Detection of Low Resilience Using Data-Driven Effective Connectivity Measures.

Conventional thresholding techniques for graph theory analysis, such as absolute, proportional and mean degree, have often been used in characterizing human brain networks under different mental disorders, such as mental stress. However, these approaches may not always be reliable as conventional thresholding approaches are subjected to human biases. Using a mental resilience study, we investigate if data-driven thresholding techniques such as Global Cost Efficiency (GCE-abs) and Orthogonal Minimum Spanning Trees (OMSTs) could provide equivalent results, whilst eliminating human biases. We implemented Phase Slope Index (PSI) to compute effective brain connectivity, and applied data-driven thresholding approaches to filter the brain networks in order to identify key features of low resilience within a cohort of healthy individuals. Our dataset encompassed resting-state EEG recordings gathered from a total of 36 participants (31 females and 5 males). Relevant features were extracted to train and validate a classifier model (Support Vector Machine, SVM). The detection of low stress resilience among healthy individuals using the SVM model scores an accuracy of 80.6% with GCE-abs, and 75% with OMSTs, respectively.

Genome-wide characterization and analysis of rice DUF247 gene family.

BACKGROUND: The domain of unknown function 247 (DUF247) proteins is involved in plant development and stress response. Rice is an important worldwide cereal crop, although an increasing number of DUF proteins have been identified, the understanding of DUF proteins is still very limited in rice. RESULTS: In this study, we identified 69 genes that encode DUF247 proteins in the rice (Oryza sativa) genome by homology searches and domain prediction. All the OsDUF247 proteins were classified into four major groups (I, II, III and IV) by phylogenetic analysis. Remarkably, OsDUF247 genes clustered on the chromosomes solely show close phylogenetic relationships, suggesting that gene duplications have driven the expansion of the DUF247 gene family in the rice genome. Tissue profile analysis showed that most DUF247 genes expressed at constitutive levels in seedlings, roots, stems, and leaves, except for seven genes (LOC_Os01g21670, LOC_Os03g19700, LOC_Os05g04060, LOC_Os08g26820, LOC_Os08g26840, LOC_Os08g26850 and LOC_Os09g13410) in panicles. These seven genes were induced by various abiotic stress, including cold, drought, heat, hormone treatment, and especially salt, as demonstrated by further experimental analysis. DUF247 proteins contain transmembrane domains located on the membrane, suggesting their significant roles in rice development and adaptation to the environment. CONCLUSIONS: These findings lay the foundation for functional characterizations of DUF247 genes to unravel their exact role in rice cultivars.

Remaining Useful Life Prediction of Rolling Bearings Based on Multi-scale Permutation Entropy and ISSA-LSTM.

The performance of bearings plays a pivotal role in determining the dependability and security of rotating machinery. In intricate systems demanding exceptional reliability and safety, the ability to accurately forecast fault occurrences during operation holds profound significance. Such predictions serve as invaluable guides for crafting well-considered reliability strategies and executing maintenance practices aimed at enhancing reliability. In the real operational life of bearings, fault information often gets submerged within the noise. Furthermore, employing Long Short-Term Memory (LSTM) neural networks for time series prediction necessitates the configuration of appropriate parameters. Manual parameter selection is often a time-consuming process and demands substantial prior knowledge. In order to ensure the reliability of bearing operation, this article investigates the application of three advanced techniques-Maximum Correlation Kurtosis Deconvolution (MCKD), Multi-Scale Permutation Entropy (MPE), and Long Short-Term Memory (LSTM) recurrent neural networks-for the prediction of the remaining useful life (RUL) of rolling bearings. The improved sparrow search algorithm (ISSA) is employed for configuring parameters in the Long Short-Term Memory (LSTM) network. Each technique's principles, methodologies, and applications are comprehensively reviewed, offering insights into their respective strengths and limitations. Case studies and experimental evaluations are presented to assess their performance in RUL prediction. Findings reveal that MCKD enhances fault signatures, MPE captures complexity, and LSTM excels in modeling temporal patterns. The root mean square error of the prediction results is 0.007. The fusion of these techniques offers a comprehensive approach to RUL prediction, leveraging their unique attributes for more accurate and reliable predictions.

Automated Age-Related Macular Degeneration Detector on Optical Coherence Tomography Images Using Slice-Sum Local Binary Patterns and Support Vector Machine.

Artificial intelligence has revolutionised smart medicine, resulting in enhanced medical care. This study presents an automated detector chip for age-related macular degeneration (AMD) using a support vector machine (SVM) and three-dimensional (3D) optical coherence tomography (OCT) volume. The aim is to assist ophthalmologists by reducing the time-consuming AMD medical examination. Using the property of 3D OCT volume, a modified feature vector connected method called slice-sum is proposed, reducing computational complexity while maintaining high detection accuracy. Compared to previous methods, this method significantly reduces computational complexity by at least a hundredfold. Image adjustment and noise removal steps are excluded for classification accuracy, and the feature extraction algorithm of local binary patterns is determined based on hardware consumption considerations. Through optimisation of the feature vector connection method after feature extraction, the computational complexity of SVM detection is significantly reduced, making it applicable to similar 3D datasets. Additionally, the design supports model replacement, allowing users to train and update classification models as needed. Using TSMC 40 nm CMOS technology, the proposed detector achieves a core area of 0.12 mm2 while demonstrating a classification throughput of 8.87 decisions/s at a maximum operating frequency of 454.54 MHz. The detector achieves a final testing classification accuracy of 92.31%.

Microbiota-mediated nitrogen fixation and microhabitat homeostasis in aerial root-mucilage.

BACKGROUND: Plants sustain intimate relationships with diverse microbes. It is well-recognized that these plant-associated microbiota shape individual performance and fitness of host plants, but much remains to be explored regarding how they exert their function and maintain their homeostasis. RESULTS: Here, using pink lady (Heterotis rotundifolia) as a study plant, we investigated the phenomenon of microbiota-mediated nitrogen fixation and elucidated how this process is steadily maintained in the root mucilage microhabitat. Metabolite and microbiota profiling showed that the aerial root mucilage is enriched in carbohydrates and diazotrophic bacteria. Nitrogen isotope-labeling experiments, 15N natural abundance, and gene expression analysis indicated that the aerial root-mucilage microbiota could fix atmospheric nitrogen to support plant growth. While the aerial root mucilage is a hotspot of nutrients, we did not observe high abundance of other environmental and pathogenic microbes inside. We further identified a fungus isolate in mucilage that has shown broad-spectrum antimicrobial activities, but solely allows the growth of diazotrophic bacteria. This "friendly" fungus may be the key driver to maintain nitrogen fixation function in the mucilage microhabitat. Video Abstract CONCLUSION: The discovery of new biological function and mucilage-habitat friendly fungi provides insights into microbial homeostasis maintenance of microenvironmental function and rhizosphere ecology.

IRONMAN peptide interacts with OsHRZ1 and OsHRZ2 to maintain Fe homeostasis in rice.

IRONMAN (IMA) is a family of small peptides which positively regulate plant responses under Fe deficiency. However, the molecular mechanism by which OsIMA1 and OsIMA2 regulate Fe homeostasis in rice is unclear. Here, we reveal that OsIMA1 and OsIMA2 interact with the potential Fe sensors, OsHRZ1 (HAEMERYTHRIN MOTIF-CONTAINING REALLY INTERESTING NEW GENE (RING) AND ZINC-FINGER PROTEIN 1) and OsHRZ2. OsIMA1 and OsIMA2 contain a conserved 17 amino acid C-terminal region which is responsible for the interactions with OsHRZ1 and OsHRZ2. Plants overexpressing OsIMA1 (OsIMA1ox) show increased Fe concentration in seeds and reduced fertility, as observed in the hrz1-2 loss-of-function mutant plants. Moreover, the expression patterns of Fe deficiency inducible genes in the OsIMA1ox plants are the same as those in hrz1-2. Co-expression assays suggest that OsHRZ1 and OsHRZ2 promote the degradation of OsIMA1 proteins. As the interaction partners of OsHRZ1, the OsPRI (POSITIVE REGULATOR OF IRON HOMEOSTASIS) proteins also interact with OsHRZ2. The conserved C-terminal region of four OsPRIs contributes to the interactions with OsHRZ1 and OsHRZ2. An artificial IMA (aIMA) derived from the C-terminal of OsPRI1 can be also degraded by OsHRZ1. Moreover, aIMA overexpressing rice plants accumulate more Fe without reduction of fertility. This work establishes the link between OsIMAs and OsHRZs, and develops a new strategy for Fe fortification in rice.

Emotion Self-Regulation in Neurotic Students: A Pilot Mindfulness-Based Intervention to Assess Its Effectiveness through Brain Signals and Behavioral Data.

Neuroticism has recently received increased attention in the psychology field due to the finding of high implications of neuroticism on an individual's life and broader public health. This study aims to investigate the effect of a brief 6-week breathing-based mindfulness intervention (BMI) on undergraduate neurotic students' emotion regulation. We acquired data of their psychological states, physiological changes, and electroencephalogram (EEG), before and after BMI, in resting states and tasks. Through behavioral analysis, we found the students' anxiety and stress levels significantly reduced after BMI, with p-values of 0.013 and 0.027, respectively. Furthermore, a significant difference between students in emotion regulation strategy, that is, suppression, was also shown. The EEG analysis demonstrated significant differences between students before and after MI in resting states and tasks. Fp1 and O2 channels were identified as the most significant channels in evaluating the effect of BMI. The potential of these channels for classifying (single-channel-based) before and after BMI conditions during eyes-opened and eyes-closed baseline trials were displayed by a good performance in terms of accuracy (~77%), sensitivity (76-80%), specificity (73-77%), and area-under-the-curve (AUC) (0.66-0.8) obtained by k-nearest neighbor (KNN) and support vector machine (SVM) algorithms. Mindfulness can thus improve the self-regulation of the emotional state of neurotic students based on the psychometric and electrophysiological analyses conducted in this study.

bHLH11 inhibits bHLH IVc proteins by recruiting the TOPLESS/TOPLESS-RELATED corepressors.

Iron (Fe) homeostasis is essential for plant growth and development. Many transcription factors (TFs) play pivotal roles in the maintenance of Fe homeostasis. bHLH11 is a negative TF that regulates Fe homeostasis. However, the underlying molecular mechanism remains elusive. Here, we generated two loss-of-function bhlh11 mutants in Arabidopsis (Arabidopsis thaliana), which display enhanced sensitivity to excess Fe, increased Fe accumulation, and elevated expression of Fe deficiency responsive genes. Levels of bHLH11 protein, localized in both the cytoplasm and nucleus, decreased in response to Fe deficiency. Co-expression assays indicated that bHLH IVc TFs (bHLH34, bHLH104, bHLH105, and bHLH115) facilitate the nuclear accumulation of bHLH11. Further analysis indicated that bHLH11 represses the transactivity of bHLH IVc TFs toward bHLH Ib genes (bHLH38, bHLH39, bHLH100, and bHLH101). The two ethylene response factor-associated amphiphilic repression motifs of bHLH11 provided the repression function by recruiting the TOPLESS/TOPLESS-RELATED (TPL/TPRs) corepressors. Correspondingly, the expression of Fe uptake genes increased in the tpr1 tpr4 tpl mutant. Moreover, genetic analysis revealed that bHLH11 has functions independent of FER-LIKE IRON DEFICIENCY-INDUCED TRANSCRIPTION FACTOR. This study provides insights into the complicated Fe homeostasis signaling network.

WPO-Net: Windowed Pose Optimization Network for Monocular Visual Odometry Estimation.

Visual odometry is the process of estimating incremental localization of the camera in 3-dimensional space for autonomous driving. There have been new learning-based methods which do not require camera calibration and are robust to external noise. In this work, a new method that do not require camera calibration called the "windowed pose optimization network" is proposed to estimate the 6 degrees of freedom pose of a monocular camera. The architecture of the proposed network is based on supervised learning-based methods with feature encoder and pose regressor that takes multiple consecutive two grayscale image stacks at each step for training and enforces the composite pose constraints. The KITTI dataset is used to evaluate the performance of the proposed method. The proposed method yielded rotational error of 3.12 deg/100 m, and the training time is 41.32 ms, while inference time is 7.87 ms. Experiments demonstrate the competitive performance of the proposed method to other state-of-the-art related works which shows the novelty of the proposed technique.

Automatic Polyp Segmentation in Colonoscopy Images Using a Modified Deep Convolutional Encoder-Decoder Architecture.

Colorectal cancer has become the third most commonly diagnosed form of cancer, and has the second highest fatality rate of cancers worldwide. Currently, optical colonoscopy is the preferred tool of choice for the diagnosis of polyps and to avert colorectal cancer. Colon screening is time-consuming and highly operator dependent. In view of this, a computer-aided diagnosis (CAD) method needs to be developed for the automatic segmentation of polyps in colonoscopy images. This paper proposes a modified SegNet Visual Geometry Group-19 (VGG-19), a form of convolutional neural network, as a CAD method for polyp segmentation. The modifications include skip connections, 5 × 5 convolutional filters, and the concatenation of four dilated convolutions applied in parallel form. The CVC-ClinicDB, CVC-ColonDB, and ETIS-LaribPolypDB databases were used to evaluate the model, and it was found that our proposed polyp segmentation model achieved an accuracy, sensitivity, specificity, precision, mean intersection over union, and dice coefficient of 96.06%, 94.55%, 97.56%, 97.48%, 92.3%, and 95.99%, respectively. These results indicate that our model performs as well as or better than previous schemes in the literature. We believe that this study will offer benefits in terms of the future development of CAD tools for polyp segmentation for colorectal cancer diagnosis and management. In the future, we intend to embed our proposed network into a medical capsule robot for practical usage and try it in a hospital setting with clinicians.

Classification of Prefrontal Cortex Activity Based on Functional Near-Infrared Spectroscopy Data upon Olfactory Stimulation.

The sense of smell is one of the most important organs in humans, and olfactory imaging can detect signals in the anterior orbital frontal lobe. This study assessed olfactory stimuli using support vector machines (SVMs) with signals from functional near-infrared spectroscopy (fNIRS) data obtained from the prefrontal cortex. These data included odor stimuli and air state, which triggered the hemodynamic response function (HRF), determined from variations in oxyhemoglobin (oxyHb) and deoxyhemoglobin (deoxyHb) levels; photoplethysmography (PPG) of two wavelengths (raw optical red and near-infrared data); and the ratios of data from two optical datasets. We adopted three SVM kernel functions (i.e., linear, quadratic, and cubic) to analyze signals and compare their performance with the HRF and PPG signals. The results revealed that oxyHb yielded the most efficient single-signal data with a quadratic kernel function, and a combination of HRF and PPG signals yielded the most efficient multi-signal data with the cubic function. Our results revealed superior SVM analysis of HRFs for classifying odor and air status using fNIRS data during olfaction in humans. Furthermore, the olfactory stimulation can be accurately classified by using quadratic and cubic kernel functions in SVM, even for an individual participant data set.

Detection of Emotional Sensitivity Using fNIRS Based Dynamic Functional Connectivity.

In this study, we proposed an analytical framework to identify dynamic task-based functional connectivity (FC) features as new biomarkers of emotional sensitivity in nursing students, by using a combination of unsupervised and supervised machine learning techniques. The dynamic FC was measured by functional Near-Infrared Spectroscopy (fNIRS), and computed using a sliding window correlation (SWC) analysis. A k -means clustering technique was applied to derive four recurring connectivity states. The states were characterized by both graph theory and semi-metric analysis. Occurrence probability and state transition were extracted as dynamic FC network features, and a Random Forest (RF) classifier was implemented to detect emotional sensitivity. The proposed method was trialled on 39 nursing students and 19 registered nurses during decision-making, where we assumed registered nurses have developed strategies to cope with emotional sensitivity. Emotional stimuli were selected from International Affective Digitized Sound System (IADS) database. Experiment results showed that registered nurses demonstrated single dominant connectivity state of task-relevance, while nursing students displayed in two states and had higher level of task-irrelevant state connectivity. The results also showed that students were more susceptive to emotional stimuli, and the derived dynamic FC features provided a stronger discriminating power than heart rate variability (accuracy of 81.65% vs 71.03%) as biomarkers of emotional sensitivity. This work forms the first study to demonstrate the stability of fNIRS based dynamic FC states as a biomarker. In conclusion, the results support that the state distribution of dynamic FC could help reveal the differentiating factors between the nursing students and registered nurses during decision making, and it is anticipated that the biomarkers might be used as indicators when developing professional training related to emotional sensitivity.

Automatic colonic polyp detection using integration of modified deep residual convolutional neural network and ensemble learning approaches.

BACKGROUND AND OBJECTIVE: The increased incidence of colorectal cancer (CRC) and its mortality rate have attracted interest in the use of artificial intelligence (AI) based computer-aided diagnosis (CAD) tools to detect polyps at an early stage. Although these CAD tools have thus far achieved a good accuracy level to detect polyps, they still have room to improve further (e.g. sensitivity). Therefore, a new CAD tool is developed in this study to detect colonic polyps accurately. METHODS: In this paper, we propose a novel approach to distinguish colonic polyps by integrating several techniques, including a modified deep residual network, principal component analysis and AdaBoost ensemble learning. A powerful deep residual network architecture, ResNet-50, was investigated to reduce the computational time by altering its architecture. To keep the interference to a minimum, median filter, image thresholding, contrast enhancement, and normalisation techniques were exploited on the endoscopic images to train the classification model. Three publicly available datasets, i.e., Kvasir, ETIS-LaribPolypDB, and CVC-ClinicDB, were merged to train the model, which included images with and without polyps. RESULTS: The proposed approach trained with a combination of three datasets achieved Matthews Correlation Coefficient (MCC) of 0.9819 with accuracy, sensitivity, precision, and specificity of 99.10%, 98.82%, 99.37%, and 99.38%, respectively. CONCLUSIONS: These results show that our method could repeatedly classify endoscopic images automatically and could be used to effectively develop computer-aided diagnostic tools for early CRC detection.

Optimizing Mental Workload Estimation by Detecting Baseline State Using Vector Phase Analysis Approach.

Improper baseline return from the previous task-evoked hemodynamic response (HR) can contribute to a large variation in the subsequent HR, affecting the estimation of mental workload in brain-computer interface systems. In this study, we proposed a method using vector phase analysis to detect the baseline state as being optimal or suboptimal. We hypothesize that selecting neuronal-related HR as observed in the optimal-baseline blocks can lead to an improvement in estimating mental workload. Oxygenated and deoxygenated hemoglobin concentration changes were integrated as parts of the vector phase. The proposed method was applied to a block-design functional near-infrared spectroscopy dataset (total blocks = 1384), measured on 24 subjects performing multiple difficulty levels of mental arithmetic task. Significant differences in hemodynamic signal change were observed between the optimal- and suboptimal-baseline blocks detected using the proposed method. This supports the effectiveness of the proposed method in detecting baseline state for better estimation of mental workload. The results further highlight the need of customized recovery duration. In short, the proposed method offers a practical approach to detect task-evoked signals, without the need of extra probes.

fNIRS-based functional connectivity estimation using semi-metric analysis to study decision making by nursing students and registered nurses.

This study aims to investigate the generalizability of the semi-metric analysis of the functional connectivity (FC) for functional near-infrared spectroscopy (fNIRS) by applying it to detect the dichotomy in differential FC under affective and neutral emotional states in nursing students and registered nurses during decision making. The proposed method employs wavelet transform coherence to construct FC networks and explores semi-metric analysis to extract network redundancy features, which has not been considered in conventional fNIRS-based FC analyses. The trials of the proposed method were performed on 19 nursing students and 19 registered nurses via a decision-making task under different emotional states induced by affective and neutral emotional stimuli. The cognitive activities were recorded using fNIRS, and the emotional stimuli were adopted from the International Affective Digitized Sound System (IADS). The induction of emotional effects was validated by heart rate variability (HRV) analysis. The experimental results by the proposed method showed significant difference (FDR-adjusted p = 0.004) in the nursing students' cognitive FC network under the two different emotional conditions, and the semi-metric percentage (SMP) of the right prefrontal cortex (PFC) was found to be significantly higher than the left PFC (FDR-adjusted p = 0.036). The benchmark method (a typical weighted graph theory analysis) gave no significant results. In essence, the results support that the semi-metric analysis can be generalized and extended to fNIRS-based functional connectivity estimation.

A Unified Analytical Framework With Multiple fNIRS Features for Mental Workload Assessment in the Prefrontal Cortex.

Knowing the actual level of mental workload is important to ensure the efficacy of brain-computer interface (BCI) based cognitive training. Extracting signals from limited area of a brain region might not reveal the actual information. In this study, a functional near-infrared spectroscopy (fNIRS) device equipped with multi-channel and multi-distance measurement capability was employed for the development of an analytical framework to assess mental workload in the prefrontal cortex (PFC). In addition to the conventional features, e.g. hemodynamic slope, we introduced a new feature - deep contribution ratio which is the proportion of cerebral hemodynamics to the fNIRS signals. Multiple sets of features were examined by a simple logical operator to suppress the false detection rate in identifying the activated channels. Using the number of activated channels as input to a linear support vector machine (SVM), the performance of the proposed analytical framework was assessed in classifying three levels of mental workload. The best set of features involves the combination of hemodynamic slope and deep contribution ratio, where the identified number of activated channels returned an average accuracy of 80.6% in predicting mental workload, compared to a single conventional feature (accuracy: 59.8%). This suggests the feasibility of the proposed analytical framework with multiple features as a means towards a more accurate assessment of mental workload in fNIRS-based BCI applications.

Automated Thresholding Method for fNIRS-Based Functional Connectivity Analysis: Validation With a Case Study on Alzheimer's Disease.

While functional integration has been suggested to reflect brain health, non-standardized network thresholding methods complicate network interpretation. We propose a new method to analyze functional near-infrared spectroscopy-based functional connectivity (fNIRS-FC). In this study, we employed wavelet analysis for motion correction and orthogonal minimal spanning trees (OMSTs) to derive the brain connectivity. The proposed method was applied to an Alzheimer's disease (AD) dataset and was compared with a number of well-known thresholding techniques. The results demonstrated that the proposed method outperformed the benchmarks in filtering cost-effective networks and in differentiation between patients with mild AD and healthy controls. The results also supported the proposed method as a feasible technique to analyze fNIRS-FC, especially with cost-efficiency, assortativity and laterality as a set of effective features for the diagnosis of AD.

Oryza sativa FER-LIKE FE DEFICIENCY-INDUCED TRANSCRIPTION FACTOR (OsFIT/OsbHLH156) interacts with OsIRO2 to regulate iron homeostasis.

Iron (Fe) is indispensable for the growth and development of plants. It is well known that FER-LIKE FE DEFICIENCY-INDUCED TRANSCRIPTION FACTOR (FIT) is a key regulator of Fe uptake in Arabidopsis. Here, we identify the Oryza sativa FIT (also known as OsbHLH156) as the interacting partner of IRON-RELATED BHLH TRANSCRIPTION FACTOR 2 (OsIRO2) that is critical for regulating Fe uptake. The OsIRO2 protein is localized in the cytoplasm and nucleus, but OsFIT facilitates the accumulation of OsIRO2 in the nucleus. Loss-of-function mutations of OsFIT result in decreased Fe accumulation, severe Fe-deficiency symptoms, and disrupted expression of Fe-uptake genes. In contrast, OsFIT overexpression promotes Fe accumulation and the expression of Fe-uptake genes. Genetic analyses indicate that OsFIT and OsIRO2 function in the same genetic node. Further analyses suggest that OsFIT and OsIRO2 form a functional transcription activation complex to initiate the expression of Fe-uptake genes. Our findings provide a mechanism understanding of how rice maintains Fe homeostasis.

bHLH121 Functions as a Direct Link that Facilitates the Activation of FIT by bHLH IVc Transcription Factors for Maintaining Fe Homeostasis in Arabidopsis.

Iron (Fe) deficiency is prevalent in plants grown in neutral or alkaline soil. Plants have evolved sophisticated mechanisms that regulate Fe homeostasis, ensuring survival. In Arabidopsis, FER-LIKE IRON DEFICIENCY-INDUCED TRANSCRIPTION FACTOR (FIT) is a crucial regulator of Fe-deficiency response. FIT is activated indirectly by basic helix-loop-helix (bHLH) IVc transcription factors (TFs) under Fe deficiency; however, it remains unclear which protein(s) act as the linker to mediate the activation of FIT by bHLH IVc TFs. In this study, we characterize the functions of bHLH121 and demonstrate that it directly associates with the FIT promoter. We found that loss-of-function mutations of bHLH121 cause severe Fe-deficiency symptoms, reduced Fe accumulation, and disrupted expression of genes associated with Fe homeostasis. Genetic analysis showed that FIT is epistatic to bHLH121 and FIT overexpression partially rescues the bhlh121 mutant. Further investigations revealed that bHLH IVc TFs interact with and promote nuclear accumulation of bHLH121. We demonstrated that bHLH121 has DNA-binding activity and can bind the promoters of the FIT and bHLH Ib genes, but we did not find that it has either direct transcriptional activation or repression activity toward these genes. Meanwhile, we found that bHLH121 functions downstream of and is a direct target of bHLH IVc TFs, and its expression is induced by Fe deficiency in a bHLH IVc-dependent manner. Taken together, these results establish that bHLH121 functions together with bHLH IVc TFs to positively regulate the expression of FIT and thus plays a pivotal role in maintaining Fe homeostasis in Arabidopsis.

Assessing Neural Compensation With Visuospatial Working Memory Load Using Near-Infrared Imaging.

Alzheimer's disease is characterized by the progressive deterioration of cognitive abilities particularly working memory while mild cognitive impairment (MCI) represents its prodrome. It is generally believed that neural compensation is intact in MCI but absent in Alzheimer's disease. This study investigated the effects of increasing task load as a means to induce neural compensation through a novel visual working memory (VSWM) task using functional near-infrared spectroscopy (fNIRS). The bilateral prefrontal cortex (PFC) was explored due to its relevance in VSWM and neural compensation. A total of 31 healthy controls (HC), 12 patients with MCI and 18 patients with mild Alzheimer's disease (mAD) were recruited. Although all groups showed sensitivity in terms of behavioral performance (i.e. score) towards increasing task load (level 1 to 3), only in MCI load effect on cortical response (as measured by fNIRS) was significant. At lower task load, bilateral PFC activation did not differ between MCI and HC. Neural compensation in the form of hyperactivation was only noticeable in MCI with a moderate task load. Lack of hyperactivation in mAD, coupled with significantly poorer task performance across task loads, suggested the inability to compensate due to a greater degree of neurodegeneration. Our findings provided an insight into the interaction of cognitive load theory and neural compensatory mechanisms. The experiment results demonstrated the feasibility of inducing neural compensation with the proposed VSWM task at the right amount of cognitive load. This may provide a promising avenue to develop an effective cognitive training and rehabilitation for dementia population.