Individual Selection Research Articles

Ранний сорт рапса озимого Актив

The early maturing variety Aktiv was developed as a result of the individual selection combined with selfpollination of plants from a hybrid population (No. 38787 х a variety Express), which was conducted during eight progenies with estimation in breeding nurseries of 1st and 2nd studying years. Then the best lines were included into a control nursery, nursery of preliminary and competitive trials. In a competitive trial, the variety Aktiv differed with stable high yield exceeding the early maturing standard variety Elvis by 0.44 t/ha in average for three years. A duration of a vegetative period of the new variety is significantly shorter comparing with early maturing standard varie-ty. The plants of the variety Aktiv are lower by 13 cm and more resistant to lodging than the plants of the variety Elvis. The early maturing variety Aktiv is similar to the standard variety Elvis by oil, glucosin-olate, and protein contents. Due to the results of ecological testing of 2022 in the Northern-Caucasus, Central and Central Black Soil zones of the Russian Federation, the new variety Aktiv exceeded the standard variety Elvis by yield in average by 0.24 t/hа. The variety Aktiv is recommended for cultivation in the North-Western. Central, Central Black Soil, and Northern-Caucasus regions of the Russian Federation. The variety is submitted in the State trial in 2023.

Высокомасличный сорт рапса ярового Юбилейный 23

In 2023, to the State commission of the Russian Federation on testing and protection of breeding achievements, approved for cultivation, the new variety of spring rapeseed Yubileyny 23 was submitted. It was developed in the Siberian Experimental Station – a branch of V.S. Pustovoit All-Russian Research Institute of Oil Crops by a method of individual selection from the variety Yubileyny, which has been included into the State register of breeding achievements since 1998. The breeding work was aimed on improvement of main economically valuable traits: very early maturation, yield, oil content, resistance to lodging, shattering, and main pathogens. In 2016, an elite plant No. 34863 was selected. The main feature of this variety is high fat content in seeds (up to 53.0%) that allows increasing oil yield per unit area. Due to competitive trial results (2021–2023), the variety if spring rapeseed Yubileyny 23 exceeded the standard variety Granit by seed yield by 0.25 t/ha and by oil yield by 168 kg/ha, and the variety Yubileyny – by 0.46 t/ha and 298 kg/ha, respectively. The new variety is supposed to be produced in Volga-Vyatka (4), Middle Volga (7), Urals (9), Western Siberian (10), and Eastern Siberian (11) regions of cultiva-tion. An originator of the variety Yubileyny 23 is V.S. Pustovoit All-Russian Research Institute of Oil Crops.

A Pipeline and Recommendations for Population and Individual Diagnostic SNP Selection in Non-Model Species.

Despite substantial reductions in the cost of sequencing over the last decade, genetic panels remain relevant due to their cost-effectiveness and flexibility across a variety of sample types. In particular, single nucleotide polymorphism (SNP) panels are increasingly favoured for conservation applications. SNP panels are often used because of their adaptability, effectiveness with low-quality samples, and cost-efficiency for population monitoring and forensics. However, the selection of diagnostic SNPs for population assignment and individual identification can be challenging. The consequences of poor SNP selection are under-powered panels, inaccurate results, and monetary loss. Here, we develop a novel anduser-friendly SNP selection pipeline (mPCRselect) that can be used to select SNPs for population assignment and/or individual identification. mPCRselect allows any researcher, who has sufficient SNP-level data, to design a successful and cost-effective SNP panel for a diploid species of conservation concern.

Animal personalities influence male mating behavior and reproductive fitness

Abstract The relationship between individual personality and sexual selection has the potential to provide insights into how variation in personality traits is maintained. However, whether and how individuals with different personality traits have different mating behaviors and the fitness consequences has remained largely unexplored. We tested the hypothesis that male individuals have different mating behavior depending on their personality traits, and that there are fitness effects of personality traits. To do so, we conducted trials to quantify personality traits and observed mating behavior under standardized conditions in the red flour beetle (Tribolium castaneum). We also collected the number of offspring produced by their female mates to assess the association between male personality and their reproductive fitness. We found that male beetles showed consistent inter-individual differences in walking distance, emergence time from shelter and death-feigning time, which likely corresponding to two personality traits of activity and boldness. Although larger males had higher mating success and produced more offspring, some personality traits were also strongly related to mating behavior and reproductive fitness. Males with longer walking distances tended to seek female mates more promptly. Other than the morphological trait of body size, the emergence time from shelter (boldness) also showed a positive relationship with the number of matings and offspring reproduced. Furthermore, we found that the interaction between death-feigning time and walking distance in males influenced the number of matings they obtain, with either shy but active or bold but inactive males achieving more matings. We therefore highlight that alongside morphological traits, certain personality traits can also play a role in affecting mating behaviors and even reproductive fitness in males.

Selection of Atlantic Forest species in South Bahia, Brazil

The study aimed to evaluate the growth potential of several Atlantic Forest species in South Bahia, displayed in high dense grid and select the best trees through selection indexes. The experiment was carried out in the Comissão Executiva do Plano da Lavoura Cacaueira (CEPLAC) located in the South of Bahia state, Brazil. A total of 44 Atlantic Forest species were tested with three replications with plot containing 36 individuals that were arranged in 2 × 2 m spacing. Experimental parameters indicated high variability among species to high density spacing, which is favorable to commercial pure stands practiced in the forestry sector. Statistical selection of native species was evaluated using selection indices based on volumetric production and survival traits. These indices allowed select species by their adaptation into the experimental region. The top two ranked species were Pterygota brasiliensis Allemão (Folheiro ou pau-rei) and Plathymenia reticulata Benth. (Vinhático), which adapted well to a tropical, humid, and rainy weather, such as the climate in South Bahia, Brazil. From this study, it was possible to subsidize the selection of ideal species and individuals for seed collection, indication of materials to assemble breeding populations, cloning, and orientate future studies in South Bahia state, Brazil.

New winter common wheat variety ‘Saban’ for the middle Volga Region

Winter wheat in the Republic of Tatarstan is sown annually on an area of more than 300 K hectares. The purpose of the current work was to develop a new variety ‘Saban’ and characterize its ability to produce high yields and grain quality in the northern regions of the Middle Volga region. The study was conducted on the fields of the Tatarsky RIA, SSU of the FRC KazRC of RAS in 2020–2022. Seeds were laid in ‘black’ fallow at the optimal time. The variety ‘Saban’ was developed by the method of individual selection from a hybrid population obtained from crossing the varieties ‘Kazanskaya 285’ and ‘Bezenchukskaya 380’. On average, over three years of testing, winter resistance was 88 %, and the plant height was 74 cm, which is 9.7 cm lower than that of the standard variety ‘Kazanskaya 560’. Lodging resistance was 8.7 points with a coefficient of variation of 6.66 %. The variety ‘Saban’ is of the intensive type, responsive to the application of complex fertilizers. The variety was better than the standard variety ‘Kazanskaya 560’ in productivity by 0.64 t/ha. Under arid conditions of 2021 the variety yielded 4.20 t/ha, which was 0.65 t/ha more than the standard variety did. This result was due to the productive stem density and a large number of grains per ear. The variety produced heavy kernels, its nature weight was 770–802 g/l. Crude protein percentage in grain ranged from 13.2 % to 15.2 %, and gluten varied from 27.0 % to 29.7 %. The variety formed high indicators of rheological qualities, such as 301 u.a. of flour strength, and 59 % of valorimetric estimation. The general baking assessment for three years of study was 4.5 points. The variety can be recommended for cultivation in the Middle Volga region of the Russian Federation from 2024.

USE OF TUBING COATINGS AND SCALE INHIBITORS TO PREVENT SCALING

The problem of formation of inorganic scaling in tubing is a common problem in oil production processes. The formation of salts on the inner surface of the tubing leads to a significant narrowing of the flow section of the pipe. An urgent task is to find methods, the use of which will either prevent or reduce the formation of inorganic scaling on the walls of pipes. Methods of removing scaling from the tubing are expensive, time consuming, and require shutdown of the oil production facility. Therefore, it is preferable to use methods for preventing salt formation in tubing. Methods of preventing the processes of scaling in the tubing include physical, chemical and technological. The most common method of preventing scaling is the use of scale inhibitors (chemical method). The use of internal protective pipe coatings (technological method) as a measure to prevent the formation of inorganic scaling on the walls of the tubing is a promising, but not fully studied method. The use of coatings is promising, since in addition to the potential reduction in the formation of scaling on the walls of pipes, their main function is corrosion protection.One of the methods of preventing salt formation in the tubing may be the integrated use of internal protective coatings of the tubing and scale inhibitors. But, since this method is not universal, an individual selection of the most effective coating-inhibitor combination will be required for each oil production facility. Selection of an effective coating-inhibitor combination using pilot field tests is an expensive and time-consuming procedure, therefore, it is necessary to develop a laboratory method to solve this problem. The article proposes a method that makes it possible to evaluate the effectiveness of the complex use of protective coatings and scale inhibitors to prevent the formation of inorganic salts on the inner surface of tubing in laboratory conditions. The results of bench tests for 9 grades of protective coatings and for 6 grades of scale inhibitors (in different dosages) are presented.

Selection indexes in terms of functional features in modern dairy cattle breeding in Europe

Abstract The increase in demand for dairy products requires continued progress in dairy farming for a sustainable supply. Europe, known as the world's leading milk producer, plays a key role in meeting this growing demand. Modern dairy farming has moved beyond its historical focus on milk yield and now focuses on functional traits such as udder health, fertility and calf survival. As a result, selection indicators have become essential tools, combining multiple attributes to support selective decisions. However, these rates show considerable variability across countries, reflecting their distinct breeding goals. Poland's production and functionality (PF) Index emphasizes production and functional traits to enhance dairy cattle. Portugal uses the total economic merit (M€T) and total performance index (IPT) for a broader assessment covering a wider range of traits. Ukraine is transitioning towards a more comprehensive breeding system incorporating stress tolerance and longevity. Factors such as climate change and the need for sustainable practices drive this evolution, underscoring the economic importance of traits beyond mere production. Future trends may include features such as feed efficiency, methane emissions reduction and stress resistance. Diverse breeding objectives across countries lead to different selection index constructions, essential for effective selection, ranking and breeding of superior individuals. This comprehensive review offers insight into constantly evolving dairy farming strategies in Europe, with a focus on Poland, Portugal and Ukraine, while highlighting the key role of functional traits in shaping the future of dairy farming.

Prefrontal cortex engagement during an fMRI task of emotion regulation as a potential predictor of treatment response in borderline personality disorder.

Borderline personality disorder (BPD) is a severe mental illness, with high rates of co-morbid depression and suicidality. Despite the importance of optimizing treatment in BPD, little is known about how neural processes relate to individual treatment response. This study examines how baseline regional brain blood oxygen level dependent (BOLD) activation during a functional magnetic resonance imaging (fMRI) task of emotion regulation is related to treatment response following a six-month randomized clinical trial of Dialectical Behavior Therapy (DBT) or Selective Serotonin Reuptake Inhibitor (SSRI) treatment. Unmedicated females with BPD (N = 37), with recent suicidal behavior or self-injury, underwent an fMRI task in which negative personal memories were presented and they were asked to distance (i.e., downregulate their emotional response) or immerse (i.e., experience emotions freely). Patients were then randomized to DBT (N = 16) or SSRI (N = 21) treatment, with baseline and post-treatment depression and BPD severity assessed. BOLD activity in prefrontal cortex, anterior cingulate, and insula was associated with distancing. Baseline BOLD during distancing in dorsolateral, ventrolateral, and orbital prefrontal cortex (dlPFC, vlPFC, OFC) differentially predicted depression response across treatment groups, with higher activity predicting better response in the SSRI group, and lower activity predicting better response in the DBT group. All female samples. Findings indicate that greater prefrontal engagement during emotion regulation may predict more antidepressant benefit from SSRIs, whereas lower engagement may predict better response to DBT. These results suggest different mechanisms of action for SSRI and DBT treatment, and this may allow fMRI to guide individualized treatment selection.

Clinical predictors of surgical selection for individuals with neck pain

BackgroundNeck pain is one of the leading causes of disability in the United States. However, the nature of cervical spine surgery is multi-factorial and often based upon clinical opinions, leaving clinical indications still to be clearly defined. ObjectiveTo predict which clinical variables most influenced trends toward surgical or conservative management for individuals with neck pain. DesignRetrospective analysis on prospectively collected data. MethodsTwo-hundred seventy individuals participated in the study. A retrospective chart review was performed to identify qualifying participants. Variables captured during data collection included the following: patient demographic information, physical examination (e.g., neurologic examination), physical performance measures, patient-reported outcome measures (Numeric Pain Rating Scale and Neck Disability Index), and chronicity of symptoms. Group allocation was determined by referring physician’s medical diagnosis. A forward step-wise multiple logistic regression analysis was used to develop a final predictor model with p<0.05 considered significant. ResultsThe model showed older age (beta=0.022; Exp(beta)=1.022; p=0.037), longer chronicity of symptoms (beta=0.000; Exp(beta)=1.000; p=0.012), and greater neck disability (beta=0.027; Exp(beta)=1.028; p<0.001) were significant predictors of the operative group. Most notably, patients with a positive neurologic examination were almost four times more likely to be referred to surgery (beta=1.368; Exp(beta)=3.927; p<0.001). ConclusionIn general neck pain patients, older age, longer chronicity of symptoms, greater disability, and a positive neuro exam are clinical predictors of surgical selection, with positive neuro exam increasing the likelihood of surgery four-fold.

Parental-effect gene-drive elements under partial selfing, or why do Caenorhabditis genomes have hyperdivergent regions?

Self-fertile Caenorhabditis nematodes carry a surprising number of Medea elements, alleles that act in heterozygous mothers and cause death or developmental delay in offspring that don't inherit them. At some loci, both alleles in a cross operate as independent Medeas, affecting all the homozygous progeny of a selfing heterozygote. The genomic coincidence of Medea elements and ancient, deeply coalescing haplotypes, which pepper the otherwise homogeneous genomes of these animals, raises questions about how these apparent gene-drive elements persist for long periods of time. Here I investigate how mating system affects the evolution of Medeas, and their paternal-effect counterparts, peels. Despite an intuition that antagonistic alleles should induce balancing selection by killing homozygotes, models show that, under partial selfing, antagonistic elements experience positive frequency dependence: the common allele drives the rare one extinct, even if the rare one is more penetrant. Analytical results for the threshold frequency required for one allele to invade a population show that a very weakly penetrant allele, one whose effects would escape laboratory detection, could nevertheless prevent a much more penetrant allele from invading under high rates of selfing. Ubiquitous weak antagonistic Medeas and peels could then act as localized barriers to gene flow between populations, generating genomic islands of deep coalescence. Analysis of gene expression data, however, suggest that this cannot be the whole story. A complementary explanation is that ordinary ecological balancing selection generates ancient haplotypes on which Medeas can evolve, while high homozygosity in these selfers minimizes the role of gene drive in their evolution.

Refined selection of individuals for preventive cardiovascular disease treatment with a transformer-based risk model

Abstract Background Although statistical models have been commonly used to identify patients at risk of cardiovascular disease (CVD) for preventive therapy, these models tend to over-recommend therapy [1]. Moreover, in populations with pre-existing diseases, the current approach is to indiscriminately treat all individuals, as modelling in this context is currently inadequate. Aims To develop and validate a novel deep learning model, the Transformer-based Risk assessment survival model (TRisk) model, for prediction of 10-year risk of CVD in both the general population and individuals with diabetes. Methods An open cohort of 3 million adults aged 25 to 84 years free of CVD and statin use was identified using linked primary and secondary electronic health records from 291 and 98 general practices in England, which were used for TRisk model development and external validation, respectively. Comparison against two commonly recommended statistical models (QRISK3 [2] and SCORE2 [3]) and a deep learning derivation of QRISK3 was conducted. Additional analyses compared discriminatory performance in other age groups, by sex, and across categories of socioeconomic status. Similar analyses were conducted in a subgroup of 59 thousand patients with diabetes. Results TRisk demonstrated superior discrimination (C index in the general population: 0.910; 95% confidence interval [CI]: 0.906 to 0.913). TRisk’s performance was found to be less sensitive to population age range than the benchmark models and outperformed other models also in all stratified analyses. All models were overall well-calibrated. In decision curve analyses, TRisk demonstrated greater net benefit than benchmark models across the range of relevant thresholds. In impact analyses, at the widely recommended 10% risk threshold and the higher 15% threshold for the general population, TRisk reduced both the total number of patients classified at high risk (by 27% and 39% respectively) and the number of false negatives as compared with currently recommended strategies. TRisk similarly outperformed other models in patients with diabetes. Compared with the widely recommended treat-all policy approach for patients with diabetes, TRisk at a 10% risk threshold would lead to deselection of 24% of individuals with a small fraction of false negatives (0.2% of cohort). Conclusion TRisk enabled a more targeted selection of individuals at risk of CVD compared to benchmark statistical and deep learning models, in both the general population and patients with diabetes. Incorporation of TRisk into routine clinical care would allow a reduction in the number of treatment-eligible patients in general population and subgroup with diabetes by approximately one-third and one-quarter respectively while preventing at least as many events as with currently adopted approaches.Models' discrimination on both cohortsImpact analyses for both cohorts

Visual coronary artery calcium assessment on routine chest CT can readily and cheaply identify liver transplant recipients with a low cardiac risk

Abstract Background Peri- and postoperative cardiac complications play an important role in survival of patients after liver transplantation (LT). However, the optimal approach for cardiac risk prediction in these patients has been debated on. LT recipients routinely receive non-gated computed tomography (CT) of the abdomen and chest to follow-up on their liver diseases. This study analyzed whether performance of coronary artery calcium (CAC) assessment on freely available scans can enhance cardiac risk stratification of LT recipients. Methods Consecutive LT recipients from 2008-2023 were included and analyzed retrospectively. CAC was scored visually on non-gated CT on a semi-quantitative ordinal scale and stratified in two groups according to CAC severity. Peri-operative (≤90 days) and post-operative all-cause mortality and cardiac events were analyzed. Furthermore, all-cause mortality and cardiac events were analyzed and compared between the groups with a Kaplan Meier curve and Cox proportional hazard regression. Results A total of 149 patients were included with a median age of 58 years. The median follow-up time was 5 years. The majority (74%) of patients had no or mild CAC. None of these patients died perioperatively due to cardiac causes, or experienced peri-operative cardiac events. The post-operative 10-year survival of LT recipients with no or mild calcifications was significantly better (p&amp;lt;0.01) than patients with severe CAC. The 10-year cardiac event-free survival was significantly better in patients with no or mild CAC (p&amp;lt;0.01) and the incidence increased &amp;gt;4-fold from only 4% in patients with no-mild CAC to 18% in patients with severe CAC. Of interest, severe CAC was a significant predictor of cardiac events (HR 4.6, p=0.02), while classical cardiovascular risk factors e.g. diabetes were not. Conclusion CAC assessment on routinely available non-gated CT can readily enhance peri- and postoperative cardiac risk stratification of LT recipients, as it identifies a selection of low-risk individuals for having peri- and post-operative cardiac events and all-cause mortality. Thereby, reducing test burden for the patients and saving healthcare expenses.

Population age structure shapes selection on social behaviour in a long-lived insect.

Social traits are expected to experience highly context-dependent selection, but we know little about the contextual factors that shape selection on social behaviours. We hypothesized that the fitness consequences of social interactions will depend on the age of social partners, and therefore that population age structure will shape evolutionary pressures on sociality. Here, we investigate the consequences of age variation at multiple levels of social organization for both individual fitness and sexual selection on social network traits. We experimentally manipulated the age composition of populations of the forked fungus beetle Bolitotherus cornutus, creating 12 replicate populations with either young or old age structures. We found that fitness is associated with variance in age at three different levels of organization: the individual, interacting social partners, and the population. Older individuals have higher reproductive success, males pay a fitness cost when they interact with old males and females achieve lower fitness in older populations. In addition to influencing fitness, population age structure also altered the selection acting on social network position in females. Female sociality is under positive selection only in old populations. Our results highlight age structure as an understudied demographic variable shaping the landscape of selection on social behaviour.This article is part of the discussion meeting issue 'Understanding age and society using natural populations'.

Proteomic and genomic evaluation of plasma protein biomarkers for atrial fibrillation

Abstract Background Atrial fibrillation (AF) is a common arrhythmia and an important cause of ischemic stroke. Several plasma proteins have been suggested as biomarkers for the risk of AF and subsequent stroke in previous studies, but validation in independent cohorts is needed. Large-scale integration of proteomics with genetic and clinical data holds potential for refinement of disease prediction and risk stratification. Purpose This study aimed to evaluate previously suggested plasma protein biomarkers of AF and ischemic stroke using plasma protein measurements in a large cohort. Methods Ten tentative protein biomarkers previously associated with AF or ischemic stroke in AF patients, were evaluated in a nested case-control study comprising 49,757 individuals from the UK biobank (UKB). Plasma proteins were measured by normalized protein expression on the Olink platform. Outcomes were incident AF and ischemic stroke. The proteins were evaluated in Cox regression models adjusted for sex, age, and clinical risk factors. To assess whether the proteins were independent predictors, they were evaluated in sensitivity analyses with additional adjustment for NT-proBNP levels. Polygenic risk scores were calculated for plasma protein concentrations using the PRS-CS-auto algorithm and tested for association with AF or ischemic stroke in an independent subset of the UKB cohort without plasma protein measurements (n=379,392). Results All ten plasma proteins were associated with incident AF in the primary model, and eight were associated with incident ischemic stroke (P-values &amp;lt; 0.0025 for all). Adjusted for NT-proBNP levels, the proteins interleukin-6, GDF15, angiopoietin 2, and Troponin-I remained independently associated with AF, and the proteins GDF15, Cystatin-C, interleukin-6, ADAMTS13, and FGF23 remained independently associated with ischemic stroke. Polygenic risk scores for NT-proBNP and angiopoietin 2 were associated with increased rates of AF. No polygenic scores were significantly associated with ischemic stroke. Conclusion Large-scale proteomic evaluation validated ten plasma proteins associated AF and found eight to be associated with ischemic stroke. Seven proteins were associated with AF or ischemic stroke independently of NT-proBNP levels. Additional genomic evaluation provided evidence of associations between incident AF and genetically predicted plasma levels of angiopoietin 2 and NT-proBNP. As proteomic analyses of the plasma proteome become cheaper and more readily available, these findings could contribute to future risk stratification and selection of individuals for AF screening programs.Proteins associated with AFProteins associated with ischemic stroke

TIBA: A web application for the visual analysis of temporal occurrences, interactions, and transitions of animal behavior.

Data in behavioral research is often quantified with event-logging software, generating large data sets containing detailed information about subjects, recipients, and the duration of behaviors. Exploring and analyzing such large data sets can be challenging without tools to visualize behavioral interactions between individuals or transitions between behavioral states, yet software that can adequately visualize complex behavioral data sets is rare. TIBA (The Interactive Behavior Analyzer) is a web application for behavioral data visualization, which provides a series of interactive visualizations, including the temporal occurrences of behavioral events, the number and direction of interactions between individuals, the behavioral transitions and their respective transitional frequencies, as well as the visual and algorithmic comparison of the latter across data sets. It can therefore be applied to visualize behavior across individuals, species, or contexts. Several filtering options (selection of behaviors and individuals) together with options to set node and edge properties (in the network drawings) allow for interactive customization of the output drawings, which can also be downloaded afterwards. TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. The web application and usage instructions are available at tiba.inf.uni-konstanz.de. The source code is publicly available on GitHub: github.com/LSI-UniKonstanz/tiba.

In silico drug repurposing approach to predict most effective HAART for HIV drug resistance variants prevalent in the Indian HIV-positive population.

HIV epidemics still exist as a major global public health burden, especially in middle- and low-income countries. Given the lack of approved vaccines, antiretroviral therapy (ART) remains the primary approach to reduce the mortality and morbidity linked to this disease. Effective treatment for HIV-1 requires the simultaneous administration of multiple drugs. However, the virus can show resistance to antiretroviral drugs, resulting in treatment failure. Therefore, this study focused on assessing the prevalence of mutations within the Indian HIV-positive population. After assessing the data, we intended to identify the most effective highly active ART (HAART) regimens for individuals with drug-resistant variants. Furthermore, our analysis revealed a spectrum of HIV mutations, with varying effects on protein stability. The significance of this analysis lies in its potential to optimize HAART selection for HIV-positive individuals by accounting for both prevalence and stability-altering mutations. By considering mutation effects on protein stability, we can modify treatment regimens, increasing the likelihood of therapy success and diminishing the risk of resistance. Moreover, this study contributes to the broader field of drug repurposing, offering insights into the rational design of antiretroviral therapies.

The representational geometry for naturalistic textures in macaque V1 and V2.

Our understanding of visual cortical processing has relied primarily on studying the selectivity of individual neurons in different areas. A complementary approach is to study how the representational geometry of neuronal populations differs across areas. Though the geometry is derived from individual neuronal selectivity, it can reveal encoding strategies difficult to infer from single neuron responses. In addition, recent theoretical work has begun to relate distinct functional objectives to different representational geometries. To understand how the representational geometry changes across stages of processing, we measured neuronal population responses in primary visual cortex (V1) and area V2 of macaque monkeys to an ensemble of synthetic, naturalistic textures. Responses were lower dimensional in V2 than V1, and there was a better alignment of V2 population responses to different textures. The representational geometry in V2 afforded better discriminability between out-of-sample textures. We performed complementary analyses of standard convolutional network models, which did not replicate the representational geometry of cortex. We conclude that there is a shift in the representational geometry between V1 and V2, with the V2 representation exhibiting features of a low-dimensional, systematic encoding of different textures and of different instantiations of each texture. Our results suggest that comparisons of representational geometry can reveal important transformations that occur across successive stages of visual processing.

Adaptive Bi-Level Variable Selection for Quantile Regression Models with a Diverging Number of Covariates

The paper develops an innovatively adaptive bi-level variable selection methodology for quantile regression models with a diverging number of covariates. Traditional variable selection techniques in quantile regression, such as the lasso and group lasso techniques, offer solutions predominantly for either individual variable selection or group-level selection, but not for both simultaneously. To address this limitation, we introduce an adaptive group bridge approach for quantile regression, to simultaneously select variables at both the group and within-group variable levels. The proposed method offers several notable advantages. Firstly, it adeptly handles the heterogeneous and/or skewed data inherent to quantile regression. Secondly, it is capable of handling quantile regression models where the number of parameters grows with the sample size. Thirdly, via employing an ingeniously designed penalty function, our method surpasses traditional group bridge estimation techniques in identifying important within-group variables with high precision. Fourthly, it exhibits the oracle group selection property, implying that the relevant variables at both the group and within-group levels can be identified with a probability converging to one. Several numerical studies corroborated our theoretical results.

The effect of sowing dates on the yield of soybean (Glycine max (L.) Merr.) accessions differing in maturity schedules under the climate conditions of Irkutsk Province

Background. Soybean is the main supplier of complete vegetable protein and oil. Prior to the emergence of new early-ripening cultivars, this crop was not cultivated in Irkutsk Province. Its introduction into the area requires a detailed study of agricultural practices, primarily the choice of optimal sowing dates. Materials and methods. Soybean cv. ‘Barguzin’, late-ripening under the conditions of Irkutsk Province, and early-ripening accession 15 served as the research objects. Field studies were carried out in 2019–2021. There were four sowing dates, from the first ten-day period of May through the first ten-day period of June. Results and conclusion. Cv. ‘Barguzin’ appeared highly productive under the climate of Irkutsk Province (its average seed yield in 2019–2021 was 2.7 t/ha), but its productivity was significantly influenced by weather conditions and sowing dates (seed yields varied from 1.4 to 3.6 t/ha). The optimal sowing times for this cultivar are the first and second ten-day periods of May. Early-ripening accession 15 was characterized by more stable yield values in the studied years (average productivity was 2.4 t/ha, minimum 1.9 t/ha, and maximum 3.2 t/ha). The preferred sowing times for this accession are the second and third ten-day periods of May. The case study of two soybean genotypes differing in maturation schedules revealed the need for individual selection of sowing dates. It was shown that the early-ripening soybean genotype imposed less stringent requirements on the choice of sowing dates.