Repressor element-1 silencing transcription factor (REST) is a transcriptional repressor involved in neurodevelopment and neuroprotection. REST forms a complex with the REST corepressors, CoREST1, CoREST2, or CoREST3 (encoded by RCOR1, RCOR2, and RCOR3, respectively). Emerging evidence suggests that the CoREST family can target unique genes independently of REST, in various neural and glial cell types during different developmental stages. However, there is limited knowledge regarding the expression and function of the CoREST family in human neurodevelopment. To address this gap, we employed 2D and 3D human pluripotent stem cell (hPSC) models to investigate REST and RCOR gene expression levels. Our study revealed a significant increase in RCOR3 expression in glutamatergic cortical and GABAergic ventral forebrain neurons, as well as mature functional NGN2-induced neurons. Additionally, a simplified astrocyte transdifferentiation protocol resulted in a significant decrease in RCOR2 expression following differentiation. REST expression was notably reduced in mature neurons and cerebral organoids. In summary, our findings provide the first insights into the cell-type-specific expression patterns of RCOR genes in human neuronal and glial differentiation. Specifically, RCOR3 expression increases in neurons, while RCOR2 levels decrease in astrocytes. The dynamic expression patterns of REST and RCOR genes during hPSC neuronal and glial differentiation underscore the potential distinct roles played by REST and CoREST proteins in regulating the development of these cell types in humans.

To obtain precise positional information, in this study, we propose an adaptive expectation-maximization (EM)-based Kalman filter (KF)/finite impulse response (FIR) integrated filter for inertial navigation system (INS)-based posture capture of human upper limbs. Initially, a data fusion model for wrist and elbow position is developed. Subsequently, the Mahalanobis distance is utilized to evaluate the performance of the filter. The integrated filter employs the EM-based KF to enhance noise estimation accuracy when the performance of KF declines. Conversely, upon deterioration in the performance of the EM-based KF, which is evaluated using the Mahalanobis distance, the FIR filter is employed to maintain the effectiveness of the data fusion filter. This research utilizes the proposed EM-based KF/FIR integrated filter to ascertain wrist and elbow positions. The empirical results demonstrate the proficiency of the proposed approach in estimating these positions, thereby overcoming the challenge and highlighting its inherent effectiveness.

Autonomous vehicles (AVs) require accurate navigation, but the reliability of Global Navigation Satellite Systems (GNSS) can be degraded by signal blockage and multipath interference in urban areas. Therefore, a navigation system that integrates a calibrated Reduced Inertial Sensors System (RISS) with GNSS is proposed. The system employs a machine-learning-based Adaptive Neuro-Fuzzy Inference System (ANFIS) as a novel calibration technique to improve the accuracy and reliability of the RISS. The ANFIS-based RISS/GNSS integration provides a more precise navigation solution in such environments. The effectiveness of the proposed integration scheme was validated by conducting tests using real road trajectory and simulated GNSS outages ranging from 50 to 150 s. The results demonstrate a significant improvement in 2D position Root Mean Square Error (RMSE) of 43.8% and 28% compared to the traditional RISS/GNSS and the frequency modulated continuous wave (FMCW) Radar (Rad)/RISS/GNSS integrated navigation systems, respectively. Moreover, an improvement of 47.5% and 23.4% in 2D position maximum errors is achieved compared to the RISS/GNSS and the Rad/RISS/GNSS integrated navigation systems, respectively. These results reveal significant improvements in positioning accuracy, which is essential for safe and efficient navigation. The long-term stability of the proposed system makes it suitable for various navigation applications, particularly those requiring continuous and precise positioning information. The ANFIS-based approach used in the proposed system is extendable to other low-end IMUs, making it an attractive option for a wide range of applications.

Type 2 Diabetes Mellitus (T2DM), as a significant health concern globally, particularly in India, underscoring the vital need for effective therapeutics. Current drug therapies for T2DM may have limitations, leading researchers to explore natural products as alternatives. In this study. We have investigated the anti-diabetic compounds from the Costus genus, known as the insulin plant, which is abundant in southern India. The bioinformatics tools and software used for in-silico analysis to identify potential therapeutic compounds and hub genes associated with T2DM in the Indian population that could cut short the in-vitro and in-vivo experimental approaches in near future. The systematic review and combinatorial in-silico analysis revealed IGF2BP2, INS and TCF as the key targets that are associated with T2DM. The compounds stigmasterol, cycloartenol, and diosgenone were explored to be potent among all the 38 phytocompounds from genus Costus with binding energies -8.48, -10.07, and -10.31 kcal/mol against IGF2BP2, INS and TCF. The molecular dynamics (MD) simulation studies of these complexes demonstrated stable and consistent dynamic behavior, particularly in the INS-cycloartenol, IGF2BP2-stigmasterol and TCF7L2-diosgenone complexes. The identified compounds and associated targets represent potential candidates for T2DM therapeutics in the Indian population. The pharmacoinformatics approach presented in the study could streamline the drug discovery process by prioritizing compounds for further experimental validation.Communicated by Ramaswamy H. Sarma.

Pedestrian navigation methods based on inertial sensors are commonly used to solve navigation and positioning problems when satellite signals are unavailable. To address the issue of heading angle errors accumulating over time in pedestrian navigation systems that rely solely on the Zero Velocity Update (ZUPT) algorithm, it is feasible to use the pedestrian\'s motion constraints to constrain the errors. Firstly, a human step length model is built using human kinematic data collected by the motion capture system. Secondly, we propose the bipedal constraint algorithm based on the established human step length model. Real field experiments demonstrate that, by introducing the bipedal constraint algorithm, the mean biped radial errors of the experiments are reduced by 68.16% and 50.61%, respectively. The experimental results show that the proposed algorithm effectively reduces the radial error of the navigation results and improves the accuracy of the navigation.

The challenge of precise dynamic positioning for mobile robots is addressed through the development of a multi-inertial navigation system (M-INSs). The inherent cumulative sensor errors prevalent in traditional single inertial navigation systems (INSs) under dynamic conditions are mitigated by a novel algorithm, integrating multiple INS units in a predefined planar configuration, utilizing fixed distances between the units as invariant constraints. An extended Kalman filter (EKF) is employed to significantly enhance the positioning accuracy. Dynamic experimental validation of the proposed 3INS EKF algorithm reveals a marked improvement over individual INS units, with the positioning errors reduced and stability increased, resulting in an average accuracy enhancement rate exceeding 60%. This advancement is particularly critical for mobile robot applications that demand high precision, such as autonomous driving and disaster search and rescue. The findings from this study not only demonstrate the potential of M-INSs to improve dynamic positioning accuracy but also to provide a new research direction for future advancements in robotic navigation systems.

Insulin-like peptides are a group of hormones crucial for regulating metabolism, growth, and development in animals. Invertebrates, such as C. elegans, have been instrumental in understanding the molecular mechanisms of insulin-like peptides. Here, we review the 40 insulin-like peptide genes encoded in the C. elegans genome. Despite the large number, there is only one C. elegans insulin-like peptide receptor, called DAF-2. The insulin and insulin-like growth factor signaling (IIS) pathway is evolutionarily conserved from worms to humans. Thus C. elegans provides an excellent model to understand how these insulin-like peptides function. C. elegans is unique in that it possesses insulin-like peptides that have antagonistic properties, unlike all human insulin-like peptides, which are agonists. This review provides an overview of the current literature on C. elegans insulin-like peptide structures, processing, tissue localization, and regulation. We will also provide examples of insulin-like peptide signaling in C. elegans during growth, development, germline development, learning/memory, and longevity.

To obtain accurate position information, herein, a one-assistant method involving the fusion of extreme learning machine (ELM)/finite impulse response (FIR) filters and vision data is proposed for inertial navigation system (INS)-based human motion capture. In the proposed method, when vision is available, the vision-based human position is considered as input to an FIR filter that accurately outputs the human position. Meanwhile, another FIR filter outputs the human position using INS data. ELM is used to build mapping between the output of the FIR filter and the corresponding error. When vision data are unavailable, FIR is used to provide the human posture and ELM is used to provide its estimation error built in the abovementioned stage. In the right-arm elbow, the proposed method can improve the cumulative distribution functions (CDFs) of the position errors by about 12.71%, which shows the effectiveness of the proposed method.

The increase in the prevalence of overweight, obesity and associated diseases is a serious problem. The aim of the study was to identify rare variants in obesity-associated genes in young adults with abdominal obesity in our population and to analyze information about these variants in other populations. Targeted high-throughput sequencing of obesity-associated genes was performed (203 young adults with an abdominal obesity phenotype). In our study, all of the 203 young adults with abdominal obesity had some rare variant in the genes associated with obesity. The widest range of rare and common variants was presented in ADIPOQ, FTO, GLP1R, GHRL, and INS genes. The use of targeted sequencing and clinical criteria makes it possible to identify carriers of rare clinically significant variants in a wide range of obesity-associated genes and to investigate their influence on phenotypic manifestations of abdominal obesity.

We elucidated the effect of four known T1D-susceptibility associated single nucleotide polymorphism (SNP) markers in three genes (rs12722495 and rs2104286 in IL2RA, rs689 in INS and rs2476601 in PTPN22) on CpG site methylation of their proximal promoters in different lymphocyte subsets using pyrosequencing. The study cohort comprised 25 children with newly diagnosed T1D and 25 matched healthy controls. The rs689 SNP was associated with methylation at four CpG sites in INS promoter: -234, -206, -102 and -69. At all four CpG sites, the susceptibility genotype AA was associated with a higher methylation level compared to the other genotypes. We also found an association between rs12722495 and methylation at CpG sites -373 and -356 in IL2RA promoter in B cells, where the risk genotype AA was associated with lower methylation level compared to the AG genotype. The other SNPs analyzed did not demonstrate significant associations with CpG site methylation in the examined genes. Additionally, we compared the methylation between children with T1D and controls, and found statistically significant methylation differences at CpG -135 in INS in CD8+ T cells (p = 0.034), where T1D patients had a slightly higher methylation compared to controls (87.3 ± 7.2 vs. 78.8 ± 8.9). At the other CpG sites analyzed, the methylation was similar. Our results not only confirm the association between INS methylation and rs689 discovered in earlier studies but also report this association in sorted immune cells. We also report an association between rs12722495 and IL2RA promoter methylation in B cells. These results suggest that at least part of the genetic effect of rs689 and rs12722495 on T1D pathogenesis may be conveyed by DNA methylation.