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A machine learning model to predict small molecules with antischistosomal activity
Get PDFCaused by parasitic worms that live in fresh water, schistosomasis is a disease that affects 251 million people world wide. This study proposes a computer-aided drug design system to predict small molecules that can inhibit schistosomal activity, and thus could be used as a treatment for this disease. We introduce a regression model that estimates the IC50 value for each molecule and a classification model that predicts whether a certain molecule is active or not against the Schistosoma mansoni parasite. We acquire a set of active and nonactive molecules from the ChemBL database and generate descriptors of those molecules using the rdkit library. The resulting features are preprocessed and fed into machine learning models to perform regression and classification tasks. In both cases, the artificial neural network scored the highest accuracy while tested on a set of 754 molecules using cross-validation techniques. Finally, we train a genetic algorithm using the proposed regression model within its fitness function and retrieve 3 molecules that have the best fitness values and have not been screened before
Enhancing the prediction accuracy of groundnut yield by integrating significant markers and modeling genotype × environment interaction
Get PDFMulti-environment trials are routinely conducted in plant breeding to capture the genotype-by-environment interaction (G × E) effects. Significant G × E could alter the response pattern of genotypes (the change in rankings of genotypes), subsequently complicating the selection process. Four genomic prediction (GP) models were assessed in three groundnut yield-related traits: pod yield (PY), seed weight (SW), and 100 seed weight (SW100), across four environments. The models, M1 (environment + line), M2 (environment + line + genomic), M3 (environment + line + genomic + genomic × environment interaction), and M4 (environment + line + genomic + genomic × environment interaction + significant markers), were tested using four cross-validation (CV) schemes (CV2, CV1, CV0, and CV00), each simulating different practical breeding scenarios. The results revealed that models incorporating marker data (M2, M3, and M4) consistently improved predictive ability in comparison to the phenotypic model (M1). Incorporating G × E (M3 and M4) further improved predictive ability and reduced residual and environmental variances. The inclusion of significant markers and G × E was more advantageous in CV1 and CV00 scenarios, demonstrating that this strategy is especially useful when phenotypic data for the target genotypes is limited or unavailable. Across the CV schemes, predictive ability was higher in CV2, suggesting that including additional information on the performance of genotypes in known environments can increase the accuracy of selecting superior genotypes in breeding programs. Integrating significant markers and modeling G × E in GP models could be an effective approach in groundnut breeding programs to accelerate genetic gains
Commonly observed sex differences in direct aggression are absent or reversed in sibling contexts
Get PDFDecades of research support the generalization that human males tend to be more aggressive than females. However, most of that research has examined aggression between unrelated individuals. Data drawn from 24 societies around the globe (n = 4,013) indicate that this generalization does not hold in the context of sibling relationships. In retrospective self-reports, females report being at least as aggressive as males toward their siblings, often more so. This holds for direct as well as indirect aggression, and for aggression between adult siblings as well as aggression that occurred during childhood. Consistent with prior research on sex differences, males reported engaging in more direct aggression toward nonkin than did females in the majority of societies. The results suggest that the dynamics of aggression within the family are different from those outside of it, and ultimately that understanding the role of sex in aggressive tendencies depends on context and target
The distinct roles of genome, methylation, transcription, and translation on protein expression in Arabidopsis thaliana resolve the Central Dogma’s information flow
Get PDFBackground: We investigate the flow of genetic information from DNA to RNA to protein as described by the Central Dogma in molecular biology, to determine the impact of intermediate genomic levels on plant protein expression. Results: We perform genomic profiling of rosette leaves in two Arabidopsis accessions, Col-0 and Can-0, and assemble their genomes using long reads and chromatin interaction data. We measure gene and protein expression in biological replicates grown in a controlled environment, also measuring CpG methylation, ribosome-associated transcript levels, and tRNA abundance. Each omic level is highly reproducible between biological replicates and between accessions despite their ~1% sequence divergence; the single best predictor of any level in one accession is the corresponding level in the other. Within each accession, gene codon frequencies accurately model both mRNA and protein expression. The effects of a codon on mRNA and protein expression are highly correlated but independent of genome-wide codon frequencies or tRNA levels which instead match genome-wide amino acid frequencies. Ribosome-associated transcripts closely track mRNA levels. Conclusions: DNA codon frequencies and mRNA expression levels are the main predictors of protein abundance. In the absence of environmental perturbation neither gene-body methylation, tRNA abundance nor ribosome-associated transcript levels add appreciable information. The impact of constitutive gene-body methylation is mostly explained by gene codon composition. tRNA abundance tracks overall amino acid demand. However, genetic differences between accessions associate with differential gene-body methylation by inflating differential expression variation. Our data show that the dogma holds only if both sequence and abundance information in mRNA are considered
Use of P450 Enzymes for Late-Stage Functionalization in Drug Discovery
Get PDFHerein, we demonstrate the use of a commercially available enzymatic kit to achieve late-stage hydroxylation of biologically relevant compounds by using the PolyCYPs screening kit. A selection of promising biotransformations were scaled up, products isolated, and structures elucidated. Isolated compounds were screened against a range of pathogens, namely, Schistosoma mansoni, Leishmania donovani, Trypanosoma cruzi, and Trypanosoma brucei to obtain biological data. This approach has allowed data generation more efficiently than the chemical synthesis of the same molecules. Importantly, it has been demonstrated that production of hits of interest can also be scaled up to enable further study. We also demonstrate the biosynthetic synthesis of a lead compound in fewer steps than using standard synthetic chemistry, offering faster access to compounds for screening or further transformation. This approach has the potential to save time and resources in a drug discovery program, by reducing the necessity to synthesize late-stage intermediates and develop new chemistry.</p
A Comprehensive Review of DDoS Detection and Mitigation in SDN Environments:Machine Learning, Deep Learning, and Federated Learning Perspectives
Get PDFSoftware-defined networking (SDN) has reformed the traditional approach to managing and configuring networks by isolating the data plane from control plane. This isolation helps enable centralized control over network resources, enhanced programmability, and the ability to dynamically apply and enforce security and traffic policies. The shift in architecture offers numerous advantages such as increased flexibility, scalability, and improved network management but also introduces new and notable security challenges such as Distributed Denial-of-Service (DDoS) attacks. Such attacks focus on affecting the target with malicious traffic and even short-lived DDoS incidents can drastically impact the entire network’s stability, performance and availability. This comprehensive review paper provides a detailed investigation of SDN principles, the nature of DDoS threats in such environments and the strategies used to detect/mitigate these attacks. It provides novelty by offering an in-depth categorization of state-of-the-art detection techniques, utilizing machine learning, deep learning, and federated learning in domain-specific and general-purpose SDN scenarios. Each method is analyzed for its effectiveness. The paper further evaluates the strengths and weaknesses of these techniques, highlighting their applicability in different SDN contexts. In addition, the paper outlines the key performance metrics used in evaluating these detection mechanisms. Moreover, the novelty of the study is classifying the datasets commonly used for training and validating DDoS detection models into two major categories: legacy-compatible datasets that are adapted from traditional network environments, and SDN-contextual datasets that are specifically generated to reflect the characteristics of modern SDN systems. Finally, the paper suggests a few directions for future research. These include enhancing the robustness of detection models, integrating privacy-preserving techniques in collaborative learning, and developing more comprehensive and realistic SDN-specific datasets to improve the strength of SDN infrastructures against DDoS threats.</p
Self-assembled clusters of mutually repelling particles in confinement
Get PDFMutually repelling particles spontaneously form ordered clusters when forced into confinement. The clusters may adopt similar spatial arrangements even if the underlying particle interactions are contrastingly different. Here, we demonstrate with both simulations and experiments that it is possible to induce particles of very different types to self-assemble into the same ordered geometric structure by simply regulating the ratio between the repulsive and confining forces. This is the case for both long- and short-ranged potentials. This property is initially explored in systems with two-dimensional circular symmetry and subsequently demonstrated to be valid throughout the transition to one-dimensional structures through continuous elliptical deformations of the confining field. We argue that this feature can be utilized to manipulate the spatial structure of confined particles, thereby paving the way for the design of clusters with specific functionalities.</p
Integrated valuation of instrumental and relational values of marine protected areas in the United Arab Emirates
Get PDFWe integrate contingent valuation and self-reported wellbeing indicators to inclusively assess instrumental and relational values around two potential marine protected areas (MPAS) in lagoons in Abu Dhabi (AD) and Umm al Quwain (UAQ), United Arab Emirates (UAE). In total, 510 residents and tourists were surveyed. There was near consensus that designating the lagoons as MPAs was important. All wellbeing indicators reflecting relational values scored strongly, and significantly higher for lower incomes and in the less urbanised UAQ lagoon, particularly for connection to nature. Individual willingness to pay (WTP) for establishing MPAs varied by user/non-user, size, level of protection. Central estimates for aggregated annual WTP for highly protected MPAs ranged from 13.63 m (AD, 230 km2). Results suggest the substitutability of relational values varied by income, and that there were trade-offs between aggregate monetary values increasing with number of users, and local and lower income users’ wellbeing. Inclusive management must carefully balance the diverse values of marine ecosystems and wildlife, the instrumental benefits of coastal tourism, the relational connections between local people and nature, and equity issues around the lower availability of substitutes and the stronger relational values for lower-income users of potential protected areas.</p
Pregnancy Scams as Actionable and Unactionable Fraud in Ghana
Get PDFPregnancy scams or fraudulent representations of pregnancy – a situation where a woman who knows that she is not pregnant, or has no reason to suspect that she is, deceives her spouse or sexual partner into believing that she is, usually for her own self-interest – have become a common phenomenon in Ghana, and the media is replete with such episodes. However, even though these media publications are useful in bringing this practice to the attention of the public, an analysis of the legal ramifications of, and the criminal justice response to, the phenomenon is virtually non-existent in the academic literature. Drawing on pertinent judicial decisions, statutes and academic literature, the present study offers an exposition of the legal implications of the pregnancy scam phenomenon in contemporary Ghana. It explores the extent to which this type of fraud is (un)actionable, highlighting important legal principles and controversies.</p
