Diagnosis of ADHD using virtual reality and artificial intelligence: an exploratory study of clinical applications.

Introduction: Diagnosis of Attention Deficit/Hyperactivity Disorder (ADHD) is based on clinical evaluation of symptoms by a psychiatrist, referencing results of psychological tests. When diagnosing ADHD, the child's behavior and functionality in real-life situations are critical components. However, direct observation by a clinician is often not feasible in practice. Therefore, such information is typically gathered from primary caregivers or teachers, which can introduce subjective elements. To overcome these limitations, we developed AttnKare-D, an innovative digital diagnostic tool that could analyze children's behavioral data in Virtual Reality using Artificial Intelligence. The purpose of this study was to explore the utility and safety of AttnKare-D for clinical application. Method: A total of 21 children aged between 6 and 12 years were recruited for this study. Among them, 15 were children diagnosed with ADHD, 5 were part of a normal control group, and 1 child was excluded due to withdrawal of consent. Psychological assessments, including K-WISC, Conners CPT, K-ARS, and K-CBCL, were conducted for participants and their primary caregivers. Diagnoses of ADHD were confirmed by child and adolescent psychiatrists based on comprehensive face-to-face evaluations and results of psychological assessments. Participants underwent VR diagnostic assessment by performing various cognitive and behavioral tasks in a VR environment. Collected data were analyzed using an AI model to assess ADHD diagnosis and the severity of symptoms. Results: AttnKare-D demonstrated diagnostic performance with an AUC of 0.893 when compared to diagnoses made by child and adolescent psychiatrist, showing a sensitivity of 0.8 and a specificity of 1.0 at a cut-off score of 18.44. AttnKare-D scores showed a high correlation with K-ARS scores rated by parents and experts, although the correlation was relatively low for inattention scores. Conclusion: Results of this study suggest that AttnKare-D can be a useful tool for diagnosing ADHD in children. This approach has potential to overcome limitations of current diagnostic methods, enhancing the accuracy and objectivity of ADHD diagnoses. This study lays the groundwork for further improvement and research on diagnostic tools integrating VR and AI technologies. For future clinical applications, it is necessary to conduct clinical trials involving a sufficient number of participants to ensure reliable use.

Arabidopsis transcription factor TCP13 promotes shade avoidance syndrome-like responses by directly targeting a subset of shade-responsive gene promoters.

TCP13 belongs to a subgroup of TCP transcription factors implicated in the shade avoidance syndrome (SAS), but its exact role remains unclear. Here, we show that TCP13 promotes the SAS-like response by enhancing hypocotyl elongation and suppressing flavonoid biosynthesis as a part of the incoherent feed-forward loop in light signaling. Shade is known to promote the SAS by activating PHYTOCHROME-INTERACTING FACTOR (PIF)-auxin signaling in plants, but we found no evidence in a transcriptome analysis that TCP13 activates PIF-auxin signaling. Instead, TCP13 mimics shade by activating the expression of a subset of shade-inducible and cell elongation-promoting SAUR genes including SAUR19, by direct targeting of their promoters. We also found that TCP13 and PIF4, a molecular proxy for shade, repress the expression of flavonoid biosynthetic genes by directly targeting both shared and distinct sets of biosynthetic gene promoters. Together, our results indicate that TCP13 promotes the SAS-like response by directly targeting a subset of shade-responsive genes without activating the PIF-auxin signaling pathway.

Shade represses photosynthetic genes by disrupting the DNA binding of GOLDEN2-LIKE1.

PHYTOCHROME-INTERACTING FACTORs (PIFs) repress photosynthetic genes partly by upregulating REPRESSOR OF PHOTOSYNTHETIC GENES 1 (RPGE1) and RPGE2. However, it is unknown how RPGEs inhibit gene expression at the molecular level. Here, we show that Arabidopsis (Arabidopsis thaliana) RPGE overexpression lines display extensive similarities to the golden2-like 1 (glk1)/glk2 double mutant at the phenotypic and transcriptomic levels, prompting us to hypothesize that there is a close molecular relationship between RPGEs and chloroplast development-regulating GLK transcription factors. Indeed, we found that RPGE1 disrupts the homodimerization of GLK1 by interacting with its dimerization domain and debilitates the DNA-binding activity of GLK1. The interaction was not restricted to the Arabidopsis RPGE1-GLK1 pair, but rather extended to RPGE-GLK homolog pairs across species, providing a molecular basis for the pale green leaves of Arabidopsis transgenic lines expressing a rice (Oryza sativa) RPGE homolog. Our discovery of RPGE-GLK regulatory pairs suggests that any condition leading to an increase in RPGE levels would decrease the expression levels of GLK target genes. Consistently, we found that shade, which upregulates the RPGE mRNA by stabilizing PIFs, represses the expression of photosynthetic genes partly by inhibiting the DNA-binding activity of GLK1. Taken together, these results indicate that RPGE-GLK regulatory pairs regulate photosynthetic gene expression downstream of PIFs.

Differentiated service in OBS networks using a dynamic FDL bank partitioning algorithm.

In order to solve the differentiated service problem in optical burst switching (OBS) networks, we propose a dynamic fiber delay line (FDL) bank partitioning algorithm, which divides a FDL bank into several groups, using a feed-forward output buffering architecture. In the analysis, three classes and groups are considered for traffic and FDL, respectively, and each group is assigned to each class. This paper shows that the loss differentiation in OBS networks is easily accomplished in Poisson traffic environments when our dynamic algorithm is adopted.