Biology subjects

Soetedjo, J.

Publications and source records attributed to Soetedjo, J..

3 recordsLinked to original sources

bioRxiv2026

Kv2.1/Kv8.2 Channels Regulate Fluid Homeostasis in the Outer Retina

PurposePhotoreceptor Kv2.1/Kv8.2 voltage-gated potassium channels carry an outward potassium current, helping to set the resting membrane potential and to shape dim light responses. Because potassium flux in the outer retina influences extracellular osmolarity and fluid distribution, we hypothesized that Kv2.1/Kv8.2 channels also contribute to fluid homeostasis in this region of the retina. MethodsOCT imaging was performed in Kv8.2 heterozygous (Het) and knockout (KO) mice aged 4-7 weeks under dark- and light-adapted conditions. Light-dark differences in the distance between the external limiting membrane (ELM) and retinal pigment epithelium (RPE) ({Delta}ELM-RPE) were calculated to quantify light-evoked expansion of the subretinal space (SRS). As a secondary outcome, outer nuclear layer (ONL) thickness was also measured under both lighting conditions. Retinal gene expression was assessed by RNA-seq and droplet digital RT-PCR. Retinal protein expression was determined by western blotting and immunolabeling. Results{Delta}ELM-RPE was significantly reduced in Kv8.2 KO mice compared with Het controls, indicating reduced SRS hydration. ONL thickness exhibited a small but significant light-dark change that was different between genotypes. Transcriptomic analyses revealed upregulation of osmosensitive genes, including osmolyte transporters and aquaporins. AQP1 protein expression in photoreceptors increased. ConclusionsThese findings reveal a previously unrecognized role for Kv2.1/Kv8.2 channels in outer retinal fluid homeostasis and support a model in which photoreceptor potassium efflux contributes to osmotic water movement into the subretinal space.

neuroscience↗

bioRxiv2025

Effect of Large Language Models on P300 Speller Performance with Cross-Subject Training

Amyotrophic lateral sclerosis (ALS), a progressive neurodegenerative disease, severely impairs communication, requiring assistive technologies that restore interaction. The P300 speller brain-computer interface (BCI) enables communication by translating EEG responses into text; however, its practical adoption is limited by slow typing speed and the need for subject-specific calibration. Recent work has demonstrated that large language models (LLMs), such as GPT-2, can significantly improve the performance of the P300 speller by predicting words. However, it remains unclear whether these gains are model-specific or represent a broader trend across language models. Furthermore, the fundamental performance limits of LLM-assisted P300 spellers have not been systematically characterized within a unified decoding framework. In this study, we address these gaps through a systematic multi-model theoretical analysis framework. We evaluate a wide range of language models and introduce an idealized LLM to establish upper bounds on achievable performance. In addition, we incorporate cross-subject classifier training to reduce calibration requirements and assess generalization across subjects. Using extensive simulations on EEG data from 78 subjects, we demonstrate that the evaluated models consistently achieve substantial improvements in typing speed, with gains of up to [~]40% (across-subject training) and [~]75% (within-subject training) over conventional approaches. More importantly, we show that multiple models, despite architectural differences, operate within 5% of the theoretical performance bound, indicating diminishing returns from further model scaling. These improvements generalize across both within-subject and across-subject classifiers. Our results suggest that LLM-assisted P300 spellers are approaching their fundamental performance limits within the considered decoding framework, shifting the primary bottleneck from language modeling to neural signal decoding. This work provides both a practical framework for improving BCI communication and a theoretical perspective on its achievable limits.

neuroscience↗

bioRxiv2025

Esr1-Dependent Signaling and Transcriptional Maturation in the Medial Preoptic Area of the Hypothalamus Shapes the Development of Mating Behavior during Adolescence

Mating and other behaviors emerge during adolescence through the coordinated actions of steroid hormone signaling throughout the nervous system and periphery. In this study, we investigated the transcriptional dynamics of the medial preoptic area (MPOA), a critical region for reproductive behavior, using single-cell RNA sequencing (scRNAseq) and in situ hybridization techniques in male and female mice throughout adolescence development. Our findings reveal that estrogen receptor 1 (Esr1) plays a pivotal role in the transcriptional maturation of GABAergic neurons within the MPOA during adolescence. Deletion of the estrogen receptor gene, Esr1, in GABAergic neurons (Vgat+) disrupted the developmental progression of mating behaviors in both sexes, while its deletion in glutamatergic neurons (Vglut2+) had no observable effect. In males and females, these neurons displayed distinct transcriptional trajectories, with hormone-dependent gene expression patterns emerging throughout adolescence and regulated by Esr1. Esr1 deletion in MPOA GABAergic neurons, prior to adolescence, arrested adolescent transcriptional progression of these cells and uncovered sex-specific gene-regulatory networks associated with Esr1 signaling. Our results underscore the critical role of Esr1 in orchestrating sex-specific transcriptional dynamics during adolescence, revealing gene regulatory networks implicated in the development of hypothalamic controlled reproductive behaviors. One Sentence SummarySingle cell RNA sequencing reveals how adolescent sex hormones sculpt hypothalamic cell types required for mating behavior.

neuroscience↗