Miracle-dz/UniFAR-FTUs huggingface dataset:
Predictive evolution of metabolic phenotypes using model-designed selection niches

Traits lacking fitness benefit cannot be directly selected for under Darwinian evolution. Thus, features such as metabolite secretion are currently inaccessible to adaptive laboratory evolution. Here, we utilize environment-dependency of trait correlations to enable Darwinian selection of fitness-neutral or costly traits. We use metabolic models to design selection niches and to identify surrogate traits that are genetically correlated with cell fitness in the selection niche but coupled to the desired trait in the target niche. Adaptive evolution in the selection niche and subsequent return to the target niche is thereby predicted to enhance the desired trait. We experimentally validate the theory by evolving Saccharomyces cerevisiae for increased secretion of aroma compounds in wine fermentation. Genomic, transcriptomic, and proteomic changes in the evolved strains confirmed the predicted flux re-routing to aroma biosynthesis. The use of model-designed selection niches facilitates the predictive evolution of fitness-costly traits for ecological and biotechnological applications.
Pathogen clonal expansion underlies multiorgan dissemination and organ-specific outcomes during systemic infection

The dissemination of pathogens through blood and their establishment within organs lead to severe clinical outcomes. However, the within-host dynamics that underly pathogen spread to and clearance from systemic organs remain largely uncharacterized. Here, we investigate the population dynamics of extraintestinal pathogenic E. coli, a common cause of bacteremia, during systemic infection. We show that while bacteria are largely cleared by most organs, organ-specific clearance failures are pervasive and result from dramatic expansions of clones representing less than 0.0001% of the inoculum. Clonal expansion underlies the variability in bacterial burden between animals, and stochastic dissemination of clones profoundly alters the pathogen population structure within organs. Despite variable pathogen expansion events, host bottlenecks are consistent yet highly sensitive to infection variables, including inoculum size and macrophage depletion. Finally, we identify organ-specific bacterial genetic factors that distinguish between establishment of within-organ pathogen populations and subsequent survival or expansion.
Schema representations in distinct brain networks support narrative memory during encoding and retrieval

Schematic prior knowledge can scaffold the construction of event memories during perception and also provide structured cues to guide memory search during retrieval. We measured the activation of story-specific and schematic representations using fMRI while participants were presented with 16 stories and then recalled each of the narratives, and related these activations to memory for specific story details. We predicted that schema representations in mPFC would be correlated with successful recall of story details. In keeping with this prediction, an anterior mPFC region showed a significant correlation between activation of schema representations at encoding and subsequent behavioral recall performance; however, this mPFC region was not implicated in schema representation during retrieval. More generally, our analyses revealed largely distinct brain networks at encoding and retrieval in which schema activation was related to successful recall. These results provide new insight into when and where event knowledge can support narrative memory.
Soil chemistry determines whether defensive plant secondary metabolites promote or suppress herbivore growth

Significance This study demonstrates that the protective effects of multifunctional maize secondary metabolites against a major pest are dependent on soil chemical composition. By functioning as both digestibility reducers and siderophores, benzoxazinoids link soil chemistry to plant–environment interactions. Given that many plant secondary metabolites have multiple functions in roots and leaves, such links are likely widespread and may govern community composition and pest dynamics across different (agro)ecosystems. The presented findings also illustrate the limits and context dependency of using multifunctional plant secondary metabolites to combat major herbivore pests. The latter is particularly important in the context of the threat that the fall armyworm poses for global maize production. Plant secondary (or specialized) metabolites mediate important interactions in both the rhizosphere and the phyllosphere. If and how such compartmentalized functions interact to determine plant–environment interactions is not well understood. Here, we investigated how the dual role of maize benzoxazinoids as leaf defenses and root siderophores shapes the interaction between maize and a major global insect pest, the fall armyworm. We find that benzoxazinoids suppress fall armyworm growth when plants are grown in soils with very low available iron but enhance growth in soils with higher available iron. Manipulation experiments confirm that benzoxazinoids suppress herbivore growth under iron-deficient conditions and in the presence of chelated iron but enhance herbivore growth in the presence of free iron in the growth medium. This reversal of the protective effect of benzoxazinoids is not associated with major changes in plant primary metabolism. Plant defense activation is modulated by the interplay between soil iron and benzoxazinoids but does not explain fall armyworm performance. Instead, increased iron supply to the fall armyworm by benzoxazinoids in the presence of free iron enhances larval performance. This work identifies soil chemistry as a decisive factor for the impact of plant secondary metabolites on herbivore growth. It also demonstrates how the multifunctionality of plant secondary metabolites drives interactions between abiotic and biotic factors, with potential consequences for plant resistance in variable environments.
A Trifector of New Insights into Ovine Footrot for Infection Drivers, Immune Response and Host Pathogen Interactions

Footrot is a polymicrobial infectious disease in sheep causing severe lameness, leading to one of the industry’s biggest welfare problems. The complex aetiology of footrot makes in-situ or in-vitro investigations difficult. Computational methods offer a solution to understanding the bacteria involved, how they may interact with the host and ultimately providing a way to identify targets for future hypotheses driven investigative work. Here we present the first combined global analysis of the bacterial community transcripts together with the host immune response in healthy and diseased ovine feet during a natural polymicrobial infection state using metatranscriptomics. The intra tissue and surface bacterial populations and the most abundant bacterial transcriptome were analysed, demonstrating footrot affected skin has a reduced diversity and increased abundances of, not only the causative bacteria Dichelobacter nodosus, but other species such as Mycoplasma fermentans and Porphyromonas asaccharolytica. Host transcriptomics reveals a suppression of biological processes relating to skin barrier function, vascular functions, and immunosurveillance in unhealthy interdigital skin, supported by histological findings that type I collagen (associated with scar tissue formation) is significantly increased in footrot affected interdigital skin comparted to outwardly healthy skin. Finally, we provide some interesting indications of host and pathogen interactions associated with virulence genes and the host spliceosome which could lead to the identification of future therapeutic targets. Impact Statement Lameness in sheep is a global welfare and economic concern and footrot is the leading cause of lameness, affecting up to 70% of flocks in the U.K. Current methods for control of this disease are labour intensive and account for approximately 65% of antibiotic use in sheep farming, whilst preventative vaccines suffer from poor efficacy due to antigen competition. Our limited understanding of cofounders, such as strain variation and polymicrobial nature of infection mean new efficacious, affordable and scalable control measures are not receiving much attention. Here we examine the surface and intracellular bacterial populations and propose potential interactions with the host. Identification of these key bacterial species involved in the initiation and progression of disease and the host immune mechanisms could help form the basis of new therapies.
Hierarchical trait-based model reveals positive and negative effects of land abandonment on butterfly communities across climatic regions in Japan

Land abandonment may decrease biodiversity but also provides an opportunity for rewilding. It is therefore necessary to identify areas that may benefit from traditional land management practices and those that may benefit from a lack of human intervention. In this study, we conducted comparative field surveys of butterfly occurrence in abandoned and inhabited settlements in 18 regions of diverse climatic zones in Japan to test the hypotheses that species-specific responses to land abandonment correlate with their climatic niches and habitat preferences. Hierarchical models that unified species occurrence and their habitat characteristics revealed that negative responses to land abandonment were associated with species having cold climatic niches utilizing open habitats, suggesting that traditional land use by human had long mitigated climate warming since the last glacier maximum through creating non-forest open habitats. Maps representing species gains and losses due to land abandonment, which were created from the model estimates, showed similar geographic patterns, but some parts of areas exhibited high species losses relative to gains. A hierarchical trait-based approach presented here revealed to be useful for scaling up local-scale effect of land abandonment to a macro-scale assessment, which is crucial to developing spatial conservation strategies in the era of depopulation.
Combined study of the effect of deposition temperature and post-deposition annealing on the photoluminescence of silicon quantum dots embedded in chlorinated silicon nitride thin films

Chlorinated-silicon nitride (SiNx:Cl) thin films with embedded silicon quantum dots (Si-QDs) were grown by remote plasma enhanced chemical vapor deposition using SiH2Cl2, H2, NH3 and Ar at different deposition temperatures (DT) in the range of 50 to 350 °C and using steps of 50 °C. After that, each sample was subjected to annealing treatments at temperatures (AT) of 400, 700 and 1000 °C. The evolution of the optical properties, composition and structure as a function of DT and AT were investigated by Null Ellipsometry, photoluminescence (PL) measurements, UV-Vis spectroscopy, X-ray Photoelectron Spectroscopy (XPS), Fourier Transform Infrared Spectroscopy (FTIR), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM) with (EDS) and High Resolution Transmission Electron Microscopy (HRTEM). A red-shift in the PL peak and an increment in the PL integrated intensity of the films was found on increasing DT from 150 to 350 °C; such changes can be explained by modifications of the matrix chemical composition and by the quantum confinement effect due to an increase in the size and density of Si-QDs respectively. A chemical analysis revealed that films deposited below 150 °C suffer a post-deposition oxidation process leading to the formation of silicon oxide; this process is accelerated by the temperature of the annealing treatment. Meanwhile films deposited between 150 and 350 °C are mainly composed of silicon nitride that diminishes the mentioned oxidation due to the thermal treatments and promotes the splitting of Si-QDs into two populations with different average diameter. The annealed films deposited below 150 °C show a clearly decrement in the PL intensity, meanwhile the maximum emission PL peak from annealed films grown from 150 to 350 °C shifts to high emission energies. This post thermal annealing blue-shift can be mainly related to the nucleation of new small Si-QDs. We concluded that temperature is one of the most important mainstays to achieve good luminescence from Si-QDs.
Reactive oxygen species mediate thylakoid membrane remodeling and triacylglycerol synthesis under nitrogen starvation in the alga Chlorella sorokiniana

Many microbes accumulate energy storage molecules such as triglycerides and starch during nutrient limitation. In eukaryotic green algae grown under nitrogen limiting conditions, triglyceride accumulation is coupled with chlorosis and growth arrest. In this study we show that accumulation of reactive oxygen species (ROS) under nitrogen limitation in the microalga Chlorella sorokiniana is involved in thylakoid membrane remodeling, leading to chlorosis. We show that ROS accumulation under nitrogen limitation is an active process involving downregulation of expression of ROS-quenching enzymes, such as superoxide dismutases, catalase, peroxiredoxin, and glutathione peroxidase-like, and upregulation of enzymes involved in generating ROS, such as NADPH oxidase, xanthine oxidase and amine oxidases. Expression of enzymes involved in ascorbate and glutathione metabolism are also affected under these conditions. Quenching ROS under nitrogen limitation reduces TAG accumulation, adding additional evidence for the role of ROS signaling in the process.
Membrane phase separation drives organization at B cell receptor clusters

Heterogeneity in intact cell plasma membranes has been explained by analogy to coexisting liquid-ordered and liquid-disordered phases, although models based on this idea fall short of describing the rich structure within cell membranes. Here, a new framework of lipid-driven plasma membrane heterogeneity is presented, drawing on quantitative measurements of protein partitioning and dynamics within B cell receptor clusters in live B lymphocyte plasma membranes, compared to coexisting phases in isolated plasma membranes. We propose that membrane domains in cells integrate the thermodynamic state of the membrane and the magnitude of the applied stimulus to give rise to a tunable response. This framework is supported through functional observations of B cell receptor phosphorylation in perturbed systems.
An Improved Artificial Neural Network Model for Effective Diabetes Prediction

Data analytics, machine intelligence, and other cognitive algorithms have been employed in predicting various types of diseases in health care. The revolution of artificial neural networks (ANNs) in the medical discipline emerged for data-driven applications, particularly in the healthcare domain. It ranges from diagnosis of various diseases, medical image processing, decision support system (DSS), and disease prediction. The intention of conducting the research is to ascertain the impact of parameters on diabetes data to predict whether a particular patient has a disease or not. This paper develops an improved ANN model trained using an artificial backpropagation scaled conjugate gradient neural network (ABP-SCGNN) algorithm to predict diabetes effectively. For validating the performance of the proposed model, we conduct a large set of experiments on a Pima Indian Diabetes (PID) dataset using accuracy and mean squared error (MSE) as evaluation metrics. We use different number of neurons in the hidden layer, ranging from 5 to 50, to train the ANN models. The experimental results show that the ABP-SCGNN model, containing 20 neurons, attains 93% accuracy on the validation set, which is higher than using the other ANNs models. This result confirms the model’s effectiveness and efficiency in predicting diabetes disease from the required data attributes.
Arabic Fake News Detection: Comparative Study of Neural Networks and Transformer-Based Approaches

Fake news detection (FND) involves predicting the likelihood that a particular news article (news report, editorial, expose, etc.) is intentionally deceptive. Arabic FND started to receive more attention in the last decade, and many detection approaches demonstrated some ability to detect fake news on multiple datasets. However, most existing approaches do not consider recent advances in natural language processing, i.e., the use of neural networks and transformers. This paper presents a comprehensive comparative study of neural network and transformer-based language models used for Arabic FND. We examine the use of neural networks and transformer-based language models for Arabic FND and show their performance compared to each other. We also conduct an extensive analysis of the possible reasons for the difference in performance results obtained by different approaches. The results demonstrate that transformer-based models outperform the neural network-based solutions, which led to an increase in the F1 score from 0.83 (best neural network-based model, GRU) to 0.95 (best transformer-based model, QARiB), and it boosted the accuracy by 16% compared to the best in neural network-based solutions. Finally, we highlight the main gaps in Arabic FND research and suggest future research directions.
Social Media Mining to Study Social User Group by Visualizing Tweet Clusters using Word2Vec, PCA and K-Means

In social media mining, unsupervised machine learning has not been studied extensively. Based on a specific use case in political polarization detection, we contribute an experimental method to find clusters in social media data and to explain the semantics of cluster membership using frequent word mentions. 100 dimensional word2vec word embedding vectors are generated from the raw text data. Based on this, three clustering approaches were tested: K-means, K-Means with Principal Components Analysis (PCA), and Deep Embedded Clustering (DEC). For K-Means, the optimal number of clusters was estimated visually. The performance of the clustering approaches was compared using the Silhouette score. Using PCA to reduce the dimensionality from 100 to only 2 most significant principal components had a significant performance impact, and combined with K-means, provided the best results. To explain the clusters visually, word counts of the most frequent words were plotted for three clusters. It became evident that the most frequent words per cluster explained the semantics of cluster membership in a consistent way. We conclude the following: our method demonstrates it is possible to find and explain meaningful clusters of users in social media data. Dimensionality reduction of text based on Word2Vec and PCA led to semantically coherent, clearly distinct clusters. Based on our understanding of the underlying use case, the clusters generated by the proposed method clearly reflect social phenomena that can be verified by a qualitative interpretation of the cluster visualizations. Thus, the proposed method can provide a basis for studying social behavior using social media data.
iSTART StairStepper - Using Comprehension Strategy Training to Game the Test

Literacy skills are critical for future success, yet over 60% of high school seniors lack proficient reading skills according to standardized tests. The focus on high stakes, standardized test performance may lead educators to “teach-to-the-test” rather than supporting transferable comprehension strategies that students need. StairStepper can fill this gap by blending necessary test prep and reading comprehension strategy practice in a fun, game-based environment. StairStepper is an adaptive literacy skill training game within Interactive Strategy Training for Active Reading and Thinking (iSTART) intelligent tutoring system. StairStepper is unique in that it models text passages and multiple-choice questions of high-stakes assessments, iteratively supporting skill acquisition through self-explanation prompts and scaffolded, adaptive feedback based on performance and self-explanations. This paper describes an experimental study employing a delayed-treatment control design to evaluate users’ perceptions of the StairStepper game and its influence on reading comprehension scores. Results indicate that participants enjoyed the visual aspects of the game environment, wanted to perform well, and considered the game feedback helpful. Reading comprehension scores of students in the treatment condition did not increase. However, the comprehension scores of the control group decreased. Collectively, these results indicate that the StairStepper game may fill the intended gap in instruction by providing enjoyable practice of essential reading comprehension skills and test preparation, potentially increasing students’ practice persistence while decreasing teacher workload.
Kinetics of Polymeric Substrate Removal by Activated Sludge: Hydrolysis of Polymers is the Rate-Determining Step

A large portion of organic matter in primary effluent is polymers such as proteins and polysaccharides, and heterotrophic bacteria can directly take up only monomers such as amino acids and glucose that are produced from polymers by hydrolysis. Therefore it is assumed that the hydrolysis of polymers to monomers is the rate-determining step in activated sludge process. Activated sludges were acclimated to dextrin or peptone, and polymers (dextrin or peptone) and monomers (glucose or a mixture of free amino acids) were used as substrates for kinetics tests. Monomers were removed linearly, and the removal of polymers followed pseudo first order kinetics on the other hand. The removal rate of monomers was higher than that of polymers. The only one difference between polymer and monomer is whether glycosidic or peptide bond exists in molecule or not. It was, therefore, verified that the hydrolysis of polymer to monomer was the rate-determining step in activated sludge process. The removal of polymers followed apparently first order kinetics at higher F/M ratios, but followed nth (n>l) order kinetics at lower F/M ratios.
A Building-Material Supply Chain Sustainable Operations under Fairness Concerns and Reference Price Benefits

This paper incorporates fairness concerns and consumer reference price effects into a two-echelon building-material closed-loop supply chain consisting of a manufacturer and a retailer. By establishing four differential game models, we investigate the sustainable operations and cooperation of this supply chain. The four game models are a Nash noncooperative game, Stackelberg game with cost sharing, Stackelberg game with fairness concerns and cost sharing, and centralized decision model. By using dynamic models and optimal control theory, we obtain the two members’ optimal equilibrium strategies in the supply chain. Analytical results show that the consumer reference price effect has a positive impact on the manufacturer’s effort level, retailer’s publicity level, and product brand goodwill, which can improve the supply chain performance. The retailer’s partial commitment to cost sharing can enhance the production enthusiasm of the manufacturer, improve the brand reputation of the product, and enhance the two members’ individual profitability. The distributional fairness concerns of the manufacturer not only prevent the manufacturer and retailer from achieving Pareto improvement but also lead to the decline of the manufacturer’s effort level and profitability. The research conclusions of this paper can provide some insights into the cooperation and sustainable development of the supply chain.