Phosphorus quantification in soil using LIBS assisted by laser-induced fluorescence.

Quantification and monitoring of phosphorus in soil plays a critical role in environmentally friendly agriculture, especially in mitigation of phosphorus leakages to water systems and subsequent risk for eutrophication. On the other hand, deficiency in phosphorus would lead to problems in development and growth of cultivated crops. Therefore, monitoring and quantification of phosphorus status in soil is essential. In this work, laser-induced breakdown spectroscopy assisted by laser-induced fluorescence (LIBS-LIF) is introduced for quantification of readily soluble phosphorus in soil and compared to the analytical performance of the conventional LIBS method. Mineral soils with variable phosphorus status were used for the analysis. The calibration curves are plotted to evaluate the detection limit of the soluble phosphorus. Compared results demonstrate improvement in detection limit from 3.74 mg/kg to 0.12 mg/kg for clay soil and from 10.94 mg/kg to 0.27 mg/kg for silt loam/loam soil in LIBS and LIBS-LIF measurements, respectively. For the LIBS-LIF measurement, detection limits are comparable with established chemical soil analyses. The proposed method would substantially reduce required sample preparation and laboratory work compared with conventional phosphorus quantification. In addition, as the calibration curves demonstrate that the calibration for soluble phosphorus holds within a soil type, LIBS-LIF has the potential to be used for high throughput soil analysis.

Intracranial recordings reveal high-frequency activity in the human temporal-parietal cortex supporting non-literal language processing.

Objective: Non-literal expressions such as sarcasm, metaphor and simile refer to words and sentences that convey meanings or intentions that are different and more abstract than literal expressions. Neuroimaging studies have shown activations in a variety of frontal, parietal and temporal brain regions implicated in non-literal language processing. However, neurophysiological correlates of these brain areas underlying non-literal processing remain underexplored. Methods: To address this, we investigated patterns of intracranial EEG activity during non-literal processing by leveraging a unique patient population. Seven neurosurgical patients with invasive electrophysiological monitoring of superficial brain activity were recruited. Intracranial neural responses were recorded over the temporal-parietal junction (TPJ) and its surrounding areas while patients performed a language task. Participants listened to vignettes that ended with non-literal or literal statements and were then asked related questions to which they responded verbally. Results: We found differential neurophysiological activity during the processing of non-literal statements as compared to literal statements, especially in low-Gamma (30-70 Hz) and delta (1-4 Hz) bands. In addition, we found that neural responses related to non-literal processing in the high-gamma band (>70 Hz) were significantly more prominent at TPJ electrodes as compared to non-TPJ (i.e., control) electrodes in most subjects. Moreover, in half of patients, high-gamma activity related to non-literal processing was accompanied by delta-band modulation. Conclusion: These results suggest that both low- and high-frequency electrophysiological activities in the temporal-parietal junction play a crucial role during non-literal language processing in the human brain. The current investigation, utilizing better spatial and temporal resolution of human intracranial electrocorticography, provides a unique opportunity to gain insights into the localized brain dynamics of the TPJ during the processing of non-literal language expressions.

The case of the neglected alphasyllabary: orthographic processing in Devanagari.

We applaud Ram Frost for highlighting the need for multicultural perspectives while developing universal models of visual word recognition. We second Frost's proposal that factors like lexical morphology should be incorporated besides purely orthographic features in modeling word recognition. In support, we provide fresh evidence from Hindi (written in Devanagari), an example of hitherto under-represented alphasyllabic orthographies, in which flexible encoding of aksara (character) position is constrained by the morphological structure of words.

Brain electrical dynamics in speech segmentation depends upon prior experience with the language.

It remains unclear whether the process of speech tracking, which facilitates speech segmentation, reflects top-down mechanisms related to prior linguistic models or stimulus-driven mechanisms, or possibly both. To address this, we recorded electroencephalography (EEG) responses from native and non-native speakers of English that had different prior experience with the English language but heard acoustically identical stimuli. Despite a significant difference in the ability to segment and perceive speech, our EEG results showed that theta-band tracking of the speech envelope did not depend significantly on prior experience with language. However, tracking in the theta-band did show changes across repetitions of the same sentence, suggesting a priming effect. Furthermore, native and non-native speakers showed different phase dynamics at word boundaries, suggesting differences in segmentation mechanisms. Finally, we found that the correlation between higher frequency dynamics reflecting phoneme-level processing and perceptual segmentation of words might depend on prior experience with the spoken language.

Human EEG and Recurrent Neural Networks Exhibit Common Temporal Dynamics During Speech Recognition.

Recent deep-learning artificial neural networks have shown remarkable success in recognizing natural human speech, however the reasons for their success are not entirely understood. Success of these methods might be because state-of-the-art networks use recurrent layers or dilated convolutional layers that enable the network to use a time-dependent feature space. The importance of time-dependent features in human cortical mechanisms of speech perception, measured by electroencephalography (EEG) and magnetoencephalography (MEG), have also been of particular recent interest. It is possible that recurrent neural networks (RNNs) achieve their success by emulating aspects of cortical dynamics, albeit through very different computational mechanisms. In that case, we should observe commonalities in the temporal dynamics of deep-learning models, particularly in recurrent layers, and brain electrical activity (EEG) during speech perception. We explored this prediction by presenting the same sentences to both human listeners and the Deep Speech RNN and considered the temporal dynamics of the EEG and RNN units for identical sentences. We tested whether the recently discovered phenomenon of envelope phase tracking in the human EEG is also evident in RNN hidden layers. We furthermore predicted that the clustering of dissimilarity between model representations of pairs of stimuli would be similar in both RNN and EEG dynamics. We found that the dynamics of both the recurrent layer of the network and human EEG signals exhibit envelope phase tracking with similar time lags. We also computed the representational distance matrices (RDMs) of brain and network responses to speech stimuli. The model RDMs became more similar to the brain RDM when going from early network layers to later ones, and eventually peaked at the recurrent layer. These results suggest that the Deep Speech RNN captures a representation of temporal features of speech in a manner similar to human brain.

The effects of periodic interruptions on cortical entrainment to speech.

Speech is perceived as a continuous stream of words despite consisting of a discontinuous, quasi-periodic signal of interleaved sounds and silences. Speech perception is surprisingly robust to interference by interruption, however speech that is replaced by gaps of silence is difficult to understand. When those silences are filled with noise, the speech is once again perceived as continuous even when the underlying speech sounds are removed completely. This is a phenomenon known as phonemic restoration. Perception of normal speech is accompanied by robust phase-locking of EEG signals to acoustic and linguistic features of speech. In this study we test the theory that interrupting speech with silence impairs perception by interfering with neural speech tracking. Further, we test the theory that we can restore perception and phase-tracking of the original acoustics by inserting noise in the interruptions. We find that disruptions of the acoustic envelope reduce the tracking of both acoustic and phonemic features. By inserting amplitude modulated noise such that the original broadband envelope is restored, we improved perception of the degraded speech and restored the magnitude of the speech tracking response; however, topographic analysis suggests that the neural response to noise-interrupted speech may recruit systematically different brain areas. The acoustic envelope seems to be an important physical component of speech that facilitates the dynamic neural mechanisms for perception of spoken language, particularly in adverse listening conditions.

Swelling behavior of cross-linked dextran hydrogels and preliminary Gliclazide release behavior.

In this study, dextran (Dex) has been cross-linked with epichlorohydrin (Ech) to yield cross-linked hydrogels. These gels were characterized by Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD) analysis, Thermogravimetric analysis (TGA), and Scanning electron microscopy (SEM). The water absorption behavior of gels was studied in simulating gastric fluid (SGF) and simulating intestinal fluid (SIF) at 37°C. The data was interpreted by various kinetic models. The swelling was found to be totally diffusion controlled. The equilibrium data was also used to calculate network parameters. The antidiabetic drug Gliclazide (Glz) was loaded to the gels and its release was investigated in the media of varying pH, to mimic transition from mouth to colon. Finally, the in-vivo study on "Albino Wistar rats" was carried out to investigate the efficiency of the formulations. The drug-loaded hydrogel was found to be quite effective in reducing the glucose level at lower administration frequency as compared to the plain drug.

Controlled release of anti-diabetic drug Gliclazide from poly(caprolactone)/poly(acrylic acid) hydrogels.

Drug Gliclazide (Glz) has limited solubility and low bioavailability. In order to obtain a controlled release of this drug and to improve its bioavailability, the drug has been loaded into poly(caprolactone) (PCL)/poly(acrylic acid) (PAAc) hydrogels, prepared by free radical polymerization of acrylic acid in the presence of poly(caprolactone) in acetone medium using azo-isobutyronitrile as initiator and N,N' methylene bisacrylamide as cross-linking agent. The swelling behaviour of these hydrogels has been investigated in the physiological gastric and intestinal fluids to obtain an optimum composition suitable for delivery of a biologically active compound. The gels were loaded with anti-diabetic drug Glz and a detailed investigation of release of drug has been carried out. Various kinetic models have been applied on the release data. Finally, the Albino wistar rats were treated for Streptozotocin plus nicotinamide - induced diabetes using a Glz-loaded PCL/PAAc hydrogel. The results indicated a fair reduction in the glucose level of rats.