Information Content Research Articles

Research Note: Whole-genome sequencing revealed genomic diversity dynamics in duck conserved populations

The purpose of this study was to comprehensively analyze the genetic diversity of the Pekin duck conserved population with five generations and to evaluate the effectiveness of the current conservation strategy. In total, 277 Pekin duck conserved individuals and 40 Mallards as ancestral controls were collected. Each duck was sequenced at about 10X whole-genome coverage, while over 7.4 million single nucleotide polymorphisms (SNPs) in total were detected for genetic diversity analysis. Both the expected heterozygosity and observed heterozygosity values exceeded 0.3. The genetic differentiation (FST) values ranged from 0.007 to 0.039, and the Polymorphism information content (PIC) values ranged from 0.29 to 0.34. These results indicate no significant differentiation between generations, and the genetic diversity remains high. In particular, the inbreeding coefficient has been strictly controlled and has not increased rapidly during the conservation. Overall, the inbreeding coefficient of the conserved Pekin duck population was higher than that of its wild ancestors, indicating that domestication has resulted in reduced genetic diversity. This is the first report using whole genome resequencing data to systematically evaluate the genomic dynamics across several generations in ducks. The results show that the strategy of free mating and random seed retention within sire families is effective for maintaining the genetic diversity of the conserved Pekin duck population.

Genome-Wide Development and Characterization of Microsatellite Markers in the Great Web-Spinning Sawfly Acantholyda posticalis.

The great web-spinning sawfly Acantholyda posticalis is notorious for damaging Pinus forests across the Palearctic region. At present, uncertainties persist regarding its intraspecies variation and presumed subspecies. To use as tools for future studies, herein we developed genome-wide microsatellite markers for A. posticalis. Through searching, rigorous manual screening, and amplification trial, 56 microsatellite markers were obtained from the genome sequences. We characterized these markers across two populations from Shandong province (SD) and Heilongjiang province (HLJ) in China, and carried out cross-amplification in three related species. Out of the 56 markers tested, 10, 31, and 15 were categorized into high, moderate, and low polymorphic levels, respectively, based on their polymorphic information content (PIC) values. Meanwhile, 28, 19, and 4 microsatellite loci were successfully cross-amplified in Cephalcia yanqingensis, C. chuxiongica, and C. infumata, respectively, which could serve as potential molecular markers for their further studies. STRUCTURE and PCoA analyses revealed two distinct clusters corresponding to SD and HLJ, respectively, indicating a high resolution of these markers. Therefore，the 56 microsatellite markers identified here have the potential to serve as efficient tools for unraveling intraspecies variation and evolutionary history of A. posticalis.

Quijote-PNG: Optimizing the Summary Statistics to Measure Primordial Non-Gaussianity

We apply a suite of different estimators to the Quijote-png halo catalogs to find the best approach to constrain Primordial non-Gaussianity (PNG) at nonlinear cosmological scales, up to kmax=0.5hMpc−1 . The set of summary statistics considered in our analysis includes the power spectrum, bispectrum, halo mass function, marked power spectrum, and marked modal bispectrum. Marked statistics are used here for the first time in the context of the PNG study. We perform a Fisher analysis to estimate their cosmological information content, showing substantial improvements when marked observables are added to the analysis. Starting from these summaries, we train deep neural networks to perform likelihood-free inference of cosmological and PNG parameters. We assess the performance of different subsets of summary statistics; in the case of fNLequil , we find that a combination of the power spectrum and a suitable marked power spectrum outperforms the combination of power spectrum and bispectrum, the baseline statistics usually employed in PNG analysis. A minimal pipeline to analyze the statistics we identified can be implemented either with our ML algorithm or via more traditional estimators, if these are deemed more reliable.

Visualization tools as a method of working with text in the specialty "Economics" in the Chinese audience

Introduction. New requirements for the competencies of economic specialists in an intensively changing international situation dictate the need to apply a new format for teaching the language of the specialty in a foreign language (in this case, in Russian), a format characterized by maximum efficiency and productivity and focused on the specifics of students. It is also necessary to define learning strategies for working with text in the specialty as a basic element in the development of professional competencies among students. The new format of teaching the language of the specialty necessitates the use of information visualization technology as one of the most advanced at the present time. The purpose of the study is to identify / establish a set of the most effective visualization tools when working with the language of the specialty in the profile "Economics" in the Chinese audience. Methods and materials. The experimental basis of the study was a group of Chinese students of the economic profile of Peter the Great St. Petersburg Polytechnic University (Russian Federation). A total of 40 students took part in the experiment. The questionnaire developed by the authors included questions concerning the specifics of the visual component, the degree of difficulty / lightness of the text depending on the type of visual medium, as well as the percentage of efficiency of processing, assimilation and reproduction of information. Results. It was revealed that Chinese students, when working with economic texts, perceive infographics better as visual accompaniment – 25% and a flowchart – 25%, that is, these components facilitate the primary perception of the text. The students pointed out the low informative content of the graph and its complementary nature, 36% of respondents considered the text with the graph to be the most difficult, 19% also considered the drawing to be uninformative. When comparing texts with different types of visualization, the text with a diagram was called absolutely easy, and the text with a graph was called absolutely difficult. In turn, the majority of respondents indicated that when retelling a text, they are more often guided by a plan, keywords and a scheme created on the basis of the text. 36% of Chinese trainees considered a diagram to be the most preferred way of visualization in the text, 24% – infographics and 16% – keywords. The drawing and graph were considered uninformative. Conclusion. The conducted experiment confirmed the peculiarities of the perception of texts in the specialty by Chinese students, and also helped to identify the most effective visualization tools for this contingent. The data obtained can be used to create textbooks on Russian as a foreign language for Chinese economics students.

HOIMotion: Forecasting Human Motion During Human-Object Interactions Using Egocentric 3D Object Bounding Boxes.

We present HOIMotion - a novel approach for human motion forecasting during human-object interactions that integrates information about past body poses and egocentric 3D object bounding boxes. Human motion forecasting is important in many augmented reality applications but most existing methods have only used past body poses to predict future motion. HOIMotion first uses an encoder-residual graph convolutional network (GCN) and multi-layer perceptrons to extract features from body poses and egocentric 3D object bounding boxes, respectively. Our method then fuses pose and object features into a novel pose-object graph and uses a residual-decoder GCN to forecast future body motion. We extensively evaluate our method on the Aria digital twin (ADT) and MoGaze datasets and show that HOIMotion consistently outperforms state-of-the-art methods by a large margin of up to 8.7% on ADT and 7.2% on MoGaze in terms of mean per joint position error. Complementing these evaluations, we report a human study (N=20) that shows that the improvements achieved by our method result in forecasted poses being perceived as both more precise and more realistic than those of existing methods. Taken together, these results reveal the significant information content available in egocentric 3D object bounding boxes for human motion forecasting and the effectiveness of our method in exploiting this information.

Automated Grasp Labeling and Detection Framework with Pixel-Level Precision

Due to the differences in shape, material, and color of objects, the detection of planar grasps by robots remains challenging. Traditional methods rely on discrete grasp configurations for annotation, ignoring many possible grasp configurations. This leads to poor network generalization, making it challenging to handle a diverse range of grasped objects. Manual reannotation to continuous labels can effectively address this issue but comes with a significant cost. Therefore, this paper proposes a Pixel-level Grasp framework. Firstly, APGLG can automatically generate pixel-level grasp labels and discrete labels into continuous labels, effectively increasing the information content of individual data and improving the network generalization performance. Then, we propose the Max-Grasp-Net, built on the Multi-axis Vision Transformer and Dynamic Convolution Decomposition, to construct a U-shaped Network structure. A grasp decoder is incorporated, and deep supervision is applied to enhance network generalization. On the Cornell dataset, we achieve the best results, a grasping accuracy of 99.55%, an average success rate of 97.92% in single-object grasping, and 95.83% in multi-object grasping. We verified the effectiveness of our label generation algorithms and network innovation through actual grasp experiments.

Population structure and markers analysis associated with powdery mildew resistance in pumpkin, Cucurbita pepo L

Pumpkin (Cucurbita pepo L.) is an economically important vegetable and summer crop. The seeds have high nutritional value due to their edible oil and protein content with a high medicinal value. Pumpkin is attacked by several fungal diseases; one of which is powdery mildew (PM) caused by Podosphaera xanthii race-0. PM causes severe disease on pumpkin leading to major yield losses globally. In this study, we analyzed the genetic diversity and population structure of fourteen local pumpkin cultivars, all under Cucurbita pepo L. based on dominance in the region using 15 Inter-Simple-Sequence-Repeats-(ISSR) primer-pairs in association with resistance against PM as well as regulating the expression of three resistant marker genes i.e. SGT1, bHLH and PSK via qRT-PCR at three stages with 10 days' interval post-inoculation-stages-(SPI) with the powdery mildew agent, P. xanthii race-0. The results showed that ISSR markers demonstrated 13 primers out of 15 primers generated 169 distinct amplified bands, out of which 143 bands showed polymorphism. The mean polymorphism information content (PIC), marker index (MI), effective-number of alleles (I), expected heterozygosity (H), and Nei's gene diversity parameters were 0.354, 3.344, 0.334, 0.205 and 0.200, respectively. According to PIC, MI, I, H, and Nei indices evaluation, ISSR10 primer had the best performance in genetic diversity of the related pumpkin population. NTSYS pc 2.02e software and several similarity coefficients (r = 0.964), placed the related genotypes into five distinct groups. Of which, ‘Maragheh1′ and ‘Shahreza’ as resistant ones with 9.4 % and 6.1 % disease severity (DS), and ‘Gorgan’, ‘Urmia1′ and 3 and ‘Maragheh3’ with 79.6–84.8 % DS, as susceptible ones into two distinct groups, respectively. Further, the marker genes SGT1, bHLH and PSK expression level changes showed that the effect of variable factors including different genotypes (susceptible and resistant), disease-causing fungus, SPI, and the mutual effect of these factors is significant with the highest increase in resistant genotypes. Considering the relative compatibility in the classification of pumpkin genotypes in the dendrograms of DS and ISSR markers, it can be expected that increasing the number of markers in evaluating the genetic diversity of these genotypes makes it possible to differentiate pumpkin genotypes resistant and susceptible to PM disease based on the degree of disease resistance. The markers associated with resistance are recommended for use in marker assisted breeding.

Antidiabetic and antioxidant profiling of 67 African trifoliate yam accessions by planar on-surface assays versus in vitro assays.

Trifoliate yam (Dioscorea dumetorum) is traditionally used to treat diabetics in Nigeria. However, almost no information is available on its antidiabetic constituents and their natural variance. Hence, the activity of methanolic tuber extracts of 67 trifoliate yam accessions from the largest collection in Africa was proven by four colorimetric antidiabetic and antioxidant in vitro assays, as diabetes is also linked with oxidative stress. For the first time, selected accessions were also analyzed by planar bioactivity profiling. It has a comparatively higher, more differentiated information content, is more sustainable in terms of material consumption, and enables straightforward compound prioritization and characterization. Up to a dozen individual antioxidant zones were revealed as well as one prominent zone inhibiting α-glucosidase and α-amylase. The latter inhibition zone was tentatively assigned to palmitic, linoleic, oleic, linolenic, oxo-nonanoic fatty acids by direct elution to heated electrospray ionization high-resolution mass spectrometry.

Development of marketing research technologies as the basis of a socially responsible marketing strategy

The object of this study was the trends in the development of marketing research technologies as a basis for the strategy of socially responsible marketing. The study is aimed at solving the problem of increasing the effectiveness of the marketing strategy under the influence of digital marketing research technologies. It has been proven that under the conditions of increased competition and transformation of consumer needs, the strategy of socially responsible marketing acquires great relevance. It has been established that the use of digital marketing research technologies significantly increases the effectiveness of the strategy of socially responsible marketing, ensuring a high degree of transparency and openness of its directions. The main stages of the development of marketing research technologies have been detailed – market research, consumer behavior research, the beginning of digitization, and the digital revolution. Digital technologies, on the basis of which marketing research will be conducted in the future, have been identified – Big Data, blockchain, artificial intelligence, the Internet of Things, augmented and virtual reality. The relationship between digital technologies of marketing research and directions of socially responsible marketing strategy was substantiated. It has been established that the effectiveness of the socially responsible marketing strategy should be determined on the basis of key performance indicators (KPI) based on the elements of the 4P complex. Schemes for researching the effectiveness of areas of social responsibility for individual elements of the marketing complex based on digital technologies are proposed. On this basis, a model of the influence of digital technologies on the KPI strategy of socially responsible marketing was developed and its advantages were substantiated. The practical use of the proposed approach could provide an opportunity to choose a specific marketing research technology depending on the target orientations of social responsibility of marketing. This would provide maximum information content and support for the strategy development and implementation process.

The effect of a new coronavirus infection on hemogram parameters in the early convalescence period of patients with coronary heart disease

Laboratory data, especially a general blood test, play an important role in the treatment of infectious diseases, including COVID-19. However, in patients with coronary heart disease (CHD) and COVID-19, a general blood test is rarely analyzed and is insufficient to fully assess an impact of the new coronavirus infection on patients with cardiac diseases. The aim of the study was to analyze a COVID-19 effect on hemogram parameters in the early recovery period of patients with coronary heart disease. The study was performed at the Kursk City Clinical Hospital for Emergency Medical Care in the years 2021–2022, which involved 58 mature-aged patients (45–59 years old) suffering from coronary heart disease who had a new coronavirus infection and 62 elderly patients suffering from coronary heart disease alone. Morning blood sampling was carried out from the forearm superficial veins into Vacutainer test tubes added with a coagulation activator. In elderly patients, 3–4 weeks after recovery, significant changes in red blood parameters persisted — a decline in the level of peripheral blood erythrocytes, hemoglobin and hematocrit. Elderly patients with coronary heart disease comorbid with a moderate new coronavirus infection, 3–4 weeks later had also higher count of leukocytes, neutrophils, segmented neutrophils and ESR level relative to comparison group. It should be noted that in the early convalescence period elderly patients with coronary heart disease and COVID-19 had significantly elevated ESR, which confirms ongoing sustained inflammatory process. After evaluating the information content according to the calculated Kullback informativeness assessment for analyzed general blood test parameters, it was found to peak for platelet and leukocyte counts as well as ESR level. Thus, among the analyzed indicators in the general blood test, it turned out that platelet and leukocyte count along with ESR level were most informative and of prognostic significance during early convalescent period of elderly patients with coronary heart disease comorbid with a new coronavirus infection, which are proposed to be used as biomarkers of early convalescent period.

Design of Shape Forming Elements for Architected Composites via Bayesian Optimization and Genetic Algorithms: A Concept Evaluation.

This article presents the first use of shape forming elements (SFEs) to produce architected composites from multiple materials in an extrusion process. Each SFE contains a matrix of flow channels connecting input and output ports, where materials are routed between corresponding ports. The mathematical operations of rotation and shifting are described, and design automation is explored using Bayesian optimization and genetic algorithms to select fifty or more parameters for minimizing two objective functions. The first objective aims to match a target cross-section by minimizing the pixel-by-pixel error, which is weighted with the structural similarity index (SSIM). The second objective seeks to maximize information content by minimizing the SSIM relative to a white image. Satisfactory designs are achieved with better objective function values observed in rectangular rather than square flow channels. Validation extrusion of modeling clay demonstrates that while SFEs impose complex material transformations, they do not achieve the material distributions predicted by the digital model. Using the SSIM for results comparison, initial stages yielded SSIM values near 0.8 between design and simulation, indicating a good initial match. However, the control of material processing tended to decline with successive SFE processing with the SSIM of the extruded output dropping to 0.023 relative to the design intent. Flow simulations more closely replicated the observed structures with SSIM values around 0.4 but also failed to predict the intended cross-sections. The evaluation highlights the need for advanced modeling techniques to enhance the predictive accuracy and functionality of SFEs for biomedical, energy storage, and structural applications.

The effectiveness of a mobile mammography units in breast cancer screening

Breast cancer remains the leading cause of death in the female population. Screening studies are conducted in order to detect the disease at an early stage. At the same time, domestic authors emphasize the lack of alternatives to preventive mammography in breast cancer screening and the prospects of using mobile mammographs for early diagnosis of breast cancer and precancerous breast diseases.Aim. To evaluat the results of the use of mobile mammography units for active and early detection of breast cancer.Materials and methods. The research uses methods of content analysis, information and analytical materials of Russian and foreign researchers, and forms of federal statistical observation. Statistical research methods were used to analyze the data obtained.Results. Regression analysis with a very low probability of error (p = 0.001) showed that the larger the total volume of mammographic studies with preventive purposes in the subjects of the Russian Federation, the higher the proportion of cases of active detection of breast cancer, although this relationship is rather weak. Taking into account the obtained value of R2 (0.192), only 19.2% of the increase in the proportion of breast cancer cases detected actively can be explained by an increase in the volume of preventive mammograms. The correlation analysis showed that the volumes of preventive mammograms performed using mobile devices in the subjects of the Russian Federation were not associated with either the proportion of breast cancer cases detected actively or with the proportion of breast cancer cases detected in the early stages. At the same time, it is worth noting that the obtained value of p ≥ 0.5 indicates a statistically insignificant result of evaluating the relationship between variables, the interpretation of which should be performed with caution.Conclusion. Despite the high cost of mobile mammography units, the increase in their number in the subjects of the Russian Federation and the growth in their activities, the contribution of these diagnostic devices to solving the problem of early and active diagnosis of breast cancer remains uncertain. More promising are personalized approaches to the timely detection of cancers, including breast cancer, based on an assessment of the individual risk of their occurrence.

Intelligent Sentinel satellite image processing technology for land cover mapping

Purpose. This article proposes to develop an intelligent Sentinel satellite image processing technology for land cover mapping using convolutional neural networks. The result will be an image with improved spatial resolution. Methodology. The paper presents a technology using a combination of biquadratic interpolation, histogram alignment, PCA transform, as well as a parallel residual architecture of convolutional neural networks. The technology increases the information content of Sentinel-2 optical images by combining 10 and 20-meter resolution data, resulting in primary 20-meter images with improved spatial resolution. Findings. The root mean square error (RMSE = 3.64) indicates a high accuracy in reproducing the spectral properties of the images. The correlation coefficient (CC = 0.997) confirms a high linear relationship between the estimated and observed images. The low value of Spectral Angle Mapper (SAM = 0.52) with the high Universal Image Quality Index (UIQI = 0.999) indicates high quality and structural similarity between the synthesized and reference images. These results confirm the proposed technology’s effectiveness in enhancing the spatial resolution of Sentinel satellite images. Originality. Traditional pansharpening methods of multispectral images developed for satellite images with panchromatic channels cannot be directly applied to Sentinel multispectral data, because these images do not contain a panchromatic channel. In addition, atmospheric conditions and the presence of clouds affect the quality of optical images, complicating their further thematic processing. The proposed technology, using biquadratic interpolation, histogram alignment, convolutional neural networks, and PCA transformation, removes clouds and enhances the spatial resolution of the primary 20-meter optical satellite image channels of Sentinel-2. This technology reduces color distortion and increases the detail of digital optical images, which allows for more accurate analysis of the state of the earth’s surface. Practical value. The results obtained can be used to improve the methods for processing Sentinel satellite images, which provide high spatial resolution and accurate preservation of spectral characteristics. It provides the foundation for the development of new geographic information systems for land cover monitoring.

Analysis of Inter-Primer Binding Site Retrotransposon Length Polymorphism in Selected Group of Vaccinium corymbosum

Abstract The genetic variability in 13 selected genotypes of Vaccinium corymbosum using re-trotransposon-based iPBS markers system was characterized in this study. The four selected iPBS primers amplified 232 fragments, average 58 fragments per primer, approximately 53.16% of which were polymorphic. The polymorphism information content of iPBS marker was 0.238. The hierarchical cluster using unweighted pair group method with arithmetic mean analysis divided selected genotypes into two main groups and several subgroups. The values of the Jaccard coefficient of genetic relatedness were between 0.036–0.243. The similarity between two sample sets was 3.6%–24.3%. This study confirmed inter-PBS amplification technique as a reproducible and trustable marker system, but has proved to be an effective DNA fingerprinting technology to generate polymorphism in Vaccinium corymbosum.

Why Are Foodies Active on Social Network Services? An Exploratory Study on Foodies' Influence on Social Media.

As social media continues to gain traction in the food industry, this study explored how cooking and dining-out behaviors and social media use differ depending on the foodie tendencies of consumers, particularly Korean Millennial and Gen Z consumers. Additionally, this study considered the effect of foodie tendencies on active social media usage. Based on the Foodie Index, two groups were identified: high and low. The high group scored higher than the low group on foodie knowledge and spending, food interest, and time commitment. Comparison of the food, cooking, and dining-out behaviors of both groups revealed a significantly greater frequency of cooking and a higher proportion of using their own ingredients, even when dining alone, in the high group. Analysis of social media use characteristics also demonstrated significantly higher scores for the high group than the low group in terms of the degree of use, average daily usage time, checking frequency, and use of social media recommendations when cooking and purchasing food. Furthermore, foodie inclination significantly influenced active usage behavior, as users shared informative content and frequently posted articles and photos. This study found that foodies play a leading role in producing food-related information by actively using and sharing social media. Considering this ripple effect, consumers' foodie tendencies can be used as an important measure in food marketing-related research.

PhyKIT: A Multitool for Phylogenomics.

Multiple sequence alignments and phylogenetic trees are rich in biological information and are fundamental to research in biology. PhyKIT is a tool for processing and analyzing the information content of multiple sequence alignments and phylogenetic trees. Here, we describe how to use PhyKIT for diverse analyses, including (i) constructing a phylogenomic supermatrix, (ii) detecting errors in orthology inference, (iii) quantifying biases in phylogenomic data sets, (iv) identifying radiation events or lack of resolution using gene support frequencies, and (v) conducting evolution-based screens to facilitate gene function prediction. Several PhyKIT functions that streamline multiple sequence alignment and phylogenetic processing-such as renaming FASTA entries or tree tips-are also discussed. These protocols demonstrate how simple command-line operations in the unified framework of PhyKIT facilitate diverse phylogenomic data analysis and processing, from supermatrix construction and diagnosis to gaining clues about gene function. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Installing PhyKIT and syntax for usage Basic Protocol 2: Constructing a phylogenomic supermatrix Basic Protocol 3: Detecting anomalies in orthology relationships Basic Protocol 4: Quantifying biases in phylogenomic data matrices and related measures Basic Protocol 5: Identifying polytomies Basic Protocol 6: Assessing gene-gene coevolution as a genetic screen.

The Application of Information Theory to Interpret Shore Platform Erosion Rates

Advancements in information physics have recently introduced the application of information theory to investigate physical systems. The behaviour of erosion at the granular scale is to date still a complex system to unpack, and therefore geomorphology research requires novel approaches to better inform the interpretation of temporal and spatial erosion patterns at different scales. This paper applies information theory concepts to re-evaluate erosional data that were measured on limestone surfaces of two shore platforms in Malta with a traversing micro-erosion meter (TMEM). By representing erosion rates through their information content using a Box-Cox style transformation of the raw data (application of an inverse normal distribution function to fractionally ranked data), it is possible to identify points and measurement periods that contribute to a disproportionately large share of unexpected erosion rates that could provide more insight into the causes of erosion rates. Despite the variations in the information content from erosion rates at individual measurement points, most points consistently contribute to a similar amount of information. These findings illuminate the importance of considering the informational value of erosion data to further understand the underlying physical processes and potentially improve predictive models.

Neural encoding of melodic expectations in music across EEG frequency bands.

The human brain tracks regularities in the environment and extrapolates these to predict future events. Prior work on music cognition suggests that low-frequency (1-8Hz) brain activity encodes melodic predictions beyond the stimulus acoustics. Building on this work, we aimed to disentangle the frequency-specific neural dynamics linked to melodic prediction uncertainty (modelled as entropy) and prediction error (modelled as surprisal) for temporal (note onset) and content (note pitch) information. By using multivariate temporal response function (TRF) models, we re-analysed the electroencephalogram (EEG) from 20 subjects (10 musicians) who listened to Western tonal music. Our results show that melodic expectation metrics improve the EEG reconstruction accuracy in all frequency bands below the gamma range (< 30 Hz). Crucially, we found that entropy contributed more strongly to the reconstruction accuracy enhancement compared to surprisal in all frequency bands. Additionally, we found that the encoding of temporal, but not content, information metrics was not limited to low frequencies, rather it extended to higher frequencies (> 8Hz). An analysis of the TRF weights revealed that the temporal predictability of a note (entropy of note onset) may be encoded in the delta- (1-4Hz) and beta-band (12-30 Hz) brain activity prior to the stimulus, suggesting that these frequency bands associate with temporal predictions. Strikingly, we also revealed that melodic expectations selectively enhanced EEG reconstruction accuracy in the beta band for musicians, and in the alpha band (8-12 Hz) for non-musicians, suggesting that musical expertise influences the neural dynamics underlying predictive processing in music cognition.

Utilizing SSR-based core collection development to improve conservation and utilization of Corylus L. genetic resources.

Hazelnuts are traditional woody oilseed plants. Corylus L. resources are rich in variety and widely distributed in China. However, the identification of germplasm varieties and the selection of superior varieties remain quite limited. This study aimed to analyze the genetic diversity of 331 Corylus L. germplasms using 16 simple sequence repeat (SSR) markers. Based on this, 11 pairs of core primers were selected, a fingerprint database of germplasm resources was constructed, and a primary core collection was screened. The results indicated that these tested Corylus L. germplasms exhibited a high level of genetic diversity, with an average number of alleles (Na) per locus of 14.5 and a polymorphic information content of 0.777. The phylogenetic relationships among various hazelnut cultivars were characterized by complexity, and they were delineated into four distinct groups facilitated by genetic distance analyses. An SSR fingerprint database for 331 Corylus L. germplasms was successfully constructed using the 11 obtained core SSR markers to increase the discrimination efficiency. Ultimately, 127 primary core accessions of Corylus L. were selected. The retention rate for the observed Na and MAF (the minor allele frequency) in the primary core germplasm constructed based on a sampling proportion of 38.36% was 100% and 94.7%, respectively. Shannon's information index (I) was highly consistent between the core and original germplasms, indicating that the core germplasm could fully represent the genetic diversity of the original germplasm. Additionally, the principal coordinate analysis of the selected primary core germplasm was essentially consistent with that of the entire original germplasm, further supporting the broad representativeness of the core germplasm. This study provided a basis for precisely identifying and efficiently utilizing Corylus L. accession.

Development of Simple Sequence Repeat of Monochamus alternatus (Coleoptera: Cerambycidae) Based on Restriction Site-Associated DNA Sequencing

Monochamus alternatus, a pest posing a serious threat to coniferous species, such as Pinus massoniana, has had devastating effects on pine forests due to its association with Bursaphelenchus xylophilus. The creation of unique simple sequence repeat (SSR) primers for M. alternatus is crucial, as there has been little study of the species’ phylogeography. The aim of this study was to identify and create polymorphic SSR primers by sequencing samples of M. alternatus obtained from three different sampling points using the restriction site-associated DNA sequencing (Red-seq) approach. Subsequently, supplementary samples were integrated, and genetic typing was performed utilizing the identified polymorphic primers. Through comprehensive analysis, a total of 95,612 SSR loci were identified. Among these, mononucleotide repeats (51.43%), dinucleotide repeats (28.79%), and trinucleotide repeats (16.74%) predominated among the SSR motif types. Ultimately, 18 pairs of SSR primers were screened out, demonstrating stable amplification and high polymorphism. Genetic typing revealed that the mean number of alleles (Na) for these primer pairs ranged from 3 to 8, observed heterozygosity (Ho) ranged from 0.133 to 0.733, polymorphic information content (PIC) ranged from 0.294 and 0.783, and Shannon’s index (I) ranged from 0.590 to 1.802. This study effectively produced 16 pairs of SSR primers that can be applied to different populations of M. alternatus. As a result, important tools for furthering studies on the phylogeography of pine wood nematodes, creating genetic maps, gene mapping, and carrying out in-depth investigations into gene function have been made available.