Miracle-dz/UniFAR-FTUs huggingface dataset:
Roles of discoidin domain receptor 1 in gastrointestinal tumors

Discoidin domain receptors (DDRs) are receptor tyrosine kinases that recognize collagens as their ligands and are expressed in both epithelium and mesenchyme cells. DDR1 and DDR2 are two unique members of the DDRs family. Recent studies have demonstrated that DDR1 is closely associated with the proliferation, invasion, immigration, and epithelial-mesenchymal transformation (EMT) of solid tumor cells. Inhibiting the DDR1 activity can effectively suppress tumor growth by activating a variety of factors and signaling pathways. Novel selective DDR1 inhibitors may be promising strategies for cancer treatment. This article reviews the roles of DDR1 in gastrointestinal tumors.
Target Capture Sequencing Unravels Rubus Evolution

Background Rubus (Rosaceae) comprises more than 500 species with additional commercially cultivated raspberries and blackberries. The most recent (> 100 years old) global taxonomic treatment of the genus defined 12 subgenera; two subgenera were subsequently described and some species were rearranged. Intra- and interspecific ploidy levels and hybridization make phylogenetic estimation of Rubus challenging. Our objectives were to: estimate the phylogeny of 94 geographically diverse species and 3 cultivars using chloroplast DNA sequences and target capture of approximately 1,000 low copy nuclear genes; estimate divergence times between major Rubus clades; and examine the historical biogeography of species diversification. Results Target capture sequencing identified eight major groups within Rubus. Subgenus Orobatus and Subg. Anoplobatus were monophyletic, while other recognized subgenera were para- or polyphyletic. Multiple hybridization events likely occurred across the phylogeny at subgeneric levels, e.g., Subg. Rubus (blackberries) × Subg. Idaeobatus (raspberries) and Subg. Idaeobatus × Subg. Cylactis (Arctic berries) hybrids. The raspberry heritage within known cultivated blackberry hybrids was confirmed. The most recent common ancestor of the genus was most likely distributed in North America. Multiple distribution events occurred during the Miocene (about 20 Ma) from North America into Asia and Europe across the Bering land bridge and southward crossing the Panamanian Isthmus. Rubus species diversified greatly in Asia during the Miocene. Conclusions Rubus taxonomy does not reflect phylogenetic relationships and subgeneric revision is warranted. Target capture sequencing confirmed that most subgenera are para- or polyphyletic. The most recent common ancestor migrated from North America towards Asia, Europe, and Central and South America early in the Miocene then diversified. Ancestors of the genus Rubus may have migrated to Oceania by long distance bird dispersal. This phylogeny presents a roadmap for further Rubus taxonomic and phylogenetic research.
Functional diversity enhances, but exploitative traits reduce tree mixture effects on microbial biomass

Soil micro‐organisms play key roles in terrestrial biodiversity and ecosystem functions. Despite recent progress in elucidating the association between plant diversity and soil micro‐organisms, it remains unclear whether the functional properties of plant mixtures might alter this association. We examined whether the effects of tree species mixtures on soil microbial biomass were impacted by the functional diversity (FD) and community‐weighted mean (CWM) of tree mixtures, by conducting a global meta‐analysis involving 123 paired observations of tree mixtures and the corresponding monocultures from 38 studies in forests. We found that the tree mixture effect on microbial biomass increased with the FD of specific leaf area (SLA) and leaf N and P contents, as well as the FD based on all of these traits plus leaf dry matter content. Meanwhile, the responses of microbial biomass to tree mixtures decreased with the CWM of SLA and leaf N and P contents. The effects of FD and CWM remained consistent, despite variable tree species richness, stand age and climatic factors. Our results provide a new insight that the functional properties of plants may alter the magnitude of the association between plant diversity and soil micro‐organisms. A free Plain Language Summary can be found within the Supporting Information of this article.
A Histone Methyltransferase Inhibitor Can Reverse Epigenetically Acquired Drug Resistance in the Malaria Parasite Plasmodium falciparum

Malaria parasites invade and replicate within red blood cells (RBCs), extensively modifying their structure and gaining access to the extracellular environment by placing the plasmodial surface anion channel (PSAC) into the RBC membrane. Expression of members of the cytoadherence linked antigen gene 3 (clag3) family is required for PSAC activity, a process that is regulated epigenetically. PSAC is a well-established route of uptake for large, hydrophilic antimalarial compounds, and parasites can acquire resistance by silencing clag3 gene expression, thereby reducing drug uptake. ABSTRACT Malaria parasites invade and replicate within red blood cells (RBCs), extensively modifying their structure and gaining access to the extracellular environment by placing the plasmodial surface anion channel (PSAC) into the RBC membrane. Expression of members of the cytoadherence linked antigen gene 3 (clag3) family is required for PSAC activity, a process that is regulated epigenetically. PSAC is a well-established route of uptake for large, hydrophilic antimalarial compounds, and parasites can acquire resistance by silencing clag3 gene expression, thereby reducing drug uptake. We found that exposure to sub-IC50 concentrations of the histone methyltransferase inhibitor chaetocin caused substantial changes in both clag3 gene expression and RBC permeability, and reversed acquired resistance to the antimalarial compound blasticidin S that is transported through PSACs. Chaetocin treatment also altered progression of parasites through their replicative cycle, presumably by changing their ability to modify chromatin appropriately to enable DNA replication. These results indicate that targeting histone modifiers could represent a novel tool for reversing epigenetically acquired drug resistance in P. falciparum.
Biomonitoring of mycotoxin exposure using urinary biomarker approaches: a review

Abstract Due to the dangerous toxic effects of mycotoxin ingestion via food products, several studies were performed in order to estimate human exposure to dietary mycotoxins as important factor in risk assessment of these food contaminants. The exposure to mycotoxins can be evaluated by using some biomarkers especially in urine among several bio-specimens. The current review was undertaken to provide an overview regarding mycotoxin biomarkers in urine and presenting the recent data on human biomonitoring of aflatoxins, ochratoxin A, fumonisin, zearalenone, and deoxynivalenol using urinary biomarkers. Multi-mycotoxin biomarker approaches for assessment of combined exposure to multiple mycotoxins are also discussed.
Prediction of RNA Secondary Structure Using Quantum-inspired Genetic Algorithms

The prediction of RNA secondary structure using optimization algorithms is key to understand the real structure of an RNA. Evolutionary algorithms (EAs) are popular strategies for RNA secondary structure prediction. However, compared to most state-of-the-art software based on DPAs, the performances of EAs are a bit far from satisfactory. Therefore, a more powerful strategy is required to improve the performances of EAs when applied to the prediciton of RNA secondary structures. The idea of quantum computing is introduced here yielding a new strategy to find all possible legal paired-bases with the constraint of minimum free energy. The sate of a stem pool with size N is encoded as a population of QGA, which is represented by N quantum bits but not classical bits. The updating of populations is accomplished by so-called quantum crossover operations, quantum mutation operations and quantum rotation operations. The numerical results show that the performances of traditional EAs are significantly improved by using QGA with regard to not only prediction accuracy and sensitivity but also complexity. Moreover, for RNA sequences with middle-short length, QGA even improves the state-of-art software based on DPAs in terms of both prediction accuracy and sensitivity. This work sheds an interesting light on the applications of quantum computing on RNA structure prediction.
RNA-Bloom provides lightweight reference-free transcriptome assembly for single cells

We present RNA-Bloom, a de novo RNA-seq assembly algorithm that leverages the rich information content in single-cell transcriptome sequencing (scRNA-seq) data to reconstruct cell-specific isoforms. We benchmark RNA-Bloom’s performance against leading bulk RNA-seq assembly approaches, and illustrate its utility in detecting cell-specific gene fusion events using sequencing data from HiSeq-4000 and BGISEQ-500 platforms. We expect RNA-Bloom to boost the utility of scRNA-seq data, expanding what is informatically accessible now.
PARSE TREE POSITION MEASURING IN DISTRIBUTED GENETIC PROGRAMMING

Distributed genetic programming (GP) is a step forward in optimization of the GP algorithm, but it suffers from the difficulties of setting the proper distribution parameters. One of the most important parameter – classes, responsible for the migration among subpopulations, can be put under the control of flocking. The challenge in applying flocking to distributed GP lies in measuring the positions and distances between the program parse trees. This paper discusses the details of possible methods for measuring the tree position, paying the most attention to resulting distance values that are of the primary goal for a successful combination of distributed GP and flocking.
Theory and Applications of Deep Learning Networks

We analyze the capabilities of various deep learning algorithms which employ multiple-layer networks in order to model abstract structures from data sets and recognize patterns. This makes it possible to perform various classiﬁcation and normalization tasks. We investigate the use of a Deep Belief Network (DBN) and the Restricted Boltzmann Machine (RBM) as components for a deep learning network, as well as the alternative training algorithms that can be used to adjust the representation of the learned data. We introduce the underlying concepts and study the convergence properties. This allows us to derive another simpliﬁed version of a learning rule for the Contrastive Divergence algorithm used in pretraining the RBM. We also present a number of applications of Deep Learning by experiments on data sets. This includes handwriting detection and games.
Performance Improvement of Montgomery Multiplier Architecture Using Pre-Computation and Unfolding Technique

The Modern era of information and communication technology demands security. It is a prime important parameter along with other features. Data encryption and decryption algorithms such as RSA and ECC are popularly used to get the desired level of security. Modular multiplication is the integral part of these algorithms. Montgomery Multiplication is most efficient algorithm for modular multiplication. The modulo multiplication is a slow process for large bit size computations for key size more than 512. The critical path delay in architecture affects the iteration delay and overall computation time of encryption and decryption. The slow ripples of the carries in the Montgomery multiplication are often replaced by the carry save architectures of additions. The paper optimizes this traditional approach using a novel combination of an unfolding algorithm and a pre-computation technique. A new architectureMM4_2 multiplier, which holds input and output in carry save format and consuming the smallest critical path, is also modified using unfolding approach . The novel approach improves the overall computation time by 37.89% and 34.68% for ASIC and FPGA implementations as compared to traditional carry save approach. Further, unfolding of MM42_Multiplier in resent architecture gives improvement of 10.98 % in ASIC and 31.24% in FPGA as compare to original MM42 multiplier architecture.
Spatial statistical modelling of epigenomic variability

Each cell in our body carries the same genetic information encoded in the DNA, yet the human organism contains hundreds of cell types which di er substantially in physiology and functionality. This variability stems from the existence of regulatory mechanisms that control gene expression, and hence phenotype. The field of epigenetics studies how changes in biochemical factors, other than the DNA sequence itself, might a ect gene regulation. The advent of high throughput sequencing platforms has enabled the profiling of di erent epigenetic marks on a genome-wide scale; however, bespoke computational methods are required to interpret these high-dimensional data and investigate the coupling between the epigenome and transcriptome. This thesis contributes to the development of statistical models to capture spatial correlations of epigenetic marks, with the main focus being DNA methylation. To this end, we developed BPRMeth (Bayesian Probit Regression for Methylation), a probabilistic model for extracting higher order methylation features that precisely quantify the spatial variability of bulk DNA methylation patterns. Using such features, we constructed an accurate machine learning predictor of gene expression from DNA methylation and identified prototypical methylation profiles that explain most of the variability across promoter regions. The BPRMeth model, and its algorithmic implementation, were subsequently substantially extended both to accommodate di erent data types, and to improve the scalability of the algorithm. Bulk experiments have paved the way for mapping the epigenetic landscape, nonetheless, they fall short of explaining the epigenetic heterogeneity and quantifying its dynamics, which inherently occur at the single cell level. Single cell bisulfite sequencing protocols have been recently developed, however, due to intrinsic limitations of the technology they result in extremely sparse coverage of CpG sites, e ectively limiting the analysis repertoire to a semiquantitative level. To overcome these di culties we developed Melissa (MEthyLation Inference for Single cell Analysis), a Bayesian hierarchical model that leverages local correlations between neighbouring CpGs and similarity between individual cells to jointly impute missing methylation states, and cluster cells based on their genome-wide methylation profiles. A recent experimental innovation enables the parallel profiling of DNA methylation, transcription and chromatin accessibility (scNMT-seq), making it possible to link transcriptional and epigenetic heterogeneity at the single cell resolution. For the scNMT-seq study, we applied the extended BPRMeth model to quantify cell-to-cell chromatin accessibility heterogeneity
Effect of fire regimes on the demographic parameters of the perennial tussock grasses of a humid savanna

Perennial tussock grasses represent the principal fuel source for savanna fires; however, basic information about the impact of fire on their demography remains scarce. Do dominant perennial grass species differ in their demographic parameters? What is the overall impact of tussock circumference and fire regimes on grass demographic parameters? Do grass species differ in the sensitivity of their demographic parameters to fire regimes and tussock circumference?
StructureDistiller: Structural relevance scoring increases resilience of contact maps to false positive predictions

Protein folding and structure prediction are two sides of the same coin. We propose contact maps and the related techniques of constraint-based structure reconstruction as unifying aspect of both processes. The presented Structural Relevance (SR) score quantifies the contribution of individual contacts and residues to structural integrity. It is demonstrated that entries of a contact map are not equally relevant for structural integrity. Structure prediction methods should explicitly consider the most relevant contacts for optimal performance because they effectively double resilience toward false positively predicted contacts. Furthermore, knowledge of the most relevant contacts significantly increases reconstruction fidelity on sparse contact maps by 0.4 Å. Protein folding is commonly characterized with spatial and temporal resolution: some residues are Early Folding while others are Highly Stable with respect to unfolding events. Using the proposed SR score, we demonstrate that folding initiation and structure stabilization are distinct processes.
Ongoing surveillance protects tanoak whilst conserving biodiversity: applying optimal control theory to a spatial simulation model of sudden oak death

The sudden oak death epidemic in California is spreading uncontrollably. Large-scale eradication has been impossible for some time. However, small-scale disease management could still slow disease spread. Although empirical evidence suggests localised control could potentially be successful, mathematical models have said little about such management. By approximating a detailed, spatially-explicit simulation model of sudden oak death with a simpler, mathematically-tractable model, we demonstrate how optimal control theory can be used to unambiguously characterise effective time-dependent disease management strategies. We focus on protection of tanoak, a tree species which is culturally and ecologically important, but also highly susceptible to sudden oak death. We identify management strategies to protect tanoak in a newly-invaded forest stand, whilst also conserving biodiversity. We find that thinning of bay laurel is essential early in the epidemic. We apply model predictive control, a feedback strategy in which both the approximating model and the control are repeatedly updated as the epidemic progresses. Adapting optimal control strategies in this way is vital for effective disease management. This feedback strategy is robust to parameter uncertainty, limiting loss of tanoak in the worst-case scenarios. However, the methodology requires ongoing surveillance to re-optimise the approximating model. This introduces an optimal level of surveillance to balance the high costs of intensive surveys against improved management resulting from better estimates of disease progress. Our study shows how detailed simulation models can be coupled with optimal control theory and model predictive control to find effective control strategies for sudden oak death. We demonstrate that control strategies for sudden oak death must depend on local management goals, and that success relies on adaptive strategies that are updated via ongoing disease surveillance. The broad framework allowing the use of optimal control theory on complex simulation models is applicable to a wide range of systems.
Paradoxical gaps in resilient environmental governance

Over the last few decades, resilience and its related practices have been at the core of responding challenges in the Global South and North. We should, however, be conscious of its gaps for many reasons. First, environmental plans not attuned to local traditions can create cultural conflicts. Second, the politicised nature of international agreements poses unintended consequences as societies find it hard to engage in such agreements. Third, uncertainties about changes in socio-ecological systems reduce people’s adaptive capacity. Without an awareness of these inconsistencies, policymakers risk impeding societies’ adaptation to environmental change. By doing a systematic review of articles from academic and policy publications, this paper explores repercussions for environmental governance, illuminating key concerns in protected areas, climate change policy, and hydropower systems.
Direct detection of molecular intermediates from first-passage times

Model-free method reveals details of underlying energy landscapes from dynamics in experimental systems across a range of scales. All natural phenomena are governed by energy landscapes. However, the direct measurement of this fundamental quantity remains challenging, particularly in complex systems involving intermediate states. Here, we uncover key details of the energy landscapes that underpin a range of experimental systems through quantitative analysis of first-passage time distributions. By combined study of colloidal dynamics in confinement, transport through a biological pore, and the folding kinetics of DNA hairpins, we demonstrate conclusively how a short-time, power-law regime of the first-passage time distribution reflects the number of intermediate states associated with each of these processes, despite their differing length scales, time scales, and interactions. We thereby establish a powerful method for investigating the underlying mechanisms of complex molecular processes.
Incorporating uncertainty is essential to macroecological inferences: Grass, grit, and the evolution of kangaroos

Studying organismal ecology and evolution on deep timescales provides us opportunities to identify the processes driving patterns in diversity and forms. Macroecological and macroevolutionary studies of trait evolution however, often fail to account for sources of artifactual variation in the data—be it phylogenetic, temporal, or other. In some instances, this may not affect our evolutionary understanding, and accounting for sources of uncertainty may only subdue confidence in our inferences. In more dramatic cases, narrow views of trait uncertainty may result in conclusions that are misleading. Because macroevolutionary analyses are built atop a number of preconceived hypotheses regarding the relationships between taxa, origination and divergence times, intraspecific variation, and environmental variables, it is important to incorporate and present this uncertainty. Here I use a dataset for Australian kangaroos to demonstrate the importance of incorporating uncertainty when testing patterns of diversification. After accounting for fossil age uncertainty, I provide evidence that a proposed Pliocene origin of Macropus kangaroos is at odds with combined evidence molecular and morphological dating methods. Depending on the estimated crown age of kangaroos, the evolution of hypsodonty is as likely caused by the continental expansion of C4 grasses as it is by increasing windborne dust levels or paleotemperature fluctuations. These results suggest that previous interpretations of the radiation of modern kangaroos are not as bulletproof as we believe, and that multiple factors have likely influenced their remarkable diversification across the Australian continent. More broadly, this demonstrates the importance of incorporating uncertainty in comparative ecological and evolutionary studies, and the value in testing the assumptions inherent in our data and the methods we employ.