This study investigated the genetic bases of specific reading disability (SRD) by systematically cataloging candidate genes reported as associated with SRD and reading-related processes over the last 4 decades and analyzing their evolutionary conservation, developmental expression patterns, and functional networks to address significant knowledge gaps in understanding the genetic architecture of reading (dis)ability. Through a comprehensive literature review, we identified 175 putative SRD (and reading-related processes) candidate genes (hereafter, SRD genes). Using bioinformatic approaches, we analyzed their evolutionary conservation across species, examined their expression patterns in developmental and single-cell transcriptome data sets from the Allen Brain Atlas, and performed functional pathway analyses to identify biological processes associated with these genes. SRD genes showed remarkable evolutionary conservation, with enrichment in ancient taxonomic groups. Developmental transcriptome analysis revealed two distinct gene clusters with expression differentiation around 24 postconception weeks: early genes associated with brain morphology development and later genes involved in synaptic signaling. Single-cell analysis identified cell-type-specific expression patterns and protein-protein interaction networks with hub genes potentially coordinating reading-related neural pathways. Our findings challenge the notion of the existence of reading-specific genes, suggesting instead that SRD reflects the disruption of ancient evolutionary neural mechanisms operating within human-specific brain architecture. The identification of developmental expression transitions and functional networks provides insight into how genetic variation might impact reading development and offers potential targets for future clinical approaches to the identification and remediation of reading difficulties. https://doi.org/10.23641/asha.30290446.

Cellular senescence is a heterogeneous and dynamic state characterised by stable proliferation arrest, macromolecular damage and metabolic remodelling. Although markers such as SA-β-galactosidase staining, yH2AX foci and p53 activation are widely used as de facto standards, they are imperfect and differ in terms of sensitivity, specificity and dependence on context. We present a multifactorial imaging platform integrating scanning electron, flow cytometry and high-resolution confocal microscopy. This allows us to identify senescence phenotypes in three in vitro models: replicative ageing via serial passaging; dose-graded genotoxic stress under serum deprivation; and primary fibroblasts from young and elderly donors. We present a multimodal imaging framework to characterise senescence-associated phenotypes by integrating LysoTracker and MitoTracker microscopy and SA-β-gal/FACS, p16INK4a immunostaining provides independent confirmation of proliferative arrest. Combined nutrient deprivation and genotoxic challenge elicited the most pronounced and concordant organelle alterations relative to single stressors, aligning with age-donor differences. Our approach integrates structural and functional readouts across modalities, reducing the impact of phenotypic heterogeneity and providing reproducible multiparametric endpoints. Although the framework focuses on a robustly validated panel of phenotypes, it is extensible by nature and sensitive to distributional shifts. This allows both drug-specific redistribution of established markers and the emergence of atypical or transient phenotypes to be detected. This flexibility renders the platform suitable for comparative studies and the screening of senolytics and geroprotectors, as well as for refining the evolving landscape of senescence-associated states.

Crypto-primitives of electronic digital signature (EDS) are quite important components in any information security subsystem of national blockchain ecosystems and platforms. Most of the known EDS schemes are based on the assumption of the computational complexity of large number factorization problems (for example, RSA) or discrete logarithm (such as DSA and ECDSA). However, the obtained scientific and technical results in the field of post-quantum cryptology and quantum computing call into question the sufficiency of the known crypto-primitives of EDS. For this reason, the relevance of new, post-quantum EDS schemes based on such sections of mathematics that are capable of ensuring the quantum stability of the mentioned systems to intruder attacks using a quantum computer is growing. This article presents the results of a critical analysis of the known EDS schemes based on lattice theory. The emphasis is on the practical aspects of using these crypto primitives (efficiency, quantum stability, key size, etc.)

The integration of renewable energy sources (RES) into energy systems is becoming increasingly widespread around the world, driven by various factors, the most relevant of which is the high environmental friendliness of these types of energy resources and the possibility of creating stable generation systems that are independent of the economic and geopolitical situation. The large-scale involvement of green energy leads to the creation of distributed energy networks that combine several different methods of generation, each with its own characteristics. As a result, the issues of data collection and processing necessary for optimizing the operation of such energy systems are becoming increasingly relevant. The first stage of renewable energy integration involves building models to assess theoretical potential, allowing the feasibility of using a particular type of resource in specific geographical conditions to be determined. The second stage of assessment involves determining the technical potential, which allows the actual energy values that can be obtained by the consumer to be determined. The paper discusses a method for assessing the technical potential of solar energy using the example of a private consumer’s energy system. For this purpose, a generator circuit with load models was implemented in the SimInTech dynamic simulation environment, accepting various sets of parameters as input, which were obtained using an intelligent information search procedure and intelligent forecasting methods. This approach makes it possible to forecast the amount of incoming solar insolation in the short term, whose values are then fed into the simulation model, allowing the forecast values of the technical potential of solar energy for the energy system configuration under consideration to be determined. The implementation of such a hybrid assessment system allows not only the technical potential of RES to be determined based on historical datasets but also provides the opportunity to obtain forecast values for energy production volumes. This allows for flexible configuration of the parameters of the elements used, which makes it possible to scale the solution to the specific configuration of the energy system in use. The proposed solution can be used as one of the elements of distributed energy systems with RES, where the concept of demand distribution and management plays an important role. Its implementation is impossible without predictive models.

Gene-editing tools enable precise, targeted genome modifications, providing new approach for the rapid and sustainable improvement of tea plant (Camellia sinensis (L.) Kuntze). Developing such an approach is especially important due to the perennial nature and complex genetics of the tea plant, which make traditional breeding slow and inefficient. To validate a gene editing protocol in the elite local tea cultivar Kolkhida three candidate genes were selected. Two guide RNAs (gRNAs) were designed for each gene, and corresponding constructs for targeted genome modification in tea were generated. Successful modifications of the target sequences in cv. Kolkhida tea protoplasts were achieved for all three target genes. The high mutagenic efficiency of the selected gRNAs was observed for two out of three genes, including induction of precise deletions between target motifs. gRNAs were delivered in protoplasts via co-transfection technique, and combined gRNA activity was observed when transfection efficiency exceeded 28%. The genome modification method for tea protoplasts established in this study can serve as a screening protocol to evaluate the in vivo efficiency of different genome editing approaches in the tea plant.

The present study examines the effect of viruses on forest insects depending on the virus dose. Two model approaches are used to quantify the effect of viruses on insect survival. Both approaches describe the processes of virus exposure to insects within the framework of the second-order phase transition model, which is well known in theoretical physics. The first approach examines the temporal dynamics of larval survival at a given dose of virus exposure. This dependence is characterized by the time-effect curve. In this case, the lethal time of exposure LT100 is the time required for the death of all larvae in the experiment at a given dose D of exposure. The second approach describes the relationship between the proportion qr of larvae that survived a fixed time Tc after the start of the experiment and the dose D of virus exposure. This dependence is characterized by the dose-effect curve. The experiments tested the effect of two different viruses-nucleopolyhedrovirus (NPV) and cypovirus (CPV)-on such insect species as Lymantria dispar L., Manduca sexta L. and Loxostege sticticalis L. It was shown that the proposed models of second-order phase transitions very accurately (with coefficients of determination of the models close to R2 = 0.95) describe experiments on studying the effect of different virus strains on insect survival. The proposed models turned out to be useful for assessing the effectiveness of different virus strains against insect pests. Since the parameters of the second-order "dose-time" and "dose-effect" phase transition models are related to each other, it is possible to reduce the number of measurements of virus-insect interaction due to the relationship between these parameters, and instead of n observations of insect dynamics over time depending on the dose of exposure, the basic parameters characterizing the "virus-insect" interactions can be accurately estimated using only one measurement. It appears that the proposed model can be used to calculate the effect of toxic agents on the population of victims for a wide variety of toxicant species and populations. A sharp reduction in the labor intensity of experiments to assess the toxicity of certain toxicants on animal populations will simplify and reduce the cost of testing the response of living objects to toxicants.

Visual statistical learning (visual SL) is the ability to implicitly extract statistical patterns from visual stimuli. Visual SL could be assessed using online measures, evaluating reaction times (RTs) to stimuli during task performance, and offline measures, which assess recognition of the presented patterns. We examined 96 children aged 3-9 years using a visual SL task that included online and offline measures. In the online phase, children viewed sequences of cartoon aliens presented one at a time, organized into triplets. The task was to press a button to two target stimuli: one predictable (the last alien in the triplet), and one unpredictable (the first in the triplet). In the offline phase, children performed a two-alternative-forced choice task, where they viewed two triplets and selected the one matching the sequence from the online phase. In online measures, we observed a gradual increase in RT for unpredictable stimulus and a slight decrease in RT for predictable stimulus over the experiment, with fewer errors for predictable stimulus, indicating an SL effect. In offline measures, the SL effect was also observed, though less robust: recognition rates for correct triplets exceeded chance level only for triplets containing predictable stimuli. Notably, while online measures remained stable across age, offline recognition rates increased with age, suggesting a link to the development of cognitive functions needed for explicit task performance. We propose that SL is not purely an implicit process but rather an active learning process shaped by experimental task requirements and goal setting.