Long-term prediction of algal chlorophyll based on empirical models and the machine learning approach in relation to trophic variation in Juam Reservoir, Korea.

This study analyzed spatiotemporal variation and long-term trends in water quality indicators and trophic state conditions in an Asian temperate reservoir, Juam Reservoir (JR), and developed models that forecast algal chlorophyll (CHL-a) over a period of 30 years, 1993-2022. The analysis revealed that there were longitudinal gradients in water quality indicators along the reservoir, with notable influences from tributaries and seasonal variations in nutrient regimes and suspended solids. The empirical model showed phosphorus was found to be the key determinant of algal biomass, while suspended solids played a significant role in regulating water transparency. The trophic state indices indicated varying levels of trophic status, ranging from mesotrophic to eutrophic. Eutrophic states were particularly observed in zones after the summer monsoons, indicating a heightened risk of algal blooms, which were more prevalent in flood years. The analysis of trophic state index deviation suggested that phosphorus availability strongly influences the reservoir trophic status, with several episodes of non-algal turbidity at each site during Mon. Increases in non-algal turbidity were more prevalent during the monsoon in flood years. This study also highlighted overall long-term trends in certain water quality parameters, albeit with indications of shifting pollution sources towards non-biodegradable organic matter. According to the machine learning tests, a random forest (RF) model strongly predicted CHL-a (R2 = 0.72, p < 0.01), except for algal biomass peaks (>60 µg/L), compared to all other models. Overall, our research suggests that CHL-a and trophic variation are primarily regulated by the monsoon intensity and predicted well by the machine learning RF model.

Propensity-Score-Matched Evaluation of Adverse Events Affecting Recovery after COVID-19 Vaccination: On Adenovirus and mRNA Vaccines.

This study aimed to observe adverse events following immunisation (AEFIs) that affected recovery within two weeks after COVID-19 vaccination and investigate their risks in propensity-score-matched populations. Data were collected from 447,346 reports from the VAERS between 1 January 2021 and 31 July 2021. Propensity-score-matched populations were constructed by adjusting for demographic characteristics and 11 underlying diseases in eligible subjects who received 1 of 3 COVID-19 vaccines: 19,462 Ad26.COV2.S, 120,580 mRNA-1273, and 100,752 BNT162b2. We observed that 88 suspected AEFIs (22 in Ad26.COV2.S, 62 in mRNA-1273, and 54 in BNT162b2) were associated with an increased risk of delayed recovery within 2 weeks after COVID-19 vaccinations. Nervous system, musculoskeletal and connective tissue, gastrointestinal, skin, and subcutaneous tissue disorders were the most common AEFIs after COVID-19 vaccination. Interestingly, four local and systemic reactions affected recovery in different vaccine recipients during our study period: asthenic conditions and febrile disorders in Ad26.COV2.S and mRNA-1273; general signs and symptoms in mRNA-1273 and BNT162b2; injection site reactions in Ad26.COV2.S and BNT162b2. Although it is necessary to confirm a causal relationship with COVID-19 vaccinations, some symptoms, including paralysis, allergic disorders, breathing abnormalities, and visual impairment, may hinder the recovery of these recipients.

Hemispheric asymmetry in hand preference of right-handers for passive vibrotactile perception: an fNIRS study.

Hemispheric asymmetry in hand preference for passive cutaneous perception compared to active haptic perception is not well known. A functional near-infrared spectroscopy was used to evaluate the laterality of cortical facilitation when 31 normal right-handed participants were involved in 205 Hz passive vibrotactile cutaneous stimuli on their index fingers of preferred and less-preferred hand. Passive cutaneous perception resulted that preferred (right) hand stimulation was strongly leftward lateralized, whereas less-preferred (left) hand stimulation was less lateralized. This confirms that other manual haptic exploration studies described a higher hemispheric asymmetry in right-handers. Stronger cortical facilitation was found in the right primary somatosensory cortex (S1) and right somatosensory association area (SA) during left-hand stimulation but not right-hand stimulation. This finding suggests that the asymmetric activation in the S1 and SA for less-preferred (left) hand stimulation might contribute to considerably reinforce sensorimotor network just with passive vibrotactile cutaneous stimulation.

The difference in cortical activation pattern for complex motor skills: A functional near- infrared spectroscopy study.

The human brain is lateralized to dominant or non-dominant hemispheres, and controlled through large-scale neural networks between correlated cortical regions. Recently, many neuroimaging studies have been conducted to examine the origin of brain lateralization, but this is still unclear. In this study, we examined the differences in brain activation in subjects according to dominant and non-dominant hands while using chopsticks. Fifteen healthy right-handed subjects were recruited to perform tasks which included transferring almonds using stainless steel chopsticks. Functional near-infrared spectroscopy (fNIRS) was used to acquire the hemodynamic response over the primary sensory-motor cortex (SM1), premotor area (PMC), supplementary motor area (SMA), and frontal cortex. We measured the concentrations of oxy-hemoglobin and deoxy-hemoglobin induced during the use of chopsticks with dominant and non-dominant hands. While using the dominant hand, brain activation was observed on the contralateral side. While using the non-dominant hand, brain activation was observed on the ipsilateral side as well as the contralateral side. These results demonstrate dominance and functional asymmetry of the cerebral hemisphere.

Distinction of directional coupling in sensorimotor networks between active and passive finger movements using fNIRS.

The purpose of this study is to investigate cerebral cortex activation during active movement and passive movement by using a functional near-infrared spectroscopy (fNIRS). Tasks were the flexion/extension of the right hand finger by active movement and passive movement. Oxy-hemoglobin concentration changes calculated from fNIRS and analyzed the activation and connectivity so as to understand dynamical brain relationship. The results demonstrated that the brain activation in passive movements is similar to motor execution. During active movement, the estimated causality patterns showed significant causality value from the supplementary motor area (SMA) to the primary motor cortex (M1). During the passive movement, the causality from the primary somatosensory cortex (S1) to the primary motor cortex (M1) was stronger than active movement. These results demonstrated that active and passive movements had a direct effect on the cerebral cortex but the stimulus pathway of active and passive movement is different. This study may contribute to better understanding how active and passive movements can be expressed into cortical activation by means of fNIRS.

Motion Artifact Correction of Multi-Measured Functional Near-Infrared Spectroscopy Signals Based on Signal Reconstruction Using an Artificial Neural Network.

In this paper, a new motion artifact correction method is proposed based on multi-channel functional near-infrared spectroscopy (fNIRS) signals. Recently, wavelet transform and hemodynamic response function-based algorithms were proposed as methods of denoising and detrending fNIRS signals. However, these techniques cannot achieve impressive performance in the experimental environment with lots of movement such as gait and rehabilitation tasks because hemodynamic responses have features similar to those of motion artifacts. Moreover, it is difficult to correct motion artifacts in multi-measured fNIRS systems, which have multiple channels and different noise features in each channel. Thus, a new motion artifact correction method for multi-measured fNIRS is proposed in this study, which includes a decision algorithm to determine the most contaminated fNIRS channel based on entropy and a reconstruction algorithm to correct motion artifacts by using a wavelet-decomposed back-propagation neural network. The experimental data was achieved from six subjects and the results were analyzed in comparing conventional algorithms such as HRF smoothing, wavelet denoising, and wavelet MDL. The performance of the proposed method was proven experimentally using the graphical results of the corrected fNIRS signal, CNR that is a performance evaluation index, and the brain activation map.

Genome-wide association study reveals candidate genes related to low temperature tolerance in rice (Oryza sativa) during germination.

In this study, relative germination percentage (RGP) and delayed mean germination time (DMGT) were measured in various rice accessions at the germination stage and carried out association analysis to identify candidate genes related to low temperature germination (LTG) using a natural population comprising 137 rice cultivars and inbred lines selected from the Korean rice core set. Genome-wide association study using ~ 1.44 million high-quality SNPs, which were identified by re-sequencing all rice collections, revealed 48 candidate genes on chromosome 10 and 55 candidate genes on chromosome 11 in the high peak SNP sites of associated loci for RGP and DMGT, respectively. By detecting highly associated variations located inside genic regions and performing functional annotation of the genes, we detected 23 candidate genes for RGP and 18 genes for DMGT for LTG. In addition, the haplotype and sequence analysis of the candidate gene (Os10g0371100) with RGP trait and the candidate gene (Os11t0104240-00) with DMGT revealed correlation between sequences of functional variations and phenotypes. Several novel LTG-related candidate genes previously were known for the function during rice germination and uncovered their substantial natural variations. These candidate genes represent valuable resources for molecular breeding and genetic improvement of cold tolerance during rice germination.

Cortical activation pattern for grasping during observation, imagery, execution, FES, and observation-FES integrated BCI: an fNIRS pilot study.

Passive movement, action observation and motor imagery as well as motor execution have been suggested to facilitate the motor function of human brain. The purpose of this study is to investigate the cortical activation patterns of these four modes using a functional near-infrared spectroscopy (fNIRS) system. Seven healthy volunteers underwent optical brain imaging by fNIRS. Passive movements were provided by a functional electrical stimulation (FES). Results demonstrated that while all movement modes commonly activated premotor cortex, there were considerable differences between modes. The pattern of neural activation in motor execution was best resembled by passive movement, followed by motor imagery, and lastly by action observation. This result indicates that action observation may be the least preferred way to activate the sensorimotor cortices. Thus, in order to show the feasibility of motor facilitation by a brain computer interface (BCI) for an extreme case, we paradoxically adopted the observation as a control input of the BCI. An observation-FES integrated BCI activated sensorimotor system stronger than observation but slightly weaker than FES. This limitation should be overcome to utilize the observation-FES integrated BCI as an active motor training method.

Minimizing inter-subject variability in fNIRS-based brain-computer interfaces via multiple-kernel support vector learning.

Brain signal variation across different subjects and sessions significantly impairs the accuracy of most brain-computer interface (BCI) systems. Herein, we present a classification algorithm that minimizes such variation, using linear programming support-vector machines (LP-SVM) and their extension to multiple kernel learning methods. The minimization is based on the decision boundaries formed in classifiers' feature spaces and their relation to BCI variation. Specifically, we estimate subject/session-invariant features in the reproducing kernel Hilbert spaces (RKHS) induced with Gaussian kernels. The idea is to construct multiple subject/session-dependent RKHS and to perform classification with LP-SVMs. To evaluate the performance of the algorithm, we applied it to oxy-hemoglobin data sets acquired from eight sessions and seven subjects as they performed two different mental tasks. Results show that our classifiers maintain good performance when applied to random patterns across varying sessions/subjects.