Vocal expression of emotions discriminates dementia with Lewy bodies from Alzheimer's disease.

Dementia with Lewy bodies (DLB) and Alzheimer's disease (AD), the two most common neurodegenerative dementias, both exhibit altered emotional processing. However, how vocal emotional expressions alter in and differ between DLB and AD remains uninvestigated. We collected voice data during story reading from 152 older adults comprising DLB, AD, and cognitively unimpaired (CU) groups and compared their emotional prosody in terms of valence and arousal dimensions. Compared with matched AD and CU participants, DLB patients showed reduced overall emotional expressiveness, as well as lower valence (more negative) and lower arousal (calmer), the extent of which was associated with cognitive impairment and insular atrophy. Classification models using vocal features discriminated DLB from AD and CU with an AUC of 0.83 and 0.78, respectively. Our findings may aid in discriminating DLB patients from AD and CU individuals, serving as a surrogate marker for clinical and neuropathological changes in DLB. Highlights: DLB showed distinctive reduction in vocal expression of emotions.Cognitive impairment was associated with reduced vocal emotional expression in DLB.Insular atrophy was associated with reduced vocal emotional expression in DLB.Emotional expression measures successfully differentiated DLB from AD or controls.

Distinct eye movement patterns to complex scenes in Alzheimer's disease and Lewy body disease.

Background: Alzheimer's disease (AD) and Lewy body disease (LBD), the two most common causes of neurodegenerative dementia with similar clinical manifestations, both show impaired visual attention and altered eye movements. However, prior studies have used structured tasks or restricted stimuli, limiting the insights into how eye movements alter and differ between AD and LBD in daily life. Objective: We aimed to comprehensively characterize eye movements of AD and LBD patients on naturalistic complex scenes with broad categories of objects, which would provide a context closer to real-world free viewing, and to identify disease-specific patterns of altered eye movements. Methods: We collected spontaneous viewing behaviors to 200 naturalistic complex scenes from patients with AD or LBD at the prodromal or dementia stage, as well as matched control participants. We then investigated eye movement patterns using a computational visual attention model with high-level image features of object properties and semantic information. Results: Compared with matched controls, we identified two disease-specific altered patterns of eye movements: diminished visual exploration, which differentially correlates with cognitive impairment in AD and with motor impairment in LBD; and reduced gaze allocation to objects, attributed to a weaker attention bias toward high-level image features in AD and attributed to a greater image-center bias in LBD. Conclusion: Our findings may help differentiate AD and LBD patients and comprehend their real-world visual behaviors to mitigate the widespread impact of impaired visual attention on daily activities.

STOP1-regulated SMALL AUXIN UP RNA55 (SAUR55) is involved in proton/malate co-secretion for Al tolerance in Arabidopsis.

Proton (H+) release is linked to aluminum (Al)-enhanced organic acids (OAs) excretion from the roots under Al rhizotoxicity in plants. It is well-reported that the Al-enhanced organic acid excretion mechanism is regulated by SENSITIVE TO PROTON RHIZOTOXICITY1 (STOP1), a zinc-finger TF that regulates major Al tolerance genes. However, the mechanism of H+ release linked to OAs excretion under Al stress has not been fully elucidated. Recent physiological and molecular-genetic studies have implicated the involvement of SMALL AUXIN UP RNAs (SAURs) in the activation of plasma membrane H+-ATPases for stress responses in plants. We hypothesized that STOP1 is involved in the regulation of Al-responsive SAURs, which may contribute to the co-secretion of protons and malate under Al stress conditions. In our transcriptome analysis of the roots of the stop1 (sensitive to proton rhizotoxicity1) mutant, we found that STOP1 regulates the transcription of one of the SAURs, namely SAUR55. Furthermore, we observed that the expression of SAUR55 was induced by Al and repressed in the STOP1 T-DNA insertion knockout (KO) mutant (STOP1-KO). Through in silico analysis, we identified a functional STOP1-binding site in the promoter of SAUR55. Subsequent in vitro and in vivo studies confirmed that STOP1 directly binds to the promoter of SAUR55. This suggests that STOP1 directly regulates the expression of SAUR55 under Al stress. We next examined proton release in the rhizosphere and malate excretion in the T-DNA insertion KO mutant of SAUR55 (saur55), in conjunction with STOP1-KO. Both saur55 and STOP1-KO suppressed rhizosphere acidification and malate release under Al stress. Additionally, the root growth of saur55 was sensitive to Al-containing media. In contrast, the overexpressed line of SAUR55 enhanced rhizosphere acidification and malate release, leading to increased Al tolerance. These associations with Al tolerance were also observed in natural variations of Arabidopsis. These findings demonstrate that transcriptional regulation of SAUR55 by STOP1 positively regulates H+ excretion via PM H+-ATPase 2 which enhances Al tolerance by malate secretion from the roots of Arabidopsis. The activation of PM H+-ATPase 2 by SAUR55 was suggested to be due to PP2C.D2/D5 inhibition by interaction on the plasma membrane with its phosphatase. Furthermore, RNAi-suppression of NtSTOP1 in tobacco shows suppression of rhizosphere acidification under Al stress, which was associated with the suppression of SAUR55 orthologs, which are inducible by Al in tobacco. It suggests that transcriptional regulation of Al-inducible SAURs by STOP1 plays a critical role in OAs excretion in several plant species as an Al tolerance mechanism.

LHT1/MAC7 contributes to proper alternative splicing under long-term heat stress and mediates variation in the heat tolerance of Arabidopsis.

Natural genetic variation has facilitated the identification of genes underlying complex traits such as stress tolerances. We here evaluated the long-term (L-) heat tolerance (37°C for 5 days) of 174 Arabidopsis thaliana accessions and short-term (S-) heat tolerance (42°C, 50 min) of 88 accessions and found extensive variation, respectively. Interestingly, L-heat-tolerant accessions are not necessarily S-heat tolerant, suggesting that the tolerance mechanisms are different. To elucidate the mechanisms underlying the variation, we performed a chromosomal mapping using the F2 progeny of a cross between Ms-0 (a hypersensitive accession) and Col-0 (a tolerant accession) and found a single locus responsible for the difference in L-heat tolerance between them, which we named Long-term Heat Tolerance 1 (LHT1). LHT1 is identical to MAC7, which encodes a putative RNA helicase involved in mRNA splicing as a component of the MOS4 complex. We found one amino acid deletion in LHT1 of Ms-0 that causes a loss of function. Arabidopsis mutants of other core components of the MOS4 complex-mos4-2, cdc5-1, mac3a mac3b, and prl1 prl2-also showed hypersensitivity to L-heat stress, suggesting that the MOS4 complex plays an important role in L-heat stress responses. L-heat stress induced mRNA processing-related genes and compromised alternative splicing. Loss of LHT1 function caused genome-wide detrimental splicing events, which are thought to produce nonfunctional mRNAs that include retained introns under L-heat stress. These findings suggest that maintaining proper alternative splicing under L-heat stress is important in the heat tolerance of A. thaliana.

Screening of Mild Cognitive Impairment Through Conversations With Humanoid Robots: Exploratory Pilot Study.

BACKGROUND: The rising number of patients with dementia has become a serious social problem worldwide. To help detect dementia at an early stage, many studies have been conducted to detect signs of cognitive decline by prosodic and acoustic features. However, many of these methods are not suitable for everyday use as they focus on cognitive function or conversational speech during the examinations. In contrast, conversational humanoid robots are expected to be used in the care of older people to help reduce the work of care and monitoring through interaction. OBJECTIVE: This study focuses on early detection of mild cognitive impairment (MCI) through conversations between patients and humanoid robots without a specific examination, such as neuropsychological examination. METHODS: This was an exploratory study involving patients with MCI and cognitively normal (CN) older people. We collected the conversation data during neuropsychological examination (Mini-Mental State Examination [MMSE]) and everyday conversation between a humanoid robot and 94 participants (n=47, 50%, patients with MCI and n=47, 50%, CN older people). We extracted 17 types of prosodic and acoustic features, such as the duration of response time and jitter, from these conversations. We conducted a statistical significance test for each feature to clarify the speech features that are useful when classifying people into CN people and patients with MCI. Furthermore, we conducted an automatic classification experiment using a support vector machine (SVM) to verify whether it is possible to automatically classify these 2 groups by the features identified in the statistical significance test. RESULTS: We obtained significant differences in 5 (29%) of 17 types of features obtained from the MMSE conversational speech. The duration of response time, the duration of silent periods, and the proportion of silent periods showed a significant difference (P<.001) and met the reference value r=0.1 (small) of the effect size. Additionally, filler periods (P<.01) and the proportion of fillers (P=.02) showed a significant difference; however, these did not meet the reference value of the effect size. In contrast, we obtained significant differences in 16 (94%) of 17 types of features obtained from the everyday conversations with the humanoid robot. The duration of response time, the duration of speech periods, jitter (local, relative average perturbation [rap], 5-point period perturbation quotient [ppq5], difference of difference of periods [ddp]), shimmer (local, amplitude perturbation quotient [apq]3, apq5, apq11, average absolute differences between the amplitudes of consecutive periods [dda]), and F0cov (coefficient of variation of the fundamental frequency) showed a significant difference (P<.001). In addition, the duration of response time, the duration of silent periods, the filler period, and the proportion of fillers showed significant differences (P<.05). However, only jitter (local) met the reference value r=0.1 (small) of the effect size. In the automatic classification experiment for the classification of participants into CN and MCI groups, the results showed 66.0% accuracy in the MMSE conversational speech and 68.1% accuracy in everyday conversations with the humanoid robot. CONCLUSIONS: This study shows the possibility of early and simple screening for patients with MCI using prosodic and acoustic features from everyday conversations with a humanoid robot with the same level of accuracy as the MMSE.

Structural Cerebral Network Differences in Prodromal Alzheimer's Disease and Prodromal Dementia with Lewy Bodies.

INTRODUCTION: Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) have long prodromal phases without dementia. However, the patterns of cerebral network alteration in this early stage of the disease remain to be clarified. METHOD: Participants were 48 patients with mild cognitive impairment (MCI) due to AD (MCI-AD), 18 patients with MCI with DLB (MCI with Lewy bodies: MCI-LB), and 23 healthy controls who underwent a 1.5-Tesla magnetic resonance imaging scan. Cerebral networks were extracted from individual T1-weighted images based on the intracortical similarity, and we estimated the differences of network metrics among the three diagnostic groups. RESULTS: Whole-brain analyses for degree, betweenness centrality, and clustering coefficient images were performed using SPM8 software. The patients with MCI-LB showed significant reduction of degree in right putamen, compared with healthy subjects. The MCI-AD patients showed significant lower degree in left insula and bilateral posterior cingulate cortices compared with healthy subjects. There were no significant differences in small-world properties and in regional gray matter volume among the three groups. CONCLUSIONS: We found the change of degree in the patients with MCI-AD and with MCI-LB, compared with healthy controls. These findings were consistent with the past single-photon emission computed tomography studies focusing on AD and DLB. The disease-related difference in the cerebral neural network might provide an adjunct biomarker for the early detection of AD and DLB.

Characteristics of Drawing Process Differentiate Alzheimer's Disease and Dementia with Lewy Bodies.

BACKGROUND: Early differential diagnosis of Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) is important for treatment and disease management, but it remains challenging. Although computer-based drawing analysis may help differentiate AD and DLB, it has not been studied. OBJECTIVE: We aimed to identify the differences in features characterizing the drawing process between AD, DLB, and cognitively normal (CN) individuals, and to evaluate the validity of using these features to identify and differentiate AD and DLB. METHODS: We collected drawing data with a digitizing tablet and pen from 123 community-dwelling older adults in three clinical diagnostic groups of mild cognitive impairment or dementia due to AD (n = 47) or Lewy body disease (LBD; n = 27), and CN (n = 49), matched for their age, sex, and years of education. We then investigated drawing features in terms of the drawing speed, pressure, and pauses. RESULTS: Reduced speed and reduced smoothness in speed and pressure were observed particularly in the LBD group, while increased pauses and total durations were observed in both the AD and LBD groups. Machine-learning models using these features achieved an area under the receiver operating characteristic curve (AUC) of 0.80 for AD versus CN, 0.88 for LBD versus CN, and 0.77 for AD versus LBD. CONCLUSION: Our results indicate how different types of drawing features were particularly discriminative between the diagnostic groups, and how the combination of these features can facilitate the identification and differentiation of AD and DLB.

Automated Early Detection of Alzheimer's Disease by Capturing Impairments in Multiple Cognitive Domains with Multiple Drawing Tasks.

BACKGROUND: Automatic analysis of the drawing process using a digital tablet and pen has been applied to successfully detect Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, most studies focused on analyzing individual drawing tasks separately, and the question of how a combination of drawing tasks could improve the detection performance thus remains unexplored. OBJECTIVE: We aimed to investigate whether analysis of the drawing process in multiple drawing tasks could capture different, complementary aspects of cognitive impairments, with a view toward combining multiple tasks to effectively improve the detection capability. METHODS: We collected drawing data from 144 community-dwelling older adults (27 AD, 65 MCI, and 52 cognitively normal, or CN) who performed five drawing tasks. We then extracted motion- and pause-related drawing features for each task and investigated the associations of the features with the participants' diagnostic statuses and cognitive measures. RESULTS: The drawing features showed gradual changes from CN to MCI and then to AD, and the changes in the features for each task were statistically associated with cognitive impairments in different domains. For classification into the three diagnostic categories, a machine learning model using the features from all five tasks achieved a classification accuracy of 75.2%, an improvement by 7.8% over that of the best single-task model. CONCLUSION: Our results demonstrate that a common set of drawing features from multiple drawing tasks can capture different, complementary aspects of cognitive impairments, which may lead to a scalable way to improve the automated, reliable detection of AD and MCI.

Automated Analysis of Drawing Process to Estimate Global Cognition in Older Adults: Preliminary International Validation on the US and Japan Data Sets.

BACKGROUND: With the aging of populations worldwide, early detection of cognitive impairments has become a research and clinical priority, particularly to enable preventive intervention for dementia. Automated analysis of the drawing process has been studied as a promising means for lightweight, self-administered cognitive assessment. However, this approach has not been sufficiently tested for its applicability across populations. OBJECTIVE: The aim of this study was to evaluate the applicability of automated analysis of the drawing process for estimating global cognition in community-dwelling older adults across populations in different nations. METHODS: We collected drawing data with a digital tablet, along with Montreal Cognitive Assessment (MoCA) scores for assessment of global cognition, from 92 community-dwelling older adults in the United States and Japan. We automatically extracted 6 drawing features that characterize the drawing process in terms of the drawing speed, pauses between drawings, pen pressure, and pen inclinations. We then investigated the association between the drawing features and MoCA scores through correlation and machine learning-based regression analyses. RESULTS: We found that, with low MoCA scores, there tended to be higher variability in the drawing speed, a higher pause:drawing duration ratio, and lower variability in the pen's horizontal inclination in both the US and Japan data sets. A machine learning model that used drawing features to estimate MoCA scores demonstrated its capability to generalize from the US dataset to the Japan dataset (R2=0.35; permutation test, P<.001). CONCLUSIONS: This study presents initial empirical evidence of the capability of automated analysis of the drawing process as an estimator of global cognition that is applicable across populations. Our results suggest that such automated analysis may enable the development of a practical tool for international use in self-administered, automated cognitive assessment.

Can AI make people happy? The effect of AI-based chatbot on smile and speech in Parkinson's disease.

INTRODUCTION: Approaches for objectively measuring facial expressions and speech may enhance clinical and research evaluation in telemedicine, which is widely employed for Parkinson's disease (PD). This study aimed to assess the feasibility and efficacy of using an artificial intelligence-based chatbot to improve smile and speech in PD. Further, we explored the potential predictive value of objective face and speech parameters for motor symptoms, cognition, and mood. METHODS: In this open-label randomized study, we collected a series of face and conversational speech samples from 20 participants with PD in weekly teleconsultation sessions for 5 months. We investigated the effect of daily chatbot conversations on smile and speech features, then we investigated whether smile and speech features could predict motor, cognitive, and mood status. RESULTS: A repeated-measures analysis of variance revealed that the chatbot conversations had a significant interaction effect on the mean and standard deviation of the smile index during smile sections (both P = .02), maximum duration of the initial rise of the smile index (P = .04), and frequency of filler words (P = .04), but no significant interaction effects were observed for clinical measurements including motor, cognition, depression, and quality of life. Explorative analysis using statistical and machine-learning models revealed that the smile indices and several speech features were associated with motor symptoms, cognition, and mood in PD. CONCLUSION: An artificial intelligence-based chatbot may positively affect smile and speech in PD. Smile and speech features may capture the motor, cognitive, and mental status of patients with PD.

Tablet-Based Automatic Assessment for Early Detection of Alzheimer's Disease Using Speech Responses to Daily Life Questions.

Health-monitoring technologies for automatically detecting the early signs of Alzheimer's disease (AD) have become increasingly important. Speech responses to neuropsychological tasks have been used for quantifying changes resulting from AD and differentiating AD and mild cognitive impairment (MCI) from cognitively normal (CN). However, whether and how other types of speech tasks with less burden on older adults could be used for detecting early signs of AD remains unexplored. In this study, we developed a tablet-based application and compared speech responses to daily life questions with those to neuropsychological tasks in terms of differentiating MCI from CN. We found that in daily life questions, around 80% of speech features showing significant differences between CN and MCI overlapped those showing significant differences in both our study and other studies using neuropsychological tasks, but the number of significantly different features as well as their effect sizes from life questions decreased compared with those from neuropsychological tasks. On the other hand, the results of classification models for detecting MCI by using the speech features showed that daily life questions could achieve high accuracy, i.e., 86.4%, comparable to neuropsychological tasks by using eight questions against all five neuropsychological tasks. Our results indicate that, while daily life questions may elicit weaker but statistically discernable differences in speech responses resulting from MCI than neuropsychological tasks, combining them could be useful for detecting MCI with comparable performance to using neuropsychological tasks, which could help develop health-monitoring technologies for early detection of AD in a less burdensome manner.

Combining Multimodal Behavioral Data of Gait, Speech, and Drawing for Classification of Alzheimer's Disease and Mild Cognitive Impairment.

BACKGROUND: Gait, speech, and drawing behaviors have been shown to be sensitive to the diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, previous studies focused on only analyzing individual behavioral modalities, although these studies suggested that each of these modalities may capture different profiles of cognitive impairments associated with AD. OBJECTIVE: We aimed to investigate if combining behavioral data of gait, speech, and drawing can improve classification performance compared with the use of individual modality and if each of these behavioral data can be associated with different cognitive and clinical measures for the diagnosis of AD and MCI. METHODS: Behavioral data of gait, speech, and drawing were acquired from 118 AD, MCI, and cognitively normal (CN) participants. RESULTS: Combining all three behavioral modalities achieved 93.0% accuracy for classifying AD, MCI, and CN, and only 81.9% when using the best individual behavioral modality. Each of these behavioral modalities was statistically significantly associated with different cognitive and clinical measures for diagnosing AD and MCI. CONCLUSION: Our findings indicate that these behaviors provide different and complementary information about cognitive impairments such that classification of AD and MCI is superior to using either in isolation.

Using Speech Data From Interactions With a Voice Assistant to Predict the Risk of Future Accidents for Older Drivers: Prospective Cohort Study.

BACKGROUND: With the rapid growth of the older adult population worldwide, car accidents involving this population group have become an increasingly serious problem. Cognitive impairment, which is assessed using neuropsychological tests, has been reported as a risk factor for being involved in car accidents; however, it remains unclear whether this risk can be predicted using daily behavior data. OBJECTIVE: The objective of this study was to investigate whether speech data that can be collected in everyday life can be used to predict the risk of an older driver being involved in a car accident. METHODS: At baseline, we collected (1) speech data during interactions with a voice assistant and (2) cognitive assessment data-neuropsychological tests (Mini-Mental State Examination, revised Wechsler immediate and delayed logical memory, Frontal Assessment Battery, trail making test-parts A and B, and Clock Drawing Test), Geriatric Depression Scale, magnetic resonance imaging, and demographics (age, sex, education)-from older adults. Approximately one-and-a-half years later, we followed up to collect information about their driving experiences (with respect to car accidents) using a questionnaire. We investigated the association between speech data and future accident risk using statistical analysis and machine learning models. RESULTS: We found that older drivers (n=60) with accident or near-accident experiences had statistically discernible differences in speech features that suggest cognitive impairment such as reduced speech rate (P=.048) and increased response time (P=.040). Moreover, the model that used speech features could predict future accident or near-accident experiences with 81.7% accuracy, which was 6.7% higher than that using cognitive assessment data, and could achieve up to 88.3% accuracy when the model used both types of data. CONCLUSIONS: Our study provides the first empirical results that suggest analysis of speech data recorded during interactions with voice assistants could help predict future accident risk for older drivers by capturing subtle impairments in cognitive function.

High affinity promoter binding of STOP1 is essential for early expression of novel aluminum-induced resistance genes GDH1 and GDH2 in Arabidopsis.

Malate efflux from roots, which is regulated by the transcription factor STOP1 (SENSITIVE-TO-PROTON-RHIZOTOXICITY1) and mediates aluminum-induced expression of ALUMINUM-ACTIVATED-MALATE-TRANSPORTER1 (AtALMT1), is critical for aluminum resistance in Arabidopsis thaliana. Several studies showed that AtALMT1 expression in roots is rapidly observed in response to aluminum; this early induction is an important mechanism to immediately protect roots from aluminum toxicity. Identifying the molecular mechanisms that underlie rapid aluminum resistance responses should lead to a better understanding of plant aluminum sensing and signal transduction mechanisms. In this study, we observed that GFP-tagged STOP1 proteins accumulated in the nucleus soon after aluminum treatment. The rapid aluminum-induced STOP1-nuclear localization and AtALMT1 induction were detected in the presence of a protein synthesis inhibitor, suggesting that post-translational regulation is involved in these events. STOP1 also regulated rapid aluminum-induced expression for other genes that carry a functional/high-affinity STOP1-binding site in their promoter, including STOP2, GLUTAMATE-DEHYDROGENASE1 and 2 (GDH1 and 2). However STOP1 did not regulate Al resistance genes which have no functional STOP1-binding site such as ALUMINUM-SENSITIVE3, suggesting that the binding of STOP1 in the promoter is essential for early induction. Finally, we report that GDH1 and 2 which are targets of STOP1, are novel aluminum-resistance genes in Arabidopsis.

Expression GWAS of PGIP1 Identifies STOP1-Dependent and STOP1-Independent Regulation of PGIP1 in Aluminum Stress Signaling in Arabidopsis.

To elucidate the unknown regulatory mechanisms involved in aluminum (Al)-induced expression of POLYGALACTURONASE-INHIBITING PROTEIN 1 (PGIP1), which is one of the downstream genes of SENSITIVE TO PROTON RHIZOTOXICITY 1 (STOP1) regulating Al-tolerance genes, we conducted a genome-wide association analysis of gene expression levels (eGWAS) of PGIP1 in the shoots under Al stress using 83 Arabidopsis thaliana accessions. The eGWAS, conducted through a mixed linear model, revealed 17 suggestive SNPs across the genome having the association with the expression level variation in PGIP1. The GWAS-detected SNPs were directly located inside transcription factors and other genes involved in stress signaling, which were expressed in response to Al. These candidate genes carried different expression level and amino acid polymorphisms. Among them, three genes encoding NAC domain-containing protein 27 (NAC027), TRX superfamily protein, and R-R-type MYB protein were associated with the suppression of PGIP1 expression in their mutants, and accordingly, the system affected Al tolerance. We also found the involvement of Al-induced endogenous nitric oxide (NO) signaling, which induces NAC027 and R-R-type MYB genes to regulate PGIP1 expression. In this study, we provide genetic evidence that STOP1-independent NO signaling pathway and STOP1-dependent regulation in phosphoinositide (PI) signaling pathway are involved in the regulation of PGIP1 expression under Al stress.

A single-population GWAS identified AtMATE expression level polymorphism caused by promoter variants is associated with variation in aluminum tolerance in a local Arabidopsis population.

Organic acids (OA) are released from roots in response to aluminum (Al), conferring an Al tolerance to plants that is regulated by OA transporters such as ALMT (Al-activated malate transporter) and multi-drug and toxic compound extrusion (MATE). We have previously reported that the expression level polymorphism (ELP) of AtALMT1 is strongly associated with variation in Al tolerance among natural accessions of Arabidopsis. However, although AtMATE is also expressed following Al exposure and contributes to Al tolerance, whether AtMATE contributes to the variation of Al tolerance and the molecular mechanisms of ELP remains unclear. Here, we dissected the natural variation in AtMATE expression level in response to Al at the root using diverse natural accessions of Arabidopsis. Phylogenetic analysis revealed that more than half of accessions belonging to the Central Asia (CA) population show markedly low AtMATE expression levels, while the majority of European populations show high expression levels. The accessions of the CA population with low AtMATE expression also show significantly weakened Al tolerance. A single-population genome-wide association study (GWAS) of AtMATE expression in the CA population identified a retrotransposon insertion in the AtMATE promoter region associated with low gene expression levels. This may affect the transcriptional regulation of AtMATE by disrupting the effect of a cis-regulatory element located upstream of the insertion site, which includes AtSTOP1 (sensitive to proton rhizotoxicity 1) transcription factor-binding sites revealed by chromatin immunoprecipitation-qPCR analysis. Furthermore, the GWAS performed without the accessions expressing low levels of AtMATE, excluding the effect of AtMATE promoter polymorphism, identified several candidate genes potentially associated with AtMATE expression.

Antagonistic regulation of the gibberellic acid response during stem growth in rice.

The size of plants is largely determined by growth of the stem. Stem elongation is stimulated by gibberellic acid1-3. Here we show that internode stem elongation in rice is regulated antagonistically by an 'accelerator' and a 'decelerator' in concert with gibberellic acid. Expression of a gene we name ACCELERATOR OF INTERNODE ELONGATION 1 (ACE1), which encodes a protein of unknown function, confers cells of the intercalary meristematic region with the competence for cell division, leading to internode elongation in the presence of gibberellic acid. By contrast, upregulation of DECELERATOR OF INTERNODE ELONGATION 1 (DEC1), which encodes a zinc-finger transcription factor, suppresses internode elongation, whereas downregulation of DEC1 allows internode elongation. We also show that the mechanism of internode elongation that is mediated by ACE1 and DEC1 is conserved in the Gramineae family. Furthermore, an analysis of genetic diversity suggests that mutations in ACE1 and DEC1 have historically contributed to the selection of shorter plants in domesticated populations of rice to increase their resistance to lodging, and of taller plants in wild species of rice for adaptation to growth in deep water. Our identification of these antagonistic regulatory factors enhances our understanding of the gibberellic acid response as an additional mechanism that regulates internode elongation and environmental fitness, beyond biosynthesis and gibberellic acid signal transduction.

Genome-Wide Association Study and Genomic Prediction Elucidate the Distinct Genetic Architecture of Aluminum and Proton Tolerance in Arabidopsis thaliana.

Under acid soil conditions, Al stress and proton stress can occur, reducing root growth and function. However, these stressors are distinct, and tolerance to each is governed by multiple physiological processes. To better understand the genes that underlie these coincidental but experimentally separable stresses, a genome-wide association study (GWAS) and genomic prediction (GP) models were created for approximately 200 diverse Arabidopsis thaliana accessions. GWAS and genomic prediction identified 140/160 SNPs associated with Al and proton tolerance, respectively, which explained approximately 70% of the variance observed. Reverse genetics of the genes in loci identified novel Al and proton tolerance genes, including TON1-RECRUITING MOTIF 28 (AtTRM28) and THIOREDOXIN H-TYPE 1 (AtTRX1), as well as genes known to be associated with tolerance, such as the Al-activated malate transporter, AtALMT1. Additionally, variation in Al tolerance was partially explained by expression level polymorphisms of AtALMT1 and AtTRX1 caused by cis-regulatory allelic variation. These results suggest that we successfully identified the loci that regulate Al and proton tolerance. Furthermore, very small numbers of loci were shared by Al and proton tolerance as determined by the GWAS. There were substantial differences between the phenotype predicted by genomic prediction and the observed phenotype for Al tolerance. This suggested that the GWAS-undetectable genetic factors (e.g., rare-allele mutations) contributing to the variation of tolerance were more important for Al tolerance than for proton tolerance. This study provides important new insights into the genetic architecture that produces variation in the tolerance of acid soil.

Using Tablet-Based Assessment to Characterize Speech for Individuals with Dementia and Mild Cognitive Impairment: Preliminary Results.

Early detection of dementia as well as improvement in diagnosis coverage has been increasingly important. Previous studies involved extracting speech features during neuropsychological assessments by humans, such as medical pro- fessionals, and succeeded in detecting patients with dementia and mild cognitive impairment (MCI). Enabling such assessment in an automated fashion by using computer devices would extend the range of application. In this study, we developed a tablet-based application for neuropsychological assessments and collected speech data from 44 Japanese native speakers including healthy controls (HCs) and those with MCI and dementia. We first extracted acoustic and phonetic features and showed that several features exhibited significant difference between HC vs. MCI and HC vs. dementia. We then constructed classification models by using these features and demonstrated that these models could differentiate MCI and dementia from HC with up to 82.4 and 92.6% accuracy, respectively.

Sensitive to Proton Rhizotoxicity1 Regulates Salt and Drought Tolerance of Arabidopsis thaliana through Transcriptional Regulation of CIPK23.

The transcription factor sensitive to proton rhizotoxicity 1 (STOP1) regulates multiple stress tolerances. In this study, we confirmed its involvement in NaCl and drought tolerance. The root growth of the T-DNA insertion mutant of STOP1 (stop1) was sensitive to NaCl-containing solidified MS media. Transcriptome analysis of stop1 under NaCl stress revealed that STOP1 regulates several genes related to salt tolerance, including CIPK23. Among all available homozygous T-DNA insertion mutants of the genes suppressed in stop1, only cipk23 showed a NaCl-sensitive root growth phenotype comparable to stop1. The CIPK23 promoter had a functional STOP1-binding site, suggesting a strong CIPK23 suppression led to NaCl sensitivity of stop1. This possibility was supported by in planta complementation of CIPK23 in the stop1 background, which rescued the short root phenotype under NaCl. Both stop1 and cipk23 exhibited a drought tolerant phenotype and increased abscisic acid-regulated stomatal closure, while the complementation of CIPK23 in stop1 reversed these traits. Our findings uncover additional pleiotropic roles of STOP1 mediated by CIPK23, which regulates various ion transporters including those regulating K+-homeostasis, which may induce a trade-off between drought tolerance and other traits.