MULTI-STATE MODELS WITH MISSING COVARIATES

Multi-state models have been widely used to analyze longitudinal event history data obtained in medical studies. The tools and methods developed recently in this area require the complete observed datasets. While, in many applications measurements on certain components of the covariate vector are missing on some study subjects. In this dissertation, several likelihood-based methodologies were proposed to deal with datasets with different types of missing covariates efficiently when applying multi-state models. Firstly, a maximum observed data likelihood method was proposed when the data has a univariate missing pattern and the missing covariate is a categorical variable. The construction of the observed data likelihood function is based on the model of a joint distribution of the response longitudinal event history data and the discrete covariate with missing values. Secondly, we proposed a maximum simulated likelihood method to deal with the missing continuous covariate when applying multi-state models. The observed data likelihood function was approximated by using the Monte Carlo simulation method. At last, an EM algorithm was used to deal with multiple missing covariates when estimating the parameters of multi-state model. The EM algorithm would be able to handle multiple missing discrete covariates in general missing pattern efficiently. All the proposed methods are justified by simulation studies and applications to the datasets from the SMART project, a consortium of 11 different high-quality longitudinal studies of aging and cognition

Selection of antisense oligonucleotides based on multiple predicted target mRNA structures

BACKGROUND: Local structures of target mRNAs play a significant role in determining the efficacies of antisense oligonucleotides (ODNs), but some structure-based target site selection methods are limited by uncertainties in RNA secondary structure prediction. If all the predicted structures of a given mRNA within a certain energy limit could be used simultaneously, target site selection would obviously be improved in both reliability and efficiency. In this study, some key problems in ODN target selection on the basis of multiple predicted target mRNA structures are systematically discussed. RESULTS: Two methods were considered for merging topologically different RNA structures into integrated representations. Several parameters were derived to characterize local target site structures. Statistical analysis on a dataset with 448 ODNs against 28 different mRNAs revealed 9 features quantitatively associated with efficacy. Features of structural consistency seemed to be more highly correlated with efficacy than indices of the proportion of bases in single-stranded or double-stranded regions. The local structures of the target site 5' and 3' termini were also shown to be important in target selection. Neural network efficacy predictors using these features, defined on integrated structures as inputs, performed well in "minus-one-gene" cross-validation experiments. CONCLUSION: Topologically different target mRNA structures can be merged into integrated representations and then used in computer-aided ODN design. The results of this paper imply that some features characterizing multiple predicted target site structures can be used to predict ODN efficacy

Temporal Features of Psychological and Physical Self-Representation: An ERP Study

Psychological and physical-self are two important aspects of self-concept. Although a growing number of behavioral and neuroimaging studies have investigated the cognitive mechanism and neural substrate underlying psychological and physical-self-representation, most of the existing research on psychological and physical-self-representation had been done in isolation. The present study aims to examine the electrophysiological responses to both psychological (one’s own name) and physical (one’s own voice) self-related stimuli in a uniform paradigm. Event-related potentials (ERPs) were recorded for subjects’ own and others’ names uttered by subjects’ own or others’ voice (own voice-own name, own voice-other’s name, other’s voice-own name, other’s voice-other’s name) while subjects performed an auditory passive oddball task. The results showed that one’s own name elicited smaller P2 and larger P3 amplitudes than those of other’s names, irrespective of the voice identity. However, no differences were observed between self and other’s voice during the P2 and P3 stages. Moreover, an obvious interaction effect was observed between voice content and voice identity at the N400 stage that the subject’s own voice elicited a larger parietal N400 amplitude than other’s voice in other name condition but not in own name condition. Taken together, these findings suggested that psychological (one’s own name) and physical (one’s own voice) self-representation induced distinct electrophysiological response patterns in auditory-cognitive processing

Scattered differentiation of unlinked loci across the genome underlines ecological divergence of the selfing grass Brachypodium stacei

Ecological divergence without geographic isolation, as an early speciation process that may lead finally to reproductive isolation through natural selection, remains a captivating topic in evolutionary biology. However, the pattern of genetic divergence underlying this process across the genome may vary between species and mating systems. Here, we present evidence that Brachypodium stacei, an annual and highly selfing grass model species, has undergone sympatric ecological divergence without geographic isolation. Genomic, transcriptomic, and metabolomic analyses together with lab experiments mimicking the two opposite environmental conditions suggest that diploid B. stacei populations have diverged sympatrically in two slopes characterized by distinct biomes at Evolution Canyon I (ECI), Mount Carmel, Israel. Despite ongoing gene flow, primarily facilitated by seed dispersal, the level of gene flow has progressively decreased over time. This local adaptation involves the scattered divergence of many unlinked loci across the total genome that include both coding genes and noncoding regions. Additionally, we have identified significant differential expressions of genes related to the ABA signaling pathway and contrasting metabolome composition between the arid- vs. forest-adapted B. stacei populations in ECI. These results suggest that multiple small loci involved in environmental responses act additively to account for ecological adaptations by this selfing species in contrasting environments

Multi-modal sarcasm detection via cross-modal graph convolutional network

With the increasing popularity of posting multimodal messages online, many recent studies have been carried out utilizing both textual and visual information for multi-modal sarcasm detection. In this paper, we investigate multimodal sarcasm detection from a novel perspective by constructing a cross-modal graph for each instance to explicitly draw the ironic relations between textual and visual modalities. Specifically, we first detect the objects paired with descriptions of the image modality, enabling the learning of important visual information. Then, the descriptions of the objects are served as a bridge to determine the importance of the association between the objects of image modality and the contextual words of text modality, so as to build a cross-modal graph for each multi-modal instance. Furthermore, we devise a cross-modal graph convolutional network to make sense of the incongruity relations between modalities for multi-modal sarcasm detection. Extensive experimental results and in-depth analysis show that our model achieves state-of-the-art performance in multi-modal sarcasm detection

Identification of closely related species in Aspergillus through Analysis of Whole-Genome

The challenge of discriminating closely related species persists, notably within clinical diagnostic laboratories for invasive aspergillosis (IA)-related species and food contamination microorganisms with toxin-producing potential. We employed Analysis of the whole-GEnome (AGE) to address the challenges of closely related species within the genus Aspergillus and developed a rapid detection method. First, reliable whole genome data for 77 Aspergillus species were downloaded from the database, and through bioinformatic analysis, specific targets for each species were identified. Subsequently, sequencing was employed to validate these specific targets. Additionally, we developed an on-site detection method targeting a specific target using a genome editing system. Our results indicate that AGE has successfully achieved reliable identification of all IA-related species (Aspergillus fumigatus, Aspergillus niger, Aspergillus nidulans, Aspergillus flavus, and Aspergillus terreus) and three well-known species (A. flavus, Aspergillus parasiticus, and Aspergillus oryzae) within the Aspergillus section. Flavi and AGE have provided species-level-specific targets for 77 species within the genus Aspergillus. Based on these reference targets, the sequencing results targeting specific targets substantiate the efficacy of distinguishing the focal species from its closely related species. Notably, the amalgamation of room-temperature amplification and genome editing techniques demonstrates the capacity for rapid and accurate identification of genomic DNA samples at a concentration as low as 0.1 ng/μl within a concise 30-min timeframe. Importantly, this methodology circumvents the reliance on large specialized instrumentation by presenting a singular tube operational modality and allowing for visualized result assessment. These advancements aptly meet the exigencies of on-site detection requirements for the specified species, facilitating prompt diagnosis and food quality monitoring. Moreover, as an identification method based on species-specific genomic sequences, AGE shows promising potential as an effective tool for epidemiological research and species classification

Analysis of Whole-Genome facilitates rapid and precise identification of fungal species

Fungal identification is a cornerstone of fungal research, yet traditional molecular methods struggle with rapid and accurate onsite identification, especially for closely related species. To tackle this challenge, we introduce a universal identification method called Analysis of whole GEnome (AGE). AGE includes two key steps: bioinformatics analysis and experimental practice. Bioinformatics analysis screens candidate target sequences named Targets within the genome of the fungal species and determines specific Targets by comparing them with the genomes of other species. Then, experimental practice using sequencing or non-sequencing technologies would confirm the results of bioinformatics analysis. Accordingly, AGE obtained more than 1,000,000 qualified Targets for each of the 13 fungal species within the phyla Ascomycota and Basidiomycota. Next, the sequencing and genome editing system validated the ultra-specific performance of the specific Targets; especially noteworthy is the first-time demonstration of the identification potential of sequences from unannotated genomic regions. Furthermore, by combining rapid isothermal amplification and phosphorothioate-modified primers with the option of an instrument-free visual fluorescence method, AGE can achieve qualitative species identification within 30 min using a single-tube test. More importantly, AGE holds significant potential for identifying closely related species and differentiating traditional Chinese medicines from their adulterants, especially in the precise detection of contaminants. In summary, AGE opens the door for the development of whole-genome-based fungal species identification while also providing guidance for its application in plant and animal kingdoms

Aβ Vaccination in Combination with Behavioral Enrichment in Aged Beagles: Effects on Cognition, Aβ, and Microhemorrhages

Beta-amyloid (Aβ) immunotherapy is a promising intervention to slow Alzheimer’s disease (AD). Aging dogs naturally accumulate Aβ and show cognitive decline. An active vaccine against fibrillar Aβ 1–42 (VAC) in aged beagles resulted in maintenance but not improvement of cognition along with reduced brain Aβ. Behavioral enrichment (ENR) led to cognitive benefits but no reduction in Aβ. We hypothesized cognitive outcomes could be improved by combining VAC with ENR in aged dogs. Aged dogs (11–12 years) were placed into 4 groups: (1) control/control (C/C); (2) control/VAC (C/V); (3) ENR/control (E/C); (4) ENR and VAC (E/V) and treated for 20 months. VAC decreased brain Aβ, pyroglutamate Aβ, increased CSF Aβ42 and BDNF RNA levels but also increased microhemorrhages. ENR reduced brain Aβ and prevented microhemorrhages. The combination treatment resulted in a significant maintenance of learning over time, reduced Aβ and increased BDNF mRNA despite increased microhemorrhages, however there were no benefits to memory. These results suggest that the combination of immunotherapy with behavioral enrichment leads to cognitive maintenance associated with reduced neuropathology that may benefit people with AD