Untangling the Evolution of the Receptor-Binding Motif of SARS-CoV-2.

The spike protein determines the host-range specificity of coronaviruses. In particular, the Receptor-Binding Motif in the spike protein from SARS-CoV-2 contains the amino acids involved in molecular recognition of the host Angiotensin Converting Enzyme 2. Therefore, to understand how SARS-CoV-2 acquired its capacity to infect humans it is necessary to reconstruct the evolution of this important motif. Early during the pandemic, it was proposed that the SARS-CoV-2 Receptor-Binding Domain was acquired via recombination with a pangolin infecting coronavirus. This proposal was challenged by an alternative explanation that suggested that the Receptor-Binding Domain from SARS-CoV-2 did not originated via recombination with a coronavirus from a pangolin. Instead, this alternative hypothesis proposed that the Receptor-Binding Motif from the bat coronavirus RaTG13, was acquired via recombination with an unidentified coronavirus. And as a consequence of this event, the Receptor-Binding Domain from the pangolin coronavirus appeared as phylogenetically closer to SARS-CoV-2. Recently, the genomes from coronaviruses from Cambodia (bat_RShST182/200) and Laos (BANAL-20-52/103/247) which are closely related to SARS-CoV-2 were reported. However, no detailed analysis of the evolution of the Receptor-Binding Motif from these coronaviruses was reported. Here we revisit the evolution of the Receptor-Binding Domain and Motif in the light of the novel coronavirus genome sequences. Specifically, we wanted to test whether the above coronaviruses from Cambodia and Laos were the source of the Receptor-Binding Domain from RaTG13. We found that the Receptor-Binding Motif from these coronaviruses is phylogenetically closer to SARS-CoV-2 than to RaTG13. Therefore, the source of the Receptor-Binding Domain from RaTG13 is still unidentified. In accordance with previous studies, our results are consistent with the hypothesis that the Receptor-Binding Motif from SARS-CoV-2 evolved by vertical inheritance from a bat-infecting population of coronaviruses.

Genotyping Hepatitis B virus by Next-Generation Sequencing: Detection of Mixed Infections and Analysis of Sequence Conservation.

Our aim was to develop an accurate, highly sensitive method for HBV genotype determination and detection of genotype mixtures. We examined the preS and 5' end of the HBV X gene (5X) regions of the HBV genome using next-generation sequencing (NGS). The 1852 haplotypes obtained were subjected to genotyping via the Distance-Based discrimination method (DB Rule) using two sets of 95 reference sequences of genotypes A-H. In clinical samples from 125 patients, the main genotypes were A, D, F and H in Caucasian, B and C in Asian and A and E in Sub-Saharan patients. Genotype mixtures were identified in 28 (22.40%) cases, and potential intergenotypic recombination was observed in 29 (23.20%) cases. Furthermore, we evaluated sequence conservation among haplotypes classified into genotypes A, C, D, and E by computing the information content. The preS haplotypes exhibited limited shared conserved regions, whereas the 5X haplotypes revealed two groups of conserved regions across the genotypes assessed. In conclusion, we developed an NGS-based HBV genotyping method utilizing the DB Rule for genotype classification. We identified two regions conserved across different genotypes at 5X, offering promising targets for RNA interference-based antiviral therapies.

Social media heterogeneity and preventive behaviours during the COVID-19 outbreak: a survey on online shopping.

BACKGROUND: Residents' adoption of preventive behaviours proved beneficial in preventing the large-scale transmission of the virus during the early stages of the COVID-19 outbreak. It is critical to investigate how social media triggers residents' preventive behaviour decisions during the COVID-19 outbreak. METHODS: This paper selected online shopping as a specific preventive behaviour for empirical investigation. An online cross-sectional survey was conducted through the Sojump website from 1 to 15 March 2020, and a total of 1,289 valid questionnaires were collected from China. This paper uses multiple regression analysis to investigate the heterogeneous impacts of different information sources on residents' online shopping willingness and online shopping behaviour and the heterogeneous impacts of different information content in social media on the transformation of residents' online shopping willingness and online shopping behaviour. RESULTS: The findings indicate that both official-media and self-media positively promote residents' online shopping willingness and behaviour, with official-media having a stronger promotional effect than self-media. Furthermore, official-media and self-media can collaboratively promote residents' online shopping willingness and online shopping behaviour. The ease-of-use and usefulness of information significantly promoted the transformation of residents' online shopping willingness. CONCLUSIONS: This study analyses the heterogeneous impacts of social media on residents' preventive behaviours from the perspectives of information source differentiation and information content differentiation, which enriches related studies and provides feasible paths for promoting residents' preventive behaviours.

Integrated Discriminant Evaluation of Molecular Genetic Markers and Genetic Diversity Parameters of Endangered Balearic Dog Breeds.

The genetic diversity analysis of six dog breeds, including Ca de Bestiar (CB), Ca de Bou (CBOU), Podenco Ibicenco (PI), Ca Rater (CR), Ca Mè (CM), and Ca de Conills (CC), reveals insightful findings. CB showcases the highest mean number of alleles (6.17) and heterozygosity values, with significant deviations from Hardy-Weinberg equilibrium (HWE) observed in five markers, indicating high intra-racial genetic diversity (average observed heterozygosity (Ho) = 0.754, expected heterozygosity (He) = 0.761). In contrast, CBOU presents the lowest mean number of alleles (5.05) and heterozygosity values, coupled with moderate polymorphic information content (PIC) values and a moderate level of intra-racial genetic diversity (average Ho = 0.313, He = 0.394). PI demonstrates moderate genetic diversity with an average of 5.75 alleles and highly informative PIC values, while CR displays robust genetic diversity with an average of 6.61 alleles and deviations from equilibrium, indicating potential risks of inbreeding (average Ho = 0.563, He = 0.658). CM exhibits moderate genetic diversity and deviations from equilibrium, similar to CBOU, with an average of 6.5 alleles and moderate PIC values (average Ho = 0.598, He = 0.676). Conversely, CC shows a wider range of allelic diversity and deviations from equilibrium (average Ho = 0.611, He = 0.706), suggesting a more diverse genetic background. Inter-racial analysis underscores distinct genetic differentiation between breeds, emphasizing the importance of informed breeding decisions and proactive genetic management strategies to preserve diversity, promote breed health, and ensure long-term sustainability across all breeds studied.

COVID-19 information seeking and individuals' protective behaviors: examining the role of information sources and information content.

BACKGROUND: Seeking COVID-19 information promotes individuals to adopt preventive behaviors, including wearing a mask, social distancing, staying away from risky places, and washing hands. This study aims to investigate which information and sources individuals relied on in seeking COVID-19 information and further examine their roles in individuals' adoption of preventive behaviors. METHODS: Through a statistical analysis of 1027 valid responses from citizens in different Chinese cities in 2022 to the self-designed items in an online survey, this study identified individuals' preferred information sources and content on COVID-19. Regarding the information sources and content, the study used multiple regression analysis to examine their associations with individuals' preventive behaviors, and further applied fuzzy-set qualitative comparative analysis (fsQCA) to explore their configurations that increase the likelihood of individuals adopting preventive behaviors. RESULTS: Individuals preferred information about the newest prevention and control policies, precautions and treatment, and symptoms from the sources of workplace and community, social media, and social live streaming services. Additionally, individuals' preventive behaviors were positively related to the workplace and community (ß = 0.202, p <.001), social live streaming services (ß = 0.089, p <.01), government department websites (ß = 0.079, p <.05), television (ß = 0.073, p <.05), and online news media (ß = 0.069, p <.05), but were negatively associated with newspapers (ß=-0.087, p <.05). Regarding information content, precautions and treatments (ß = 0.211, p <.001), the newest prevention and control policies (ß = 0.173, p <.001), symptoms (ß = 0.152, p <.001), and official rumor-dispelling information (ß = 0.082, p <.05) had a positive relationship with individuals' preventive behaviors. In addition, fsQCA results presented eight configurations that promote individuals to adopt preventive behaviors. The total coverage and solution consistency values were 0.869 and 0.987, respectively. Furthermore, COVID-19 information content, the sources of social media and interpersonal sources, and official news media played an essential role in increasing the likelihood of individuals adopting preventive behaviors. CONCLUSIONS: Our findings demonstrated that individuals seek various COVID-19 information from multiple sources. The direct and degree of association of information sources and content with individuals' preventive behaviors vary from source to source and from content to content. Information sources and content could combinatorially promote individuals to adopt preventive behaviors through several configurations.

Genome assembly and microsatellite marker development using Illumina and PacBio sequencing in Persicaria maackiana (Polygonaceae) from Korea.

BACKGROUND: Persicaria maackiana (Regel) is a potential medicinal plant that exerts anti-diabetic effects. However, the lack of genomic information on P. maackiana hinders research at the molecular level. OBJECTIVE: Herein, we aimed to construct a draft genome assembly and obtain comprehensive genomic information on P. maackiana using high-throughput sequencing tools PacBio Sequel II and Illumina. METHODS: Persicaria maackiana samples from three natural populations in Gaecheon, Gichi, and Uiryeong reservoirs in South Korea were used to generate genomic DNA libraries, perform genome de novo assembly, gene ontology analysis, phylogenetic tree analysis, genotyping, and identify microsatellite markers. RESULTS: The assembled P. maackiana genome yielded 32,179 contigs. Assessment of assembly integrity revealed 1503 (93.12%) complete Benchmarking Universal Single-Copy Orthologs. A total of 64,712 protein-coding genes were predicted and annotated successfully in the protein database. In the Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologs, 13,778 genes were annotated into 18 categories. Genes that activated AMPK were identified in the KEGG pathway. A total of 316,992 microsatellite loci were identified, and primers targeting the flanking regions were developed for 292,059 microsatellite loci. Of these, 150 primer sets were randomly selected for amplification, and 30 of these primer sets were identified as polymorphic. These primers amplified 3-9 alleles. The mean observed and expected heterozygosity were 0.189 and 0.593, respectively. Polymorphism information content values of the markers were 0.361-0.754. CONCLUSION: Collectively, our study provides a valuable resource for future comparative genomics, phylogeny, and population studies of P. maackiana.

Information content of stepped wedge designs under the working independence assumption.

The stepped wedge design is increasingly popular in pragmatic trials and implementation science research studies for evaluating system-level interventions that are perceived to be beneficial to patient populations. An important step in planning a stepped wedge design is to understand the efficiency of the treatment effect estimator and hence the power of the study. We develop several novel analytical results for designing stepped wedge cluster randomized trials analyzed through generalized estimating equations under a misspecified working independence correlation structure. We first contribute a general variance expression of the treatment effect estimator when data collection is scheduled for each cluster-period. Because resource and patient-centered considerations may intentionally call for an incomplete design with outcome data being omitted for certain cluster-periods, we further derive the information content based on the robust sandwich variance to identify data elements that may be preferentially omitted with minimum loss of precision in estimating the treatment effect. We prove that centrosymmetric pairs of cluster-periods, treatment sequences and periods have identical information content and thus contribute equally to the treatment effect estimation, as long as the true covariance structure for the cluster-period means remains centrosymmetric. Finally, we provide an example of how to obtain an incomplete stepped wedge design that admits a more efficient independence GEE estimator but requires less data collection effort. Our results elegantly extend existing ones from linear mixed models coupled with model-based variances to accommodate a misspecified independence working correlation structure through the robust sandwich variances.

Harnessing genome-wide genetic diversity, population structure and linkage disequilibrium in Ethiopian durum wheat gene pool.

Yanyang Liu, Henan Academy of Agricultural Sciences (HNAAS), China; Landraces are an important genetic source for transferring valuable novel genes and alleles required to enhance genetic variation. Therefore, information on the gene pool's genetic diversity and population structure is essential for the conservation and sustainable use of durum wheat genetic resources. Hence, the aim of this study was to assess genetic diversity, population structure, and linkage disequilibrium, as well as to identify regions with selection signature. Five hundred (500) individuals representing 46 landraces, along with 28 cultivars were evaluated using the Illumina Infinium 25K wheat SNP array, resulting in 8,178 SNPs for further analysis. Gene diversity (GD) and the polymorphic information content (PIC) ranged from 0.13-0.50 and 0.12-0.38, with mean GD and PIC values of 0.34 and 0.27, respectively. Linkage disequilibrium (LD) revealed 353,600 pairs of significant SNPs at a cut-off (r2 > 0.20, P < 0.01), with an average r2 of 0.21 for marker pairs. The nucleotide diversity (π) and Tajima's D (TD) per chromosome for the populations ranged from 0.29-0.36 and 3.46-5.06, respectively, with genome level, mean π values of 0.33 and TD values of 4.43. Genomic scan using the Fst outlier test revealed 85 loci under selection signatures, with 65 loci under balancing selection and 17 under directional selection. Putative candidate genes co-localized with regions exhibiting strong selection signatures were associated with grain yield, plant height, host plant resistance to pathogens, heading date, grain quality, and phenolic content. The Bayesian Model (STRUCTURE) and distance-based (principal coordinate analysis, PCoA, and unweighted pair group method with arithmetic mean, UPGMA) methods grouped the genotypes into five subpopulations, where landraces from geographically non-adjoining environments were clustered in the same cluster. This research provides further insights into population structure and genetic relationships in a diverse set of durum wheat germplasm, which could be further used in wheat breeding programs to address production challenges sustainably.

Revisiting neural information, computing and linking capacity.

Neural information theory represents a fundamental method to model dynamic relations in biological systems. However, the notion of information, its representation, its content and how it is processed are the subject of fierce debates. Since the limiting capacity of neuronal links strongly depends on how neurons are hypothesized to work, their operating modes are revisited by analyzing the differences between the results of the communication models published during the past seven decades and those of the recently developed generalization of the classical information theory. It is pointed out that the operating mode of neurons is in resemblance with an appropriate combination of the formerly hypothesized analog and digital working modes; furthermore that not only the notion of neural information and its processing must be reinterpreted. Given that the transmission channel is passive in Shannon's model, the active role of the transfer channels (the axons) may introduce further transmission limits in addition to the limits concluded from the information theory. The time-aware operating model enables us to explain why (depending on the researcher's point of view) the operation can be considered either purely analog or purely digital.

Quantifying evoked responses through information-theoretical measures.

Information theory is a viable candidate to advance our understanding of how the brain processes information generated in the internal or external environment. With its universal applicability, information theory enables the analysis of complex data sets, is free of requirements about the data structure, and can help infer the underlying brain mechanisms. Information-theoretical metrics such as Entropy or Mutual Information have been highly beneficial for analyzing neurophysiological recordings. However, a direct comparison of the performance of these methods with well-established metrics, such as the t-test, is rare. Here, such a comparison is carried out by evaluating the novel method of Encoded Information with Mutual Information, Gaussian Copula Mutual Information, Neural Frequency Tagging, and t-test. We do so by applying each method to event-related potentials and event-related activity in different frequency bands originating from intracranial electroencephalography recordings of humans and marmoset monkeys. Encoded Information is a novel procedure that assesses the similarity of brain responses across experimental conditions by compressing the respective signals. Such an information-based encoding is attractive whenever one is interested in detecting where in the brain condition effects are present.

PPIGCF: A Protein-Protein Interaction-Based Gene Correlation Filter for Optimal Gene Selection.

Biological data at the omics level are highly complex, requiring powerful computational approaches to identifying significant intrinsic characteristics to further search for informative markers involved in the studied phenotype. In this paper, we propose a novel dimension reduction technique, protein-protein interaction-based gene correlation filtration (PPIGCF), which builds on gene ontology (GO) and protein-protein interaction (PPI) structures to analyze microarray gene expression data. PPIGCF first extracts the gene symbols with their expression from the experimental dataset, and then, classifies them based on GO biological process (BP) and cellular component (CC) annotations. Every classification group inherits all the information on its CCs, corresponding to the BPs, to establish a PPI network. Then, the gene correlation filter (regarding gene rank and the proposed correlation coefficient) is computed on every network and eradicates a few weakly correlated genes connected with their corresponding networks. PPIGCF finds the information content (IC) of the other genes related to the PPI network and takes only the genes with the highest IC values. The satisfactory results of PPIGCF are used to prioritize significant genes. We performed a comparison with current methods to demonstrate our technique's efficiency. From the experiment, it can be concluded that PPIGCF needs fewer genes to reach reasonable accuracy (~99%) for cancer classification. This paper reduces the computational complexity and enhances the time complexity of biomarker discovery from datasets.

Potential status of A1 and A2 variants of bovine beta-casein gene in milk samples of Indian cattle breeds.

This study aimed to determine the polymorphism in 7th exon of beta-casein gene (CSN2) gene in seven domestic (Kosali, Tharparkar, Gangatiri, Sahiwal, Gir, Khariar, Motu) and two exotic cattle breeds (Jersey and Holstein-Friesian). Genomic DNA was extracted from 1000 milk samples, and the C > A polymorphism in CSN2 was determined using the tetra-primer amplification refractory mutation system-polymerase chain reaction method. In all Indigenous cattle breeds, the mean frequency of A1A2 and A2A2 genotypes was 0.19 and 0.80, respectively. The A1A1 genotype was absent in all seven domestic cattle breeds. The frequency of the A2A2 genotype was highest in the Gir breed (0.93). However, the Sahiwal, Tharparkar, and Motu breeds also had a higher frequency of A2A2 genotype compared to other breeds. In contrast, Gangatiri breed of India showed lowest frequency of A2A2 genotype. The mean A1 and A2 allele frequency was 0.09 and 0.91, respectively. In exotic breeds, the mean frequencies of the A1A1, A1A2, and A2A2 genotypes were 0.42, 0.55, and 0.03, respectively. Similarly, the mean A1 and A2 allele frequency was 0.69 and 0.31, respectively. This study suggests the high potential of Gir, Sahiwal, Tharparkar, and Motu cattle for A2 milk production since they carry a favorable A2 genotype.

Informativeness across Interpreting Types: Implications for Language Shifts under Cognitive Load.

Previous quantitative studies discussing interpreting types have focused on various features of linguistic forms in outputs. However, none of them has examined their informativeness. Entropy, as a measure of the average information content and the uniformity of the probability distribution of language units, has been applied to quantitative linguistic research on different types of language texts. In the present study, entropy and repeat rate were used to investigate the difference of overall informativeness and concentration of output texts between simultaneous interpreting and consecutive interpreting. We intend to figure out the frequency distribution patterns of word and word category in two types of interpreting texts. Analyses of linear mixed-effects models showed that entropy and repeat rate can distinguish the informativeness of consecutive and simultaneous interpreting outputs, and consecutive interpreting outputs entail a higher word entropy value and a lower word repeat rate than simultaneous interpreting outputs. We propose that consecutive interpreting is a cognitive process which reaches an equilibrium between production economy for interpreters and comprehension sufficiency for listeners, especially in the case where input speeches are more complex. Our findings also shed lights on the selection of interpreting types in application scenarios. The current research is the first of its kind in examining informativeness across interpreting types, demonstrating a dynamic adaptation of language users to extreme cognitive load.

Development of Diagnostic Markers and Applied for Genetic Diversity Study and Population Structure of Bipolaris sorokiniana Associated with Leaf Blight Complex of Wheat.

Bipolaris sorokiniana, a key pathogenic fungus in the wheat leaf blight complex, was the subject of research that resulted in the development of fifty-five polymorphic microsatellite markers. These markers were then used to examine genetic diversity and population structure in Indian geographical regions. The simple sequence repeat (SSR) like trinucleotides, dinucleotides, and tetranucleotides accounted for 43.37% (1256), 23.86% (691), and 16.54% (479) of the 2896 microsatellite repeats, respectively. There were 109 alleles produced by these loci overall, averaging 2.36 alleles per microsatellite marker. The average polymorphism information content value was 0.3451, with values ranging from 0.1319 to 0.5932. The loci's Shannon diversity varied from 0.2712 to 1.2415. These 36 isolates were divided into two main groups using population structure analysis and unweighted neighbour joining. The groupings were not based on where the isolates came from geographically. Only 7% of the overall variation was found to be between populations, according to an analysis of molecular variance. The high amount of gene flow estimate (NM = 3.261 per generation) among populations demonstrated low genetic differentiation in the entire populations (FST = 0.071). The findings indicate that genetic diversity is often minimal. In order to examine the genetic diversity and population structure of the B. sorokiniana populations, the recently produced microsatellite markers will be helpful. This study's findings may serve as a foundation for developing improved management plans for the leaf blight complex and spot blotch of wheat diseases in India.

Assessment of genetic diversity and population structure in wild Ziziphus species from northwest India using SSR marker technique.

BACKGROUND: Ziziphus species particularly Ziziphus mauritiana and Ziziphus nummularia constitute an important part of genetic resources in India. They contribute economically as a fruit crop with lots of morphological and pomological variability. In current study, 48 accessions belonging to two wild Ziziphus species, i.e., Z. mauritiana and Z. nummularia, were characterized using SSR markers. In addition, external features were also examined using stereomicroscope. RESULTS: Present investigation was done to explore the genetic structure of North Indian jujube. In total, 23 SSR markers detected 57 SSR alleles with an average of 2.47 alleles. Highest number of alleles (4) were detected by three primers, namely BFU1178, BFU479, and ZCMS14, while lowest number of alleles (2) were detected by fifteen primers. Highest Polymorphism Information Content (PIC) was 0.500 and shown by two primers, namely BFU528 and BFU1248, while lowest PIC (0.041) was observed in primers BFU286 with mean value of 0.443. Similarly, highest value of marker index (MI) was detected by primer BFU1178 i.e. 1.969, and lowest value of marker index was observed in primer BFU286 i.e. 0.021. Dendrogram generated using SSR markers data and principal component analysis showed two major groups of the analyzed germplasm with intermixing. STRUCTURE analysis also clustered all the accessions into two groups. We did not found correlation between geographic and genetic distances. CONCLUSIONS: The preliminary results suggest that there is high level of gene pool mixing in these species which can be attributed to their cross-pollination habit. However, more such studies with large numbers of samples are required in future to gain concrete insights of the genetic structure in these species.

Decoding the correlation between heart activation and walking path by information-based analysis.

BACKGROND: One of the important areas of heart research is to analyze heart rate variability during (HRV) walking. OBJECTIVE: In this research, we investigated the correction between heart activation and the variations of walking paths. METHOD: We employed Shannon entropy to analyze how the information content of walking paths affects the information content of HRV. Eight healthy students walked on three designed walking paths with different information contents while we recorded their ECG signals. We computed and analyzed the Shannon entropy of the R-R interval time series (as an indicator of HRV) versus the Shannon entropy of different walking paths and accordingly evaluated their relation. RESULTS: According to the obtained results, walking on the path that contains more information leads to less information in the R-R time series. CONCLUSION: The analysis method employed in this research can be extended to analyze the relation between other physiological signals (such as brain or muscle reactions) and the walking path.

Influence of cluster-period cells in stepped wedge cluster randomized trials.

Stepped wedge cluster randomized trials (SWCRT) are increasingly used for the evaluation of complex interventions in health services research. They randomly allocate treatments to clusters that switch to intervention under investigation at variable time points without returning to control condition. The resulting unbalanced allocation over time periods and the uncertainty about the underlying correlation structures at cluster-level renders designing and analyzing SWCRTs a challenge. Adjusting for time trends is recommended, appropriate parameterizations depend on the particular context. For sample size calculation, the covariance structure and covariance parameters are usually assumed to be known. These assumptions greatly affect the influence single cluster-period cells have on the effect estimate. Thus, it is important to understand how cluster-period cells contribute to the treatment effect estimate. We therefore discuss two measures of cell influence. These are functions of the design characteristics and covariance structure only and can thus be calculated at the planning stage: the coefficient matrix as discussed by Matthews and Forbes and information content (IC) as introduced by Kasza and Forbes. The main result is a new formula for IC that is more general and computationally more efficient. The formula applies to any generalized least squares estimator, especially for any type of time trend adjustment or nonblock diagonal matrices. We further show a functional relationship between IC and the coefficient matrix. We give two examples that tie in with current literature. All discussed tools and methods are implemented in the R package SteppedPower.

A Decision Probability Transformation Method Based on the Neural Network.

When the Dempster-Shafer evidence theory is applied to the field of information fusion, how to reasonably transform the basic probability assignment (BPA) into probability to improve decision-making efficiency has been a key challenge. To address this challenge, this paper proposes an efficient probability transformation method based on neural network to achieve the transformation from the BPA to the probabilistic decision. First, a neural network is constructed based on the BPA of propositions in the mass function. Next, the average information content and the interval information content are used to quantify the information contained in each proposition subset and combined to construct the weighting function with parameter r. Then, the BPA of the input layer and the bias units are allocated to the proposition subset in each hidden layer according to the weight factors until the probability of each single-element proposition with the variable is output. Finally, the parameter r and the optimal transform results are obtained under the premise of maximizing the probabilistic information content. The proposed method satisfies the consistency of the upper and lower boundaries of each proposition. Extensive examples and a practical application show that, compared with the other methods, the proposed method not only has higher applicability, but also has lower uncertainty regarding the transformation result information.

Development of SSR Markers Linked to Stress Responsive Genes along Tomato Chromosome 3 (Solanum lycopersicum L.).

This study aimed to develop novel SSR markers in tomato. Several BAC clones along chromosome 3 in tomato were selected based on their content. The criteria was the availability of genes, either directly or indirectly related to stress response (drought, salinity, and heat) in tomato. A total of 20 novel in silico SSR markers were developed and 96 important nearby genes were identified. The identified nearby genes represent different tomato genes involved in plant growth and development and biotic and abiotic stress tolerance. The developed SSR markers were assessed using tomato landraces. A total of 29 determinate and semi-determinate local tomato landraces collected from diverse environments were utilized. A total of 33 alleles with mean of 1.65 alleles per locus were scored, showing 100% polymorphic patterns, with a mean of 0.18 polymorphism information content (PIC) values. The mean of observed and expected heterozygosity were 0.19 and 0.24, respectively. The mean value of the Jaccard similarity index was used for clustering the landraces. The developed microsatellite markers showed potential to assess genetic variability among tomato landraces. The genetic distance information reported in this study can be used by breeders in future genetic improvement of tomato for tolerance against diverse stresses.

Towards Generalizing the Information Theory for Neural Communication.

Neuroscience extensively uses the information theory to describe neural communication, among others, to calculate the amount of information transferred in neural communication and to attempt the cracking of its coding. There are fierce debates on how information is represented in the brain and during transmission inside the brain. The neural information theory attempts to use the assumptions of electronic communication; despite the experimental evidence that the neural spikes carry information on non-discrete states, they have shallow communication speed, and the spikes' timing precision matters. Furthermore, in biology, the communication channel is active, which enforces an additional power bandwidth limitation to the neural information transfer. The paper revises the notions needed to describe information transfer in technical and biological communication systems. It argues that biology uses Shannon's idea outside of its range of validity and introduces an adequate interpretation of information. In addition, the presented time-aware approach to the information theory reveals pieces of evidence for the role of processes (as opposed to states) in neural operations. The generalized information theory describes both kinds of communication, and the classic theory is the particular case of the generalized theory.