Aperiodic and periodic components of oscillatory brain activity in relation to cognition and symptoms in pediatric ADHD.

Children with attention-deficit/hyperactivity disorder show deficits in processing speed, as well as aberrant neural oscillations, including both periodic (oscillatory) and aperiodic (1/f-like) activity, reflecting the pattern of power across frequencies. Both components were suggested as underlying neural mechanisms of cognitive dysfunctions in attention-deficit/hyperactivity disorder. Here, we examined differences in processing speed and resting-state-Electroencephalogram neural oscillations and their associations between 6- and 12-year-old children with (n = 33) and without (n = 33) attention-deficit/hyperactivity disorder. Spectral analyses of the resting-state EEG signal using fast Fourier transform revealed increased power in fronto-central theta and beta oscillations for the attention-deficit/hyperactivity disorder group, but no differences in the theta/beta ratio. Using the parameterization method, we found a higher aperiodic exponent, which has been suggested to reflect lower neuronal excitation-inhibition, in the attention-deficit/hyperactivity disorder group. While fast Fourier transform-based theta power correlated with clinical symptoms for the attention-deficit/hyperactivity disorder group only, the aperiodic exponent was negatively correlated with processing speed across the entire sample. Finally, the aperiodic exponent was correlated with fast Fourier transform-based beta power. These results highlight the different and complementary contribution of periodic and aperiodic components of the neural spectrum as metrics for evaluation of processing speed in attention-deficit/hyperactivity disorder. Future studies should further clarify the roles of periodic and aperiodic components in additional cognitive functions and in relation to clinical status.

Distinct neural activations correlate with maximization of reward magnitude versus frequency.

Choice selection strategies and decision-making are typically investigated using multiple-choice gambling paradigms that require participants to maximize expected value of rewards. However, research shows that performance in such paradigms suffers from individual biases towards the frequency of gains such that users often choose smaller frequent gains over larger rarely occurring gains, also referred to as melioration. To understand the basis of this subjective tradeoff, we used a simple 2-choice reward task paradigm in 186 healthy human adult subjects sampled across the adult lifespan. Cortical source reconstruction of simultaneously recorded electroencephalography suggested distinct neural correlates for maximizing reward magnitude versus frequency. We found that activations in the parahippocampal and entorhinal areas, which are typically linked to memory function, specifically correlated with maximization of reward magnitude. In contrast, maximization of reward frequency was correlated with activations in the lateral orbitofrontal cortices and operculum, typical areas involved in reward processing. These findings reveal distinct neural processes serving reward frequency versus magnitude maximization that can have clinical translational utility to optimize decision-making.

Personalized Machine Learning-Based Prediction of Wellbeing and Empathy in Healthcare Professionals.

Healthcare professionals are known to suffer from workplace stress and burnout, which can negatively affect their empathy for patients and quality of care. While existing research has identified factors associated with wellbeing and empathy in healthcare professionals, these efforts are typically focused on the group level, ignoring potentially important individual differences and implications for individualized intervention approaches. In the current study, we implemented N-of-1 personalized machine learning (PML) to predict wellbeing and empathy in healthcare professionals at the individual level, leveraging ecological momentary assessments (EMAs) and smartwatch wearable data. A total of 47 mood and lifestyle feature variables (relating to sleep, diet, exercise, and social connections) were collected daily for up to three months followed by applying eight supervised machine learning (ML) models in a PML pipeline to predict wellbeing and empathy separately. Predictive insight into the model architecture was obtained using Shapley statistics for each of the best-fit personalized models, ranking the importance of each feature for each participant. The best-fit model and top features varied across participants, with anxious mood (13/19) and depressed mood (10/19) being the top predictors in most models. Social connection was a top predictor for wellbeing in 9/12 participants but not for empathy models (1/7). Additionally, empathy and wellbeing were the top predictors of each other in 64% of cases. These findings highlight shared and individual features of wellbeing and empathy in healthcare professionals and suggest that a one-size-fits-all approach to addressing modifiable factors to improve wellbeing and empathy will likely be suboptimal. In the future, such personalized models may serve as actionable insights for healthcare professionals that lead to increased wellness and quality of patient care.

Towards Personalised Mood Prediction and Explanation for Depression from Biophysical Data.

Digital health applications using Artificial Intelligence (AI) are a promising opportunity to address the widening gap between available resources and mental health needs globally. Increasingly, passively acquired data from wearables are augmented with carefully selected active data from depressed individuals to develop Machine Learning (ML) models of depression based on mood scores. However, most ML models are black box in nature, and hence the outputs are not explainable. Depression is also multimodal, and the reasons for depression may vary significantly between individuals. Explainable and personalised models will thus be beneficial to clinicians to determine the main features that lead to a decline in the mood state of a depressed individual, thus enabling suitable personalised therapy. This is currently lacking. Therefore, this study presents a methodology for developing personalised and accurate Deep Learning (DL)-based predictive mood models for depression, along with novel methods for identifying the key facets that lead to the exacerbation of depressive symptoms. We illustrate our approach by using an existing multimodal dataset containing longitudinal Ecological Momentary Assessments of depression, lifestyle data from wearables and neurocognitive assessments for 14 mild to moderately depressed participants over one month. We develop classification- and regression-based DL models to predict participants' current mood scores-a discrete score given to a participant based on the severity of their depressive symptoms. The models are trained inside eight different evolutionary-algorithm-based optimisation schemes that optimise the model parameters for a maximum predictive performance. A five-fold cross-validation scheme is used to verify the DL model's predictive performance against 10 classical ML-based models, with a model error as low as 6% for some participants. We use the best model from the optimisation process to extract indicators, using SHAP, ALE and Anchors from explainable AI literature to explain why certain predictions are made and how they affect mood. These feature insights can assist health professionals in incorporating personalised interventions into a depressed individual's treatment regimen.

Theta Burst Stimulation Is Not Inferior to High-Frequency Repetitive Transcranial Magnetic Stimulation in Reducing Symptoms of Posttraumatic Stress Disorder in Veterans With Depression: A Retrospective Case Series.

OBJECTIVES: Two commonly used forms of repetitive transcranial magnetic stimulation (rTMS) were recently shown to be equivalent for the treatment of depression: high-frequency stimulation (10 Hz), a protocol that lasts between 19 and 38 minutes, and intermittent theta burst stimulation (iTBS), a protocol that can be delivered in just three minutes. However, it is unclear whether iTBS treatment offers the same benefits as those of standard 10-Hz rTMS for comorbid symptoms such as those seen in posttraumatic stress disorder (PTSD). MATERIALS AND METHODS: In this retrospective case series, we analyzed treatment outcomes in veterans from the Veterans Affairs San Diego Healthcare System who received 10-Hz (n = 47) or iTBS (n = 51)-rTMS treatments for treatment-resistant depression between February 2018 and June 2022. We compared outcomes between these two stimulation protocols in symptoms of depression (using changes in the Patient Health Questionnaire-9 [PHQ-9]) and PTSD (using changes in the PTSD Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, or Patient Checklist [PCL]-5). RESULTS: There was an imbalance of sex between groups (p < 0.05). After controlling for sex, we found no significant difference by stimulation protocol for depression (PHQ-9, F [1,94] = 0.16, p = 0.69, eta-squared = 0.002), confirming the original study previously noted. We also showed no difference by stimulation protocol of changes in PTSD symptoms (PCL-5, F [1,94] = 3.46, p = 0.067, eta-squared = 0.036). The iTBS group showed a decrease from 41.9 ± 4.4 to 25.1 ± 4.9 (a difference of 16.8 points) on the PCL-5 scale whereas the 10-Hz group showed a decrease from 43.6 ± 2.9 to 35.2 ± 3.2 on this scale (a difference of 8.4 points). Follow-up analyses restricting the sample in various ways did not meaningfully change these results (no follow-up analyses showed that there was a significant difference between stimulation protocols). CONCLUSIONS: Although limited by small sample size, nonblind, and pseudorandomized assignment, our data suggest that iTBS is similar to 10-Hz stimulation in inducing reductions in PTSD symptoms and depression in military veterans.

Closed-Loop Neurofeedback of α Synchrony during Goal-Directed Attention.

α Oscillations in sensory cortex, under frontal control, desynchronize during attentive preparation. Here, in a selective attention study with simultaneous EEG in humans of either sex, we first demonstrate that diminished anticipatory α synchrony between the mid-frontal region of the dorsal attention network and ventral visual sensory cortex [frontal-sensory synchrony (FSS)] significantly correlates with greater task performance. Then, in a double-blind, randomized controlled study in healthy adults, we implement closed-loop neurofeedback (NF) of the anticipatory α FSS signal over 10 d of training. We refer to this closed-loop experimental approach of rapid NF integrated within a cognitive task as cognitive NF (cNF). We show that cNF results in significant trial-by-trial modulation of the anticipatory α FSS measure during training, concomitant plasticity of stimulus-evoked α/θ responses, as well as transfer of benefits to response time (RT) improvements on a standard test of sustained attention. In a third study, we implement cNF training in children with attention deficit hyperactivity disorder (ADHD), replicating trial-by-trial modulation of the anticipatory α FSS signal as well as significant improvement of sustained attention RTs. These first findings demonstrate the basic mechanisms and translational utility of rapid cognitive-task-integrated NF.SIGNIFICANCE STATEMENT When humans prepare to attend to incoming sensory information, neural oscillations in the α band (8-14 Hz) undergo desynchronization under the control of prefrontal cortex. Here, in an attention study with electroencephalography, we first show that frontal-sensory synchrony (FSS) of α oscillations during attentive preparation significantly correlates with task performance. Then, in a randomized controlled study in healthy adults, we show that neurofeedback (NF) training of this α FSS signal within the attention task is feasible. We show that this rapid cognitive NF (cNF) approach engenders plasticity of stimulus-evoked neural responses, and improves performance on a standard test of sustained attention. In a final study, we implement cNF in children with attention deficit hyperactivity disorder (ADHD), replicating the improvement of sustained attention found in adults.

Multi-level predictors of depression symptoms in the Adolescent Brain Cognitive Development (ABCD) study.

BACKGROUND: While identifying risk factors for adolescent depression is critical for early prevention and intervention, most studies have sought to understand the role of isolated factors rather than across a broad set of factors. Here, we sought to examine multi-level factors that maximize the prediction of depression symptoms in US children participating in the Adolescent Brain and Cognitive Development (ABCD) study. METHODS: A total of 7,995 participants from ABCD (version 3.0 release) provided complete data at baseline and 1-year follow-up data. Depression symptoms were measured with the Child Behavior Checklist. Predictive features included child demographic, environmental, and structural and resting-state fMRI variables, parental depression history and demographic characteristics. We used linear (elastic net regression, EN) and non-linear (gradient-boosted trees, GBT) predictive models to identify which set of features maximized prediction of depression symptoms at baseline and, separately, at 1-year follow-up. RESULTS: Both linear and non-linear models achieved comparable results for predicting baseline (EN: MAE = 3.757; R2 = 0.156; GBT: MAE = 3.761; R2 = 0.147) and 1-year follow-up (EN: MAE = 4.255; R2 = 0.103; GBT: MAE = 4.262; R2 = 0.089) depression. Parental history of depression, greater family conflict, and shorter child sleep duration were among the top predictors of concurrent and future child depression symptoms across both models. Although resting-state fMRI features were relatively weaker predictors, functional connectivity of the caudate was consistently the strongest neural feature associated with depression symptoms at both timepoints. CONCLUSIONS: Consistent with prior research, parental mental health, family environment, and child sleep quality are important risk factors for youth depression. Functional connectivity of the caudate is a relatively weaker predictor of depression symptoms but may represent a biomarker for depression risk.

Cognitive and Neural Correlates of Loneliness and Wisdom during Emotional Bias.

Loneliness and wisdom have opposing impacts on health and well-being, yet their neuro-cognitive bases have never been simultaneously investigated. In this study of 147 healthy human subjects sampled across the adult lifespan, we simultaneously studied the cognitive and neural correlates of loneliness and wisdom in the context of an emotion bias task. Aligned with the social threat framework of loneliness, we found that loneliness was associated with reduced speed of processing when angry emotional stimuli were presented to bias cognition. In contrast, we found that wisdom was associated with greater speed of processing when happy emotions biased cognition. Source models of electroencephalographic data showed that loneliness was specifically associated with enhanced angry stimulus-driven theta activity in the left transverse temporal region of interest, which is located in the area of the temporoparietal junction (TPJ), while wisdom was specifically related to increased TPJ theta activity during happy stimulus processing. Additionally, enhanced attentiveness to threatening stimuli for lonelier individuals was observed as greater beta activity in left superior parietal cortex, while wisdom significantly related to enhanced happy stimulus-evoked alpha activity in the left insula. Our results demonstrate emotion-context driven modulations in cognitive neural circuits by loneliness versus wisdom.

Impact of Radiation on Male Fertility.

In today's time, environmental aspects, lifestyle changes, and person's health coalesce to form stupendous impact on the fertility. All of us are knowingly or unknowingly exposed to several types of radiation. These can lead to collection of early and delayed adverse effects of which infertility is one. A spurt in the number of cases of male infertility may be attributed to intense exposure to heat, pesticides, radiations, radioactivity, and other hazardous substances. Radiation both ionizing and non-ionizing can lead to adverse effects on spermatogenesis. Though thermal and non-thermal interactions of radiation with biological tissue can't be ruled out, most studies emphasize on the generation of reactive oxygen species (ROS). In addition, radiation pathophysiology also involves the role of kinases in cellular metabolism, endocrine system, genotoxicity, and genomic instability. In this study, we intend to describe a detailed literature on the impact of ionizing and non-ionizing radiation on male reproductive system and understand its consequences leading to the phenomenon of male infertility.

Utility of Cognitive Neural Features for Predicting Mental Health Behaviors.

Cognitive dysfunction underlies common mental health behavioral symptoms including depression, anxiety, inattention, and hyperactivity. In this study of 97 healthy adults, we aimed to classify healthy vs. mild-to-moderate self-reported symptoms of each disorder using cognitive neural markers measured with an electroencephalography (EEG). We analyzed source-reconstructed EEG data for event-related spectral perturbations in the theta, alpha, and beta frequency bands in five tasks, a selective attention and response inhibition task, a visuospatial working memory task, a Flanker interference processing task, and an emotion interference task. From the cortical source activation features, we derived augmented features involving co-activations between any two sources. Logistic regression on the augmented feature set, but not the original feature set, predicted the presence of psychiatric symptoms, particularly for anxiety and inattention with >80% sensitivity and specificity. We also computed current flow closeness and betweenness centralities to identify the "hub" source signal predictors. We found that the Flanker interference processing task was the most useful for assessing the connectivity hubs in general, followed by the inhibitory control go-nogo paradigm. Overall, these interpretable machine learning analyses suggest that EEG biomarkers collected on a rapid suite of cognitive assessments may have utility in classifying diverse self-reported mental health symptoms.

Simultaneous Gut-Brain Electrophysiology Shows Cognition and Satiety Specific Coupling.

Recent studies, using high resolution magnetoencephalography (MEG) and electrogastrography (EGG), have shown that during resting state, rhythmic gastric physiological signals are linked with cortical brain oscillations. Yet, gut-brain coupling has not been investigated with electroencephalography (EEG) during cognitive brain engagement or during hunger-related gut engagement. In this study in 14 young adults (7 females, mean ± SD age 25.71 ± 8.32 years), we study gut-brain coupling using simultaneous EEG and EGG during hunger and satiety states measured in separate visits, and compare responses both while resting as well as during a cognitively demanding working memory task. We find that EGG-EEG phase-amplitude coupling (PAC) differs based on both satiety state and cognitive effort, with greater PAC modulation observed in the resting state relative to working memory. We find a significant interaction between gut satiation levels and cognitive states in the left fronto-central brain region, with larger cognitive demand based differences in the hunger state. Furthermore, strength of PAC correlated with behavioral performance during the working memory task. Altogether, these results highlight the role of gut-brain interactions in cognition and demonstrate the feasibility of these recordings using scalable sensors.

One-year continuous abstinence rate for smoking cessation via telephonic counselling: The Indian scenario.

Tobacco control methods differ by country, with telephonic counseling being one of them. The effectiveness of telephone counseling in smoking cessation has been discussed on several occasions. India's tobacco problem is more complex than that of any other country in the world. To begin with, tobacco is consumed in a variety of ways, and India is a large multilingual country with remarkable cultural diversity. In India, the National Tobacco Quitline Service (NTQLS) is a government-run program. Its data from May 2016 to May 2021 were analyzed retrospectively in this cross-sectional study to determine the prevalence and pattern of tobacco use in India, as well as the abstinence rate for smoking cessation. A total of 4,611,866 calls were received by the Interactive Voice Response system (IVR). The number of calls increased from 600 to 5400 per day after the toll-free number was printed on all tobacco products. Smokeless tobacco use was discovered to be more prevalent, with males significantly more likely to use both smoking and smokeless tobacco. At one month and one year after quitting, 33.42% and 21.9%, respectively, remained tobacco-free. The study emphasizes the efficacy of behavioral counseling in increasing abstinence rates. The printing of a toll-free number on tobacco products is an effective strategy for expanding the operation of quit lines. Despite the challenges of cultural diversity and complex tobacco use, India's quit line service has been able to provide counseling to callers with prolonged abstinence and quit rates comparable to the various quit lines around the world.

Mapping cognitive brain functions at scale.

A fundamental set of cognitive abilities enable humans to efficiently process goal-relevant information, suppress irrelevant distractions, maintain information in working memory, and act flexibly in different behavioral contexts. Yet, studies of human cognition and their underlying neural mechanisms usually evaluate these cognitive constructs in silos, instead of comprehensively in-tandem within the same individual. Here, we developed a scalable, mobile platform, "BrainE" (short for Brain Engagement), to rapidly assay several essential aspects of cognition simultaneous with wireless electroencephalography (EEG) recordings. Using BrainE, we rapidly assessed five aspects of cognition including (1) selective attention, (2) response inhibition, (3) working memory, (4) flanker interference and (5) emotion interference processing, in 102 healthy young adults. We evaluated stimulus encoding in all tasks using the EEG neural recordings, and isolated the cortical sources of the spectrotemporal EEG dynamics. Additionally, we used BrainE in a two-visit study in 24 young adults to investigate the reliability of the neuro-cognitive data as well as its plasticity to transcranial magnetic stimulation (TMS). We found that stimulus encoding on multiple cognitive tasks could be rapidly assessed, identifying common as well as distinct task processes in both sensory and cognitive control brain regions. Event related synchronization (ERS) in the theta (3-7 Hz) and alpha (8-12 Hz) frequencies as well as event related desynchronization (ERD) in the beta frequencies (13-30 Hz) were distinctly observed in each task. The observed ERS/ERD effects were overall anticorrelated. The two-visit study confirmed high test-retest reliability for both cognitive and neural data, and neural responses showed specific TMS protocol driven modulation. We also show that the global cognitive neural responses are sensitive to mental health symptom self-reports. This first study with the BrainE platform showcases its utility in studying neuro-cognitive dynamics in a rapid and scalable fashion.

Bridging M/EEG Source Imaging and Independent Component Analysis Frameworks Using Biologically Inspired Sparsity Priors.

Electromagnetic source imaging (ESI) and independent component analysis (ICA) are two popular and apparently dissimilar frameworks for M/EEG analysis. This letter shows that the two frameworks can be linked by choosing biologically inspired source sparsity priors. We demonstrate that ESI carried out by the sparse Bayesian learning (SBL) algorithm yields source configurations composed of a few active regions that are also maximally independent from one another. In addition, we extend the standard SBL approach to source imaging in two important directions. First, we augment the generative model of M/EEG to include artifactual sources. Second, we modify SBL to allow for efficient model inversion with sequential data. We refer to this new algorithm as recursive SBL (RSBL), a source estimation filter with potential for online and offline imaging applications. We use simulated data to verify that RSBL can accurately estimate and demix cortical and artifactual sources under different noise conditions. Finally, we show that on real error-related EEG data, RSBL can yield single-trial source estimates in agreement with the experimental literature. Overall, by demonstrating that ESI can produce maximally independent sources while simultaneously localizing them in cortical space, we bridge the gap between the ESI and ICA frameworks for M/EEG analysis.

Photocatalytic degradation of cyanide using polyurethane foam immobilized Fe-TCPP-S-TiO2-rGO nano-composite.

An attempt has been made for the treatment of cyanide contaminated wastewater using a S-TiO2@rGO heterogeneous photocatalyst system immobilized on polyurethane foam (PUF) supporting materials. Further, to make the photocatalytic system more efficient and active under visible light, a highly efficient iron porphyrin derivative sensitizer viz. Fe-TCPP was synthesized and employed for cyanide degradation. To investigate the synthesized heterogeneous nano-composite S-TiO2@rGO-FeTCPP photocatalytic system, advanced techniques such as XRD, XPS, FT-IR, PL spectra, UV-vis DRS, FESEM, and EDS were utilized. The photocatalytic performance of the nanocomposite was evaluated in a suspended system and results revealed that about 75% of cyanide degradation was obtained at 100 mg/L of initial cyanide within 2 h. Whereas, at the same condition, more than 91% of cyanide degradation as well as 88% toxicity removal occurred by the PUF immobilized S-TiO2@rGO-FeTCPP solid-state photocatalytic system. First-order kinetics was applied to investigate the degradation of cyanide by the photocatalytic nanocomposite. From the kinetic study, the estimated first-order rate constant (Kf) in a solid-state photocatalytic system of the nanocomposite was 1.7 times superior to that of the suspended system. Further, the rate of photocatalytic activity was nearly 10.8 times greater than that of pure TiO2. This study demonstrated that the immobilized S-TiO2@rGO-FeTCPP photocatalytic system could be an efficient technique for degrading cyanide from industrial effluent.

Trends of folate, vitamin B12 , and homocysteine levels in different trimesters of pregnancy and pregnancy outcomes.

OBJECTIVE: To evaluate the effect of folate and vitamin B12 levels on pregnancy progression and outcomes. METHODS: The present study is a prospective follow up study of 100 pregnant women. Biochemical investigations (plasma homocysteine, folate, and vitamin B12 levels) were performed on all pregnant women in first, second, and third trimesters. Nonparametric tests were used to compare the differences in median levels and odds ratio analysis for the assessment of the risk between the selected biomarkers and adverse pregnancy progression and outcomes. RESULTS: The pregnant women at their first antenatal care visit were found to be predominantly folate replete (97%) and vitamin B12 deficient (60%). Hyperhomocysteinemia in first and second trimesters was found to pose more than 3-fold increased risk for adverse pregnancy outcomes (P = .006 and .0002, respectively). Low birth weight (LBW) was found to be the most common adverse pregnancy outcome (52%), and was significantly associated with vitamin B12 deficiency in the first and second trimesters (82%, P < .0001; 71.4%, P = .04, respectively). CONCLUSION: The vitamin B12 deficiency is more common among Indian pregnant women as compared to folate deficiency. Hyperhomocysteinemia is an independent risk factor for pregnancy complications. Vitamin B12 deficiency in first and second trimesters is associated with LBW babies.

CagA of Helicobacter pylori interacts with and inhibits the serine-threonine kinase PRK2.

CagA is a multifunctional toxin of Helicobacter pylori that is secreted into host epithelial cells by a type IV secretion system. Following host cell translocation, CagA interferes with various host-cell signalling pathways. Most notably this toxin is involved in the disruption of apical-basolateral cell polarity and cell adhesion, as well as in the induction of cell proliferation, migration and cell morphological changes. These are processes that also play an important role in epithelial-to-mesenchymal transition and cancer cell invasion. In fact, CagA is considered as the only known bacterial oncoprotein. The cellular effects are triggered by a variety of CagA activities including the inhibition of serine-threonine kinase Par1b/MARK2 and the activation of tyrosine phosphatase SHP-2. Additionally, CagA was described to affect the activity of Src family kinases and C-terminal Src kinase (Csk) suggesting that interference with multiple cellular kinase- and phosphatase-associated signalling pathways is a major function of CagA. Here, we describe the effect of CagA on protein kinase C-related kinase 2 (PRK2), which acts downstream of Rho GTPases and is known to affect cytoskeletal rearrangements and cell polarity. CagA interacts with PRK2 and inhibits its kinase activity. Because PRK2 has been linked to cytoskeletal rearrangements and establishment of cell polarity, we suggest that CagA may hijack PRK2 to further manipulate cancer-related signalling pathways.

Brevundimonas diminuta mediated alleviation of arsenic toxicity and plant growth promotion in Oryza sativa L.

Arsenic (As), a toxic metalloid adversely affects plant growth in polluted areas. In the present study, we investigated the possibility of improving phytostablization of arsenic through application of new isolated strain Brevundimonas diminuta (NBRI012) in rice plant [Oryza sativa (L.) Var. Sarju 52] at two different concentrations [10ppm (low toxic) and 50ppm (high toxic)] of As. The plant growth promoting traits of bacterial strains revealed the inherent ability of siderophores, phosphate solubilisation, indole acetic acid (IAA), 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase production which may be associated with increased biomass, chlorophyll and MDA content of rice and thereby promoting plant growth. The study also revealed the As accumulation property of NBRI012 strain which could play an important role in As removal from contaminated soil. Furthermore, NBRI012 inoculation significantly restored the hampered root epidermal and cortical cell growth of rice plant and root hair elimination. Altogether our study highlights the multifarious role of B. diminuta in mediating stress tolerance and modulating translocation of As in edible part of rice plant.

Age-equivalent top-down modulation during cross-modal selective attention.

Selective attention involves top-down modulation of sensory cortical areas, such that responses to relevant information are enhanced whereas responses to irrelevant information are suppressed. Suppression of irrelevant information, unlike enhancement of relevant information, has been shown to be deficient in aging. Although these attentional mechanisms have been well characterized within the visual modality, little is known about these mechanisms when attention is selectively allocated across sensory modalities. The present EEG study addressed this issue by testing younger and older participants in three different tasks: Participants attended to the visual modality and ignored the auditory modality, attended to the auditory modality and ignored the visual modality, or passively perceived information presented through either modality. We found overall modulation of visual and auditory processing during cross-modal selective attention in both age groups. Top-down modulation of visual processing was observed as a trend toward enhancement of visual information in the setting of auditory distraction, but no significant suppression of visual distraction when auditory information was relevant. Top-down modulation of auditory processing, on the other hand, was observed as suppression of auditory distraction when visual stimuli were relevant, but no significant enhancement of auditory information in the setting of visual distraction. In addition, greater visual enhancement was associated with better recognition of relevant visual information, and greater auditory distractor suppression was associated with a better ability to ignore auditory distraction. There were no age differences in these effects, suggesting that when relevant and irrelevant information are presented through different sensory modalities, selective attention remains intact in older age.