Neural network enabled nanoplasmonic hydrogen sensors with 100 ppm limit of detection in humid air.

Environmental humidity variations are ubiquitous and high humidity characterizes fuel cell and electrolyzer operation conditions. Since hydrogen-air mixtures are highly flammable, humidity tolerant H2 sensors are important from safety and process monitoring perspectives. Here, we report an optical nanoplasmonic hydrogen sensor operated at elevated temperature that combined with Deep Dense Neural Network or Transformer data treatment involving the entire spectral response of the sensor enables a 100 ppm H2 limit of detection in synthetic air at 80% relative humidity. This significantly exceeds the <1000 ppm US Department of Energy performance target. Furthermore, the sensors pass the ISO 26142:2010 stability requirement in 80% relative humidity in air down to 0.06% H2 and show no signs of performance loss after 140 h continuous operation. Our results thus demonstrate the potential of plasmonic hydrogen sensors for use in high humidity and how neural-network-based data treatment can significantly boost their performance.

Estimating multimodal brain variability in schizophrenia spectrum disorders: A worldwide ENIGMA study.

Objective: Schizophrenia is a multifaceted disorder associated with structural brain heterogeneity. Despite its relevance for identifying illness subtypes and informative biomarkers, structural brain heterogeneity in schizophrenia remains incompletely understood. Therefore, the objective of this study was to provide a comprehensive insight into the structural brain heterogeneity associated with schizophrenia. Methods: This meta- and mega-analysis investigated the variability of multimodal structural brain measures of white and gray matter in individuals with schizophrenia versus healthy controls. Using the ENIGMA dataset of MRI-based brain measures from 22 international sites with up to 6139 individuals for a given brain measure, we examined variability in cortical thickness, surface area, folding index, subcortical volume and fractional anisotropy. Results: We found that individuals with schizophrenia are distinguished by higher heterogeneity in the frontotemporal network with regard to multimodal structural measures. Moreover, individuals with schizophrenia showed higher homogeneity of the folding index, especially in the left parahippocampal region. Conclusions: Higher multimodal heterogeneity in frontotemporal regions potentially implies different subtypes of schizophrenia that converge on impaired frontotemporal interaction as a core feature of the disorder. Conversely, more homogeneous folding patterns in the left parahippocampal region might signify a consistent characteristic of schizophrenia shared across subtypes. These findings underscore the importance of structural brain variability in advancing our neurobiological understanding of schizophrenia, and aid in identifying illness subtypes as well as informative biomarkers.

Cortical morphology in patients with the deficit and non-deficit syndrome of schizophrenia: a worldwide meta- and mega-analyses.

Converging evidence suggests that schizophrenia (SZ) with primary, enduring negative symptoms (i.e., Deficit SZ (DSZ)) represents a distinct entity within the SZ spectrum while the neurobiological underpinnings remain undetermined. In the largest dataset of DSZ and Non-Deficit (NDSZ), we conducted a meta-analysis of data from 1560 individuals (168 DSZ, 373 NDSZ, 1019 Healthy Controls (HC)) and a mega-analysis of a subsampled data from 944 individuals (115 DSZ, 254 NDSZ, 575 HC) collected across 9 worldwide research centers of the ENIGMA SZ Working Group (8 in the mega-analysis), to clarify whether they differ in terms of cortical morphology. In the meta-analysis, sites computed effect sizes for differences in cortical thickness and surface area between SZ and control groups using a harmonized pipeline. In the mega-analysis, cortical values of individuals with schizophrenia and control participants were analyzed across sites using mixed-model ANCOVAs. The meta-analysis of cortical thickness showed a converging pattern of widespread thinner cortex in fronto-parietal regions of the left hemisphere in both DSZ and NDSZ, when compared to HC. However, DSZ have more pronounced thickness abnormalities than NDSZ, mostly involving the right fronto-parietal cortices. As for surface area, NDSZ showed differences in fronto-parietal-temporo-occipital cortices as compared to HC, and in temporo-occipital cortices as compared to DSZ. Although DSZ and NDSZ show widespread overlapping regions of thinner cortex as compared to HC, cortical thinning seems to better typify DSZ, being more extensive and bilateral, while surface area alterations are more evident in NDSZ. Our findings demonstrate for the first time that DSZ and NDSZ are characterized by different neuroimaging phenotypes, supporting a nosological distinction between DSZ and NDSZ and point toward the separate disease hypothesis.

Toward a reliable detection of arachnophobia: subjective, behavioral, and neurophysiological measures of fear response.

Introduction: The administration of questionnaires presents an easy way of obtaining important knowledge about phobic patients. However, it is not well known how these subjective measurements correspond to the patient's objective condition. Our study aimed to compare scores on questionnaires and image evaluation to the objective measurements of the behavioral approach test (BAT) and the neurophysiological effect of spiders extracted from fMRI measurements. The objective was to explore how reliably subjective statements about spiders and physiological and behavioral parameters discriminate between phobics and non-phobics, and what are the best predictors of overall brain activation. Methods: Based on a clinical interview, 165 subjects were assigned to either a "phobic" or low-fear "control" group. Finally, 30 arachnophobic and 32 healthy control subjects (with low fear of spiders) participated in this study. They completed several questionnaires (SPQ, SNAQ, DS-R) and underwent a behavioral approach test (BAT) with a live tarantula. Then, they were measured in fMRI while watching blocks of pictures including spiders and snakes. Finally, the respondents rated all the visual stimuli according to perceived fear. We proposed the Spider Fear Index (SFI) as a value characterizing the level of spider fear, computed based on the fMRI measurements. We then treated this variable as the "neurophysiological effect of spiders" and examined its contribution to the respondents' fear ratings of the stimuli seen during the fMRI using the redundancy analysis (RDA). Results: The results for fear ranks revealed that the SFI, SNAQ, DS-R, and SPQ scores had a significant effect, while BAT and SPQ scores loaded in the same direction of the first multivariate axis. The SFI was strongly correlated with both SPQ and BAT scores in the pooled sample of arachnophobic and healthy control subjects. Discussion: Both SPQ and BAT scores have a high informative value about the subject's fear of spiders and together with subjective emotional evaluation of picture stimuli can be reliable predictors of spider phobia. These parameters provide easy and non-expensive but reliable measurement wherever more expensive devices such as magnetic resonance are not available. However, SFI still reflects individual variability within the phobic group, identifying individuals with higher brain activation, which may relate to more severe phobic reactions or other sources of fMRI signal variability.

Switchable PNIPAm/PPyNT Hydrogel for Smart Supercapacitors: External Control of Capacitance for Pulsed Energy Generation or Prolongation of Discharge Time.

Supercapacitors based on nonresponsive polymer hydrogels are gaining significant attention due to their fabrication simplicity and high potential for wearable electronics. However, the use of smart hydrogels in supercapacitor design remains unexplored. In this work, a smart externally controlled supercapacitor based on a temperature-responsive hydrogel doped with polypyrrole nanotubes (PPyNTs) is proposed. The redistribution of PPyNTs in the poly(N-isopropylacrylamide) (PNIPAm) hydrogel can be reversibly controlled by light illumination or temperature increase, leading to on-demand formation/disruption of the nanotube conductive network, due to release/entrapping of the nanotubes from PNIPAm globule volume on surface. The switchable material was introduced in a supercapacitor design as an active and smart electrode, responsible for external control of charge transport and storage. The created device showed a switchable supercapacitor performance with an ability to significantly and rapidly change capacity under heating/cooling or light illumination. The external trigger was applied for static or dynamic control of supercapacitor behavior: prolongation of discharge time (with constant electric loading) or vice-versa pronounced acceleration of supercapacitor discharge. The proposed smart material-based supercapacitor can find a range of attractive applications in backup energy storage or high power pulse generation.

Simultaneous fMRI-EEG-Based Characterisation of NREM Parasomnia Disease: Methods and Limitations.

Functional magnetic resonance imaging (fMRI) techniques and electroencephalography (EEG) were used to investigate sleep with a focus on impaired arousal mechanisms in disorders of arousal (DOAs). With a prevalence of 2-4% in adults, DOAs are significant disorders that are currently gaining attention among physicians. The paper describes a simultaneous EEG and fMRI experiment conducted in adult individuals with DOAs (n=10). Both EEG and fMRI data were validated by reproducing well established EEG and fMRI associations. A method for identification of both brain functional areas and EEG rhythms associated with DOAs in shallow sleep was designed. Significant differences between patients and controls were found in delta, theta, and alpha bands during awakening epochs. General linear models of the blood-oxygen-level-dependent signal have shown the secondary visual cortex and dorsal posterior cingulate cortex to be associated with alpha spectral power fluctuations, and the precuneus with delta spectral power fluctuations, specifically in patients and not in controls. Future EEG-fMRI sleep studies should also consider subject comfort as an important aspect in the experimental design.

New Insights towards High-Temperature Ethanol-Sensing Mechanism of ZnO-Based Chemiresistors.

In this work, we investigate ethanol (EtOH)-sensing mechanisms of a ZnO nanorod (NRs)-based chemiresistor using a near-ambient-pressure X-ray photoelectron spectroscopy (NAP-XPS). First, the ZnO NRs-based sensor was constructed, showing good performance on interaction with 100 ppm of EtOH in the ambient air at 327 °C. Then, the same ZnO NRs film was investigated by NAP-XPS in the presence of 1 mbar oxygen, simulating the ambient air atmosphere and O2/EtOH mixture at the same temperature. The partial pressure of EtOH was 0.1 mbar, which corresponded to the partial pressure of 100 ppm of analytes in the ambient air. To better understand the EtOH-sensing mechanism, the NAP-XPS spectra were also studied on exposure to O2/EtOH/H2O and O2/MeCHO (MeCHO = acetaldehyde) mixtures. Our results revealed that the reaction of EtOH with chemisorbed oxygen on the surface of ZnO NRs follows the acetaldehyde pathway. It was also demonstrated that, during the sensing process, the surface becomes contaminated by different products of MeCHO decomposition, which decreases dc-sensor performance. However, the ac performance does not seem to be affected by this phenomenon.

Typicality of functional connectivity robustly captures motion artifacts in rs-fMRI across datasets, atlases, and preprocessing pipelines.

Functional connectivity analysis of resting-state fMRI data has recently become one of the most common approaches to characterizing individual brain function. It has been widely suggested that the functional connectivity matrix is a useful approximate representation of the brain's connectivity, potentially providing behaviorally or clinically relevant markers. However, functional connectivity estimates are known to be detrimentally affected by various artifacts, including those due to in-scanner head motion. Moreover, as individual functional connections generally covary only very weakly with head motion estimates, motion influence is difficult to quantify robustly, and prone to be neglected in practice. Although the use of individual estimates of head motion, or group-level correlation of motion and functional connectivity has been suggested, a sufficiently sensitive measure of individual functional connectivity quality has not yet been established. We propose a new intuitive summary index, Typicality of Functional Connectivity, to capture deviations from standard brain functional connectivity patterns. In a resting-state fMRI dataset of 245 healthy subjects, this measure was significantly correlated with individual head motion metrics. The results were further robustly reproduced across atlas granularity, preprocessing options, and other datasets, including 1,081 subjects from the Human Connectome Project. In principle, Typicality of Functional Connectivity should be sensitive also to other types of artifacts, processing errors, and possibly also brain pathology, allowing extensive use in data quality screening and quantification in functional connectivity studies as well as methodological investigations.

Personality reflection in the brain's intrinsic functional architecture remains elusive.

In the last years, there has been a considerable increase of research into the neuroimaging correlates of inter-individual temperament and character variability-an endeavour for which the term 'personality neuroscience' was coined. Among other neuroimaging modalities and approaches, substantial work focuses on functional connectivity in resting state (rs-FC) functional magnetic resonance imaging data. In the current paper, we set out to independently query the questions asked in a highly cited study that reported a range of functional connectivity correlates of personality dimensions assessed by the widely used 'Big Five' Personality Inventory. Using a larger sample (84 subjects) and an equivalent data analysis pipeline, we obtained widely disagreeing results compared to the original study. Overall, the results were in line with the hypotheses of no relation between functional connectivity and personality, when more precise permutation-based multiple testing procedures were applied. The results demonstrate that as with other neuroimaging studies, great caution should be applied when interpreting the findings, among other reasons due to multiple testing problem involved at several levels in many neuroimaging studies. Of course, the current study results can not ultimately disprove the existence of some link between personality and brain's intrinsic functional architecture, but clearly shows that its form is very likely different and much more subtle and elusive than was previously reported.

An overlapping pattern of cerebral cortical thinning is associated with both positive symptoms and aggression in schizophrenia via the ENIGMA consortium.

BACKGROUND: Positive symptoms are a useful predictor of aggression in schizophrenia. Although a similar pattern of abnormal brain structures related to both positive symptoms and aggression has been reported, this observation has not yet been confirmed in a single sample. METHOD: To study the association between positive symptoms and aggression in schizophrenia on a neurobiological level, a prospective meta-analytic approach was employed to analyze harmonized structural neuroimaging data from 10 research centers worldwide. We analyzed brain MRI scans from 902 individuals with a primary diagnosis of schizophrenia and 952 healthy controls. RESULTS: The result identified a widespread cortical thickness reduction in schizophrenia compared to their controls. Two separate meta-regression analyses revealed that a common pattern of reduced cortical gray matter thickness within the left lateral temporal lobe and right midcingulate cortex was significantly associated with both positive symptoms and aggression. CONCLUSION: These findings suggested that positive symptoms such as formal thought disorder and auditory misperception, combined with cognitive impairments reflecting difficulties in deploying an adaptive control toward perceived threats, could escalate the likelihood of aggression in schizophrenia.

Phthalocyanine Photoregeneration for Low Power Consumption Chemiresistors.

It is well-known that the applicability of phthalocyanine chemiresistors suffers from long recovery time after NO2 exposure. This circumstance enforces the necessity to operate the sensors at elevated temperatures (150-200 °C), which shortens the sensor lifetime and increases its power consumption (regardless, a typical measurement period is longer than 15 min). In this paper, we propose a new method for fast and effective recovery by UV-vis illumination at a low temperature (55 °C). The method is based on short illumination following short NO2 exposure. To support and optimize the method, we investigated the effects of light in the wavelength and intensity ranges of 375-850 nm and 0.2-0.8 mW/mm2, respectively, on the rate of NO2 desorption from the phthalocyanine sensitive layer during the recovery period. This investigation was carried out for a set of phthalocyanine materials (ZnPc, CuPc, H2Pc, PbPc, and FePc) operating at slightly elevated temperatures (55-100 °C) and was further supported by the analysis of UV-vis and FTIR spectral changes. We found out that the light with the wavelength shorter than 550 nm significantly accelerates the NO2 desorption from ZnPc, CuPc, and FePc, and allows bringing the measurement period under 2 min and decreasing the sensor power consumption by 75%. Possible mechanisms of the light-stimulated desorption are discussed.

BRAD: Software for BRain Activity Detection from hemodynamic response.

BACKGROUND AND OBJECTIVE: Precise estimation of neuronal activity from neuroimaging data is one of the central challenges of the application of noninvasive neuroimaging methods. One of the widely used methods for studying brain activity is functional magnetic resonance imaging, which is a neuroimaging procedure that measures brain activity based on the blood oxygenation level dependent effect. The blood oxygenation level dependent signal can be modeled as a linear convolution of a hemodynamic response function with an input signal corresponding to the neuronal activity. Estimating such input signals is a complicated problem. METHODS: We present a software tool for estimation of brain neuronal activity, which uses a combination of Wiener filtering with deconvolution methods, including the least absolute shrinkage and selection operator, the ordinary least squares method, and the Dantzig selector. The latter two are equipped with both established selection criteria (Akaike and Bayesian information criterion) as well as newly developed mixture criteria for selection of activations. RESULTS: The software tool was tested on two types of data: measurements during basic visual experiments and during complex naturalistic audiovisual stimulation (watching a movie segment). During testing the software showed reasonable results, with the mixture criteria performing well for temporally extended activations. CONCLUSIONS: The presented software tool can be used for estimation, visualization, and analysis of brain neuronal activity from functional magnetic resonance imaging blood oxygenation level dependent measurements. The implemented methods provide valid results not only in the sparse activity scenario studied previously but also for temporally extended activations.

Small-world bias of correlation networks: From brain to climate.

Complex systems are commonly characterized by the properties of their graph representation. Dynamical complex systems are then typically represented by a graph of temporal dependencies between time series of state variables of their subunits. It has been shown recently that graphs constructed in this way tend to have relatively clustered structure, potentially leading to spurious detection of small-world properties even in the case of systems with no or randomly distributed true interactions. However, the strength of this bias depends heavily on a range of parameters and its relevance for real-world data has not yet been established. In this work, we assess the relevance of the bias using two examples of multivariate time series recorded in natural complex systems. The first is the time series of local brain activity as measured by functional magnetic resonance imaging in resting healthy human subjects, and the second is the time series of average monthly surface air temperature coming from a large reanalysis of climatological data over the period 1948-2012. In both cases, the clustering in the thresholded correlation graph is substantially higher compared with a realization of a density-matched random graph, while the shortest paths are relatively short, showing thus distinguishing features of small-world structure. However, comparable or even stronger small-world properties were reproduced in correlation graphs of model processes with randomly scrambled interconnections. This suggests that the small-world properties of the correlation matrices of these real-world systems indeed do not reflect genuinely the properties of the underlying interaction structure, but rather result from the inherent properties of correlation matrix.