Applying Quantum Cascade Laser Spectroscopy in Plasma Diagnostics

The considerably higher power and wider frequency coverage available from quantum cascade lasers (QCLs) in comparison to lead salt diode lasers has led to substantial advances when QCLs are used in pure and applied infrared spectroscopy. Furthermore, they can be used in both pulsed and continuous wave (cw) operation, opening up new possibilities in quantitative time resolved applications in plasmas both in the laboratory and in industry as shown in this article. However, in order to determine absolute concentrations accurately using pulsed QCLs, careful attention has to be paid to features like power saturation phenomena. Hence, we begin with a discussion of the non-linear effects which must be considered when using short or long pulse mode operation. More recently, cw QCLs have been introduced which have the advantage of higher power, better spectral resolution and lower fluctuations in light intensity compared to pulsed devices. They have proved particularly useful in sensing applications in plasmas when very low concentrations have to be monitored. Finally, the use of cw external cavity QCLs (EC-QCLs) for multi species detection is described, using a diagnostics study of a methane/nitrogen plasma as an example. The wide frequency coverage of this type of QCL laser, which is significantly broader than from a distributed feedback QCL (DFB-QCL), is a substantial advantage for multi species detection. Therefore, cw EC-QCLs are state of the art devices and have enormous potential for future plasma diagnostic studies

Identifying the neurophysiological effects of memory-enhancing amygdala stimulation using interpretable machine learning

BACKGROUND: Direct electrical stimulation of the amygdala can enhance declarative memory for specific events. An unanswered question is what underlying neurophysiological changes are induced by amygdala stimulation. OBJECTIVE: To leverage interpretable machine learning to identify the neurophysiological processes underlying amygdala-mediated memory, and to develop more efficient neuromodulation technologies. METHOD: Patients with treatment-resistant epilepsy and depth electrodes placed in the hippocampus and amygdala performed a recognition memory task for neutral images of objects. During the encoding phase, 160 images were shown to patients. Half of the images were followed by brief low-amplitude amygdala stimulation. For local field potentials (LFPs) recorded from key medial temporal lobe structures, feature vectors were calculated by taking the average spectral power in canonical frequency bands, before and after stimulation, to train a logistic regression classification model with elastic net regularization to differentiate brain states. RESULTS: Classifying the neural states at the time of encoding based on images subsequently remembered versus not-remembered showed that theta and slow-gamma power in the hippocampus were the most important features predicting subsequent memory performance. Classifying the post-image neural states at the time of encoding based on stimulated versus unstimulated trials showed that amygdala stimulation led to increased gamma power in the hippocampus. CONCLUSION: Amygdala stimulation induced pro-memory states in the hippocampus to enhance subsequent memory performance. Interpretable machine learning provides an effective tool for investigating the neurophysiological effects of brain stimulation

Episodic memory after trauma exposure: Medial temporal lobe function is positively related to re-experiencing and inversely related to negative affect symptoms

Hippocampal structure is particularly sensitive to trauma and other stressors. However, previous findings linking hippocampal function with trauma-related psychopathology have been mixed. Heterogeneity in psychological responses to trauma has not been considered with respect to hippocampal function and may contribute to mixed findings. To address these issues, we examined associations between data-driven symptom dimensions and episodic memory formation, a key function of the hippocampus, in a trauma-exposed sample. Symptom dimensions were defined using principal components analysis (PCA) in 3881 trauma-exposed African-American women recruited from primary care waiting rooms of a large urban hospital. Hippocampal and amygdala function were subsequently investigated in an fMRI study of episodic memory formation in a subset of 54 women. Participants viewed scenes with neutral, negative, and positive content during fMRI, and completed a delayed cued recall task. PCA analysis produced five symptom dimensions interpreted as reflecting negative affect, somatic symptoms, re-experiencing, hyper-arousal, and numbing. Re-experiencing was the only symptom type associated with hippocampal function, predicting increased memory encoding-related activation in the hippocampus as well as the amygdala. In contrast, the negative affect component predicted lower amygdala activation for subsequently recalled scenes, and lower functional coupling with other important memory-related regions including the precuneus, inferior frontal gyrus, and occipital cortex. Symptom dimensions were not related to hippocampal volume. The fMRI findings for re-experiencing versus negative affect parallel differences in behavioral memory phenomena in PTSD versus MDD, and highlight a need for more complex models of trauma-related pathology

Multisite reliability of MR-based functional connectivity

Recent years have witnessed an increasing number of multisite MRI functional connectivity (fcMRI) studies. While multisite studies are an efficient way to speed up data collection and increase sample sizes, especially for rare clinical populations, any effects of site or MRI scanner could ultimately limit power and weaken results. Little data exists on the stability of functional connectivity measurements across sites and sessions. In this study, we assess the influence of site and session on resting state functional connectivity measurements in a healthy cohort of traveling subjects (8 subjects scanned twice at each of 8 sites) scanned as part of the North American Prodrome Longitudinal Study (NAPLS). Reliability was investigated in three types of connectivity analyses: (1) seed-based connectivity with posterior cingulate cortex (PCC), right motor cortex (RMC), and left thalamus (LT) as seeds; (2) the intrinsic connectivity distribution (ICD), a voxel-wise connectivity measure; and (3) matrix connectivity, a whole-brain, atlas-based approach assessing connectivity between nodes. Contributions to variability in connectivity due to subject, site, and day-of-scan were quantified and used to assess between-session (test-retest) reliability in accordance with Generalizability Theory. Overall, no major site, scanner manufacturer, or day-of-scan effects were found for the univariate connectivity analyses; instead, subject effects dominated relative to the other measured factors. However, summaries of voxel-wise connectivity were found to be sensitive to site and scanner manufacturer effects. For all connectivity measures, although subject variance was three times the site variance, the residual represented 60–80% of the variance, indicating that connectivity differed greatly from scan to scan independent of any of the measured factors (i.e., subject, site, and day-of-scan). Thus, for a single 5 min scan, reliability across connectivity measures was poor (ICC=0.07–0.17), but increases with increasing scan duration (ICC=0.21–0.36 at 25 min). The limited effects of site and scanner manufacturer support the use of multisite studies, such as NAPLS, as a viable means of collecting data on rare populations and increasing power in univariate functional connectivity studies. However, the results indicate that aggregation of fcMRI data across longer scan durations is necessary to increase the reliability of connectivity estimates at the single-subject level

The Arabidopsis JAGGED LATERAL ORGANS (JLO) gene sensitizes plants to auxin

Plant growth and development of new organs depend on the continuous activity of the meristems. In the shoot, patterns of organ initiation are determined by PINFORMED (PIN)-dependent auxin distribution, while the undifferentiated state of meristem cells requires activity of KNOTTED LIKE HOMEOBOX (KNOX) transcription factors. Cell proliferation and differentiation of the root meristem are regulated by the largely antagonistic functions of auxin and cytokinins. It has previously been shown that the transcription factor JAGGED LATERAL ORGANS (JLO), a member of the LATERAL ORGAN BOUNDARY DOMAIN (LBD) family, coordinates KNOX and PIN expression in the shoot and promotes root meristem growth. Here we show that JLO is required for the establishment of the root stem cell niche, where it interacts with the auxin/PLETHORA pathway. Auxin signaling involves the AUX/IAA co-repressor proteins, ARF transcription factors and F-box receptors of the TIR1/AFB1–5 family. Because jlo mutants fail to degrade the AUX/IAA protein BODENLOS, root meristem development is inhibited. We also demonstrate that the expression levels of two auxin receptors, TIR1 and AFB1, are controlled by JLO dosage, and that the shoot and root defects of jlo mutants are alleviated in jlo plants expressing TIR1 and AFB1 from a transgene. The finding that the auxin sensitivity of a plant can be differentially regulated through control of auxin receptor expression can explain how different developmental processes can be integrated by the activity of a key transcription factor

Recursive Cluster Elimination Based Support Vector Machine for Disease State Prediction Using Resting State Functional and Effective Brain Connectivity

Brain state classification has been accomplished using features such as voxel intensities, derived from functional magnetic resonance imaging (fMRI) data, as inputs to efficient classifiers such as support vector machines (SVM) and is based on the spatial localization model of brain function. With the advent of the connectionist model of brain function, features from brain networks may provide increased discriminatory power for brain state classification.In this study, we introduce a novel framework where in both functional connectivity (FC) based on instantaneous temporal correlation and effective connectivity (EC) based on causal influence in brain networks are used as features in an SVM classifier. In order to derive those features, we adopt a novel approach recently introduced by us called correlation-purged Granger causality (CPGC) in order to obtain both FC and EC from fMRI data simultaneously without the instantaneous correlation contaminating Granger causality. In addition, statistical learning is accelerated and performance accuracy is enhanced by combining recursive cluster elimination (RCE) algorithm with the SVM classifier. We demonstrate the efficacy of the CPGC-based RCE-SVM approach using a specific instance of brain state classification exemplified by disease state prediction. Accordingly, we show that this approach is capable of predicting with 90.3% accuracy whether any given human subject was prenatally exposed to cocaine or not, even when no significant behavioral differences were found between exposed and healthy subjects.The framework adopted in this work is quite general in nature with prenatal cocaine exposure being only an illustrative example of the power of this approach. In any brain state classification approach using neuroimaging data, including the directional connectivity information may prove to be a performance enhancer. When brain state classification is used for disease state prediction, our approach may aid the clinicians in performing more accurate diagnosis of diseases in situations where in non-neuroimaging biomarkers may be unable to perform differential diagnosis with certainty