been shown to be influenced by both environmental and genetic factors but, to date, no specific genetic mechanisms underpinning fracture have been identified. The aim of this study was to utilise a genome-wide polygenic risk score to establish an in vitro cell system to study bone gene regulation in horses at high and low genetic risk of fracture. Candidate gene expression analysis revealed differential expression of COL3A1 and STAT1 genes in osteoblasts derived from high and low risk horses. Whole genome sequencing of fracture case and control horses revealed a single nucleotide polymorphism (SNP) upstream of COL3A1 that was confirmed in a larger cohort to be significantly associated with fracture. Bioinformatics tools predicted that this SNP may impact the binding of the transcription factor SOX11. Gene modulation demonstrated SOX11 is upstream of COL3A1 and the region binds to nuclear proteins. Furthermore, luciferase assays demonstrated that the region containing the SNP has promoter activity. However, the specific effect of the SNP depends on the broader genetic background of the cells and suggest other factors may also be involved in regulating COL3A1 expression. In conclusion, this approach provides a powerful means to identify novel DNA variants and understand their mechanism of action to enable the development of new ways to identify and treat horses at high risk of a catastrophic fracture.
Predicting Eastern Mediterranean Flash Floods Using Support Vector Machines with Precipitable Water Vapor, Pressure, and Lightning Data

Flash floods in the Eastern Mediterranean (EM) region are considered among the most destructive natural hazards, which pose a significant challenge to model due to their high complexity. Machine learning (ML) methods have made a significant contribution to the advancement of flash flood prediction systems by providing cost-effective solutions with improved performance, enabling the modeling of the complex mathematical expressions underlying physical processes of flash floods. Thus, the development of ML methods for flash flood prediction holds the potential to mitigate risks, inform policy recommendations, minimize loss of human life, and reduce property damage caused by flash floods. Here, we present a novel approach for improving flash flood predictions in the EM region using Support Vector Machines (SVMs) with a combination of precipitable water vapor (PWV) data, derived from ground-based global navigation satellite system (GNSS) receivers, along with surface pressure measurements, and nearby lightning occurrence data to predict flash floods in an arid region of the EM. The SVM model was trained on historical data from 2004 to 2019 and was used to forecast the likelihood of flash floods in the region. The study found that integrating nearby lightning data with the other variables significantly improved the accuracy of flash flood prediction compared to using only PWV and surface pressure measurements. The results of the SVM model were validated using observed flash flood events, and the model was found to have a high predictive accuracy with an area under the receiver operating characteristic curve of 0.93 for the test set. The study provides valuable insights into the potential of utilizing a combination of meteorological and lightning data for improving flash flood forecasting in the Eastern Mediterranean region.
Trie-Compressed Adaptive Set Intersection

We introduce space-and time-efficient algorithms and data structures for the offline set intersection problem. We show that a sorted integer set S ⊆ [0 . u ) of n elements can be represented using compressed space while supporting k -way intersections in adaptive O( kδ lg( u/δ )) time, δ being the alternation measure introduced by Barbay and Kenyon. Our experimental results suggest that our approaches are competitive in practice, outperforming the most efficient alternatives (Partitioned Elias-Fano indexes, Roaring Bitmaps, and Recursive Universe Partitioning (RUP)) in several scenarios, offering in general relevant space-time trade-offs. 2012 ACM Subject Classification
Robust Approach for Global Stabilization of a Class of Underactuated Mechanical Systems in Presence of Uncertainties

Underactuated mechanical systems offer complex dynamic behavior and poses control challenges, especially in the presence of uncertainties in the system. To cope with such systems, control mechanisms are required, which needs to be robust. In this research, an algorithm based on sliding mode control (SMC) is presented. The algorithm design offer a methodical way to handle underactuation, while the robustness properties of SMC suppresses the effect of norm bounded uncertainties and external disturbances. To proceed with the design, an underactuated system is converted into cascaded subsystems, a linear one and reduced-order nonlinear subsystem. The proposed SMC design is backed by rigorous mathematical presentation and based on Lyapunov theory, so that the global stabilization of overall system is ensured. Numerical simulations are performed, on the laboratory test bench underactuated systems (Inertial Wheel, Furuta Pendulum, TORA, and an Overhead Crane), to validate the efficacy of the proposed design. In addition, a novel sliding surface is presented for Inertial Wheel and Furuta Pendulum to achieve swingup control and global stabilization subjected to uncertainties.
Impacts of Climate Change and Human Activities on Plant Species α-Diversity across the Tibetan Grasslands

Plant species α-diversity is closely correlated with ecosystem structures and functions. However, whether climate change and human activities will reduce plant species α-diversity remains controversial. In this study, potential (i.e., potential species richness: SRp, Shannonp, Simpsonp and Pieloup) and actual plant species α-diversity (i.e., actual species richness: SRa, Shannona, Simpsona and Pieloua) during 2000–2020 were quantified based on random forests in grasslands on the Tibetan Plateau. Overall, climate change had positive influences on potential plant species α-diversity across all the grassland systems. However, more than one-third areas showed decreasing trends for potential plant species α-diversity. Climate change increased the SRp at rates of 0.0060 and 0.0025 yr−1 in alpine steppes and alpine meadows, respectively. Temperature change predominated the variations of Shannonp and Simpsonp, and radiation change predominated the variations of SRp and Pieloup. Geography position, local temperature, precipitation and radiation conditions regulated the impacts of climate change on potential species α-diversity. On average, human activities caused 1% plant species loss but elevated the Shannon, Simpson and Pielou by 26%, 4% and 5%, respectively. There were 46.51%, 81.08%, 61.26% and 61.10% areas showing positive effects of human activities on plant species richness, Shannon, Simpson and Pielou, respectively. There were less than 48% areas showing increasing trends of human activities’ impacts on plant species α-diversity. Human activities increased plant species richness by 2% in alpine meadows but decreased plant species richness by 1% in alpine steppes. Accordingly, both the impacts of climate change and human activities on plant species α-diversity were not always negative and varied with space and grassland types. The study warned that both climate change and human activities may not cause as much species loss as expected. This study also cautioned that the impacts of radiation change on plant species α-diversity should be at least put on the same level as the impacts of climate warming and precipitation change on plant α-diversity.
Autonomous Multi-Floor Localization Based on Smartphone-Integrated Sensors and Pedestrian Indoor Network

Autonomous localization without local wireless facilities is proven as an efficient way for realizing location-based services in complex urban environments. The precision of the current map-matching algorithms is subject to the poor ability of integrated sensor-based trajectory estimation and the efficient combination of pedestrian motion information and the pedestrian indoor network. This paper proposes an autonomous multi-floor localization framework based on smartphone-integrated sensors and pedestrian network matching (ML-ISNM). A robust data and model dual-driven pedestrian trajectory estimator is proposed for accurate integrated sensor-based positioning under different handheld modes and disturbed environments. A bi-directional long short-term memory (Bi-LSTM) network is further applied for floor identification using extracted environmental features and pedestrian motion features, and further combined with the indoor network matching algorithm for acquiring accurate location and floor observations. In the multi-source fusion procedure, an error ellipse-enhanced unscented Kalman filter is developed for the intelligent combination of a trajectory estimator, human motion constraints, and the extracted pedestrian network. Comprehensive experiments indicate that the presented ML-ISNM achieves autonomous and accurate multi-floor positioning performance in complex and large-scale urban buildings. The final evaluated average localization error was lower than 1.13 m without the assistance of wireless facilities or a navigation database.
Usher syndrome proteins ADGRV1 (USH2C) and CIB2 (USH1J) interact and share a common interactome containing TRiC/CCT-BBS chaperonins

The human Usher syndrome (USH) is the most common form of a sensory hereditary ciliopathy characterized by progressive vision and hearing loss. Mutations in the genes ADGRV1 and CIB2 have been associated with two distinct sub-types of USH, namely, USH2C and USH1J. The proteins encoded by the two genes belong to very distinct protein families: the adhesion G protein-coupled receptor ADGRV1 also known as the very large G protein-coupled receptor 1 (VLGR1) and the Ca2+- and integrin-binding protein 2 (CIB2), respectively. In the absence of tangible knowledge of the molecular function of ADGRV1 and CIB2, pathomechanisms underlying USH2C and USH1J are still unknown. Here, we aimed to enlighten the cellular functions of CIB2 and ADGRV1 by the identification of interacting proteins, a knowledge that is commonly indicative of cellular functions. Applying affinity proteomics by tandem affinity purification in combination with mass spectrometry, we identified novel potential binding partners of the CIB2 protein and compared these with the data set we previously obtained for ADGRV1. Surprisingly, the interactomes of both USH proteins showed a high degree of overlap indicating their integration in common networks, cellular pathways and functional modules which we confirmed by GO term analysis. Validation of protein interactions revealed that ADGRV1 and CIB2 mutually interact. In addition, we showed that the USH proteins also interact with the TRiC/CCT chaperonin complex and the Bardet Biedl syndrome (BBS) chaperonin-like proteins. Immunohistochemistry on retinal sections demonstrated the co-localization of the interacting partners at the photoreceptor cilia, supporting the role of USH proteins ADGRV1 and CIB2 in primary cilia function. The interconnection of protein networks involved in the pathogenesis of both syndromic retinal dystrophies BBS and USH suggest shared pathomechanisms for both syndromes on the molecular level.
Pyridylnidulin exerts anti-diabetic properties and improves non-alcoholic fatty liver disease in diet-induced obesity mice

Introduction: Non-alcoholic fatty liver disease (NAFLD) is one of the metabolic disorders related to the pathophysiology of type 2 diabetes mellitus (T2DM). Therapeutic strategies are focused on the improvement of energy balance and lifestyle modification. Additionally, the derivative of the bioactive fungal metabolite is of interest to provide health benefits, especially in obese and pre-diabetic conditions. In our screening of anti-diabetic compounds from fungal metabolites and semisynthetic derivatives, a depsidone derivative, namely pyridylnidulin (PN), showed potent glucose uptake-inducing activity. The present study aimed to investigate the liver lipid metabolism and anti-diabetic properties of PN in diet-induced obesity mice. Methods: Male C57BL/6 mice were induced obesity and pre-diabetic conditions by dietary intervention with a high-fat diet (HFD) for 6 weeks. These obese mice were orally administered with PN (40 or 120 mg/kg), metformin (150 mg/kg), or vehicle for 4 weeks. Glucose tolerance, plasma adipocytokines, hepatic gene and protein expressions were assessed after treatment. Results: Improved glucose tolerance and reduced fasting blood glucose levels were found in the PN and metformin-treated mice. Additionally, hepatic triglyceride levels were consistent with the histopathological steatosis score regarding hepatocellular hypertrophy in the PN and metformin groups. The levels of plasma adipocytokines such as tumor necrosis factor-α (TNF-α) and monocyte chemoattractant protein-1 (MCP-1) were reduced in the PN (120 mg/kg) and metformin-treated mice. In addition, hepatic gene expression involved in lipid metabolism, including lipogenic enzymes was significantly reduced in the PN (120 mg/kg) and metformin-treated mice. The increased protein expression levels of phosphorylated AMP-activated protein kinase (p-AMPK) was also found in PN and metformin-treated mice. Discussion: Considering the increased p-AMPK protein expression levels in PN and metformin-treated mice were revealed as the underlying mechanisms to improve metabolic parameters. These results suggested that PN provided the health benefit to slow the progression of NAFLD and T2DM in obese and pre-diabetic conditions.
Dynamics and Structure of Complex Networks

In this thesis the relationship between the structure of complex networks and the behavior of dynamical processes on these networks is studied by network evolution. Adopting ideas from biological evolution or Monte Carlo simulations the method of network evolution is developed as strategy to generate networks with a prescribed dynamical behavior without any prior knowledge about the structure-dynamics relation. Here, Laplacian dynamics are considered forming a basic - simple but highly relevant - class of dynamics. Networks are successfully evolved towards a power-law scaling in the spectrum of the graph Laplacian which defines the spectral dimension as important characteristic of diffusion behavior. The resulting evolved networks exhibit heterogeneous structures with densely connected cores and sparse peripheries. The influence of the degree distribution and degree correlations on the spectral behavior is investigated. To this end random networks with the degree distribution and degree correlations of the evolved networks are generated and the Laplacian spectra are compared. Finally, homogeneous networks with the prescribed power law in the Laplacian spectrum are generated by restricting the evolution to regular networks. The resulting evolved networks are found to be highly symmetric. The symmetry is exploited to construct quotient networks as systematic coarse-graining separating the Laplacian spectra into contributions from the repeating symmetric motifs on small scales and the underlying large-scale structures, which are particularly relevant in determining the spectral dimension.
Characterization of complex structural variation in the CYP2D6-CYP2D7-CYP2D8 gene loci using single-molecule long-read sequencing

Complex regions in the human genome such as repeat motifs, pseudogenes and structural (SVs) and copy number variations (CNVs) present ongoing challenges to accurate genetic analysis, particularly for short-read Next-Generation-Sequencing (NGS) technologies. One such region is the highly polymorphic CYP2D loci, containing CYP2D6, a clinically relevant pharmacogene contributing to the metabolism of >20% of common drugs, and two highly similar pseudogenes, CYP2D7 and CYP2D8. Multiple complex SVs, including CYP2D6/CYP2D7-derived hybrid genes are known to occur in different configurations and frequencies across populations and are difficult to detect and characterize accurately. This can lead to incorrect enzyme activity assignment and impact drug dosing recommendations, often disproportionally affecting underrepresented populations. To improve CYP2D6 genotyping accuracy, we developed a PCR-free CRISPR-Cas9 based enrichment method for targeted long-read sequencing that fully characterizes the entire CYP2D6-CYP2D7-CYP2D8 loci. Clinically relevant sample types, including blood, saliva, and liver tissue were sequenced, generating high coverage sets of continuous single molecule reads spanning the entire targeted region of up to 52 kb, regardless of SV present (n = 9). This allowed for fully phased dissection of the entire loci structure, including breakpoints, to accurately resolve complex CYP2D6 diplotypes with a single assay. Additionally, we identified three novel CYP2D6 suballeles, and fully characterized 17 CYP2D7 and 18 CYP2D8 unique haplotypes. This method for CYP2D6 genotyping has the potential to significantly improve accurate clinical phenotyping to inform drug therapy and can be adapted to overcome testing limitations of other clinically challenging genomic regions.
Technical feasibility of multimodal imaging in neonatal hypoxic-ischemic encephalopathy from an ovine model to a human case series

Hypoxic-ischemic encephalopathy (HIE) secondary to perinatal asphyxia occurs when the brain does not receive enough oxygen and blood. A surrogate marker for “intact survival” is necessary for the successful management of HIE. The severity of HIE can be classified based on clinical presentation, including the presence of seizures, using a clinical classification scale called Sarnat staging; however, Sarnat staging is subjective, and the score changes over time. Furthermore, seizures are difficult to detect clinically and are associated with a poor prognosis. Therefore, a tool for continuous monitoring on the cot side is necessary, for example, an electroencephalogram (EEG) that noninvasively measures the electrical activity of the brain from the scalp. Then, multimodal brain imaging, when combined with functional near-infrared spectroscopy (fNIRS), can capture the neurovascular coupling (NVC) status. In this study, we first tested the feasibility of a low-cost EEG-fNIRS imaging system to differentiate between normal, hypoxic, and ictal states in a perinatal ovine hypoxia model. Here, the objective was to evaluate a portable cot-side device and perform autoregressive with extra input (ARX) modeling to capture the perinatal ovine brain states during a simulated HIE injury. So, ARX parameters were tested with a linear classifier using a single differential channel EEG, with varying states of tissue oxygenation detected using fNIRS, to label simulated HIE states in the ovine model. Then, we showed the technical feasibility of the low-cost EEG-fNIRS device and ARX modeling with support vector machine classification for a human HIE case series with and without sepsis. The classifier trained with the ovine hypoxia data labeled ten severe HIE human cases (with and without sepsis) as the “hypoxia” group and the four moderate HIE human cases as the “control” group. Furthermore, we showed the feasibility of experimental modal analysis (EMA) based on the ARX model to investigate the NVC dynamics using EEG-fNIRS joint-imaging data that differentiated six severe HIE human cases without sepsis from four severe HIE human cases with sepsis. In conclusion, our study showed the technical feasibility of EEG-fNIRS imaging, ARX modeling of NVC for HIE classification, and EMA that may provide a biomarker of sepsis effects on the NVC in HIE.