Simultaneous measurement of intra-epidermal electric detection thresholds and evoked potentials for observation of nociceptive processing following sleep deprivation

Sleep deprivation has been shown to increase pain intensity and decrease pain thresholds in healthy subjects. In chronic pain patients, sleep impairment often worsens the perceived pain intensity. This increased pain perception is the result of altered nociceptive processing. We recently developed a method to quantify and monitor altered nociceptive processing by simultaneous tracking of psychophysical detection thresholds and recording of evoked cortical potentials during intra-epidermal electric stimulation. In this study, we assessed the sensitivity of nociceptive detection thresholds and evoked potentials to altered nociceptive processing after sleep deprivation in an exploratory study with 24 healthy male and 24 healthy female subjects. In each subject, we tracked nociceptive detection thresholds and recorded central evoked potentials in response to 180 single- and 180 double-pulse intra-epidermal electric stimuli. Results showed that the detection thresholds for single- and double-pulse stimuli and the average central evoked potential for single-pulse stimuli were significantly decreased after sleep deprivation. When analyzed separated by sex, these effects were only significant in the male population. Multivariate analysis showed that the decrease of central evoked potential was associated with a decrease of task-related evoked activity. Measurement repetition led to a decrease of the detection threshold to double-pulse stimuli in the mixed and the female population, but did not significantly affect any other outcome measures. These results suggest that simultaneous tracking of psychophysical detection thresholds and evoked potentials is a useful method to observe altered nociceptive processing after sleep deprivation, but is also sensitive to sex differences and measurement repetition.

Solar Panels String Predictive and Parametric Fault Diagnosis Using Low-Cost Sensors

This work proposes a method for real-time supervision and predictive fault diagnosis applicable to solar panel strings in real-world installations. It is focused on the detection and parametric isolation of fault symptoms through the analysis of the Voc-Isc curves. The method performs early, systematic, online, automatic, permanent predictive supervision, and diagnosis of a high sampling frequency. It is based on the supervision of predictive electrical parameters easily accessible by the design of its architecture, whose detection and isolation precedes with an adequate margin of maneuver, to be able to alert and stop by means of automatic disconnection the degradation phenomenon and its cumulative effect causing the development of a future irrecoverable failure. Its architecture design is scalable and integrable in conventional photovoltaic installations. It emphasizes the use of low-cost technology such as the ESP8266 module, ASC712-5A, and FZ0430 sensors and relay modules. The method is based on data acquisition with the ESP8266 module, which is sent over the internet to the computer where a SCADA system (iFIX V6.5) is installed, using the Modbus TCP/IP and OPC communication protocols. Detection thresholds are initially obtained experimentally by applying inductive shading methods on specific solar panels.

DeepSpot: a deep neural network for RNA spot enhancement in smFISH microscopy images

Detection of RNA spots in single molecule FISH microscopy images remains a difficult task especially when applied to large volumes of data. The small size of RNA spots combined with high noise level of images often requires a manual adaptation of the spot detection thresholds for each image. In this work we introduce DeepSpot, a Deep Learning based tool specifically designed to enhance RNA spots which enables spot detection without need to resort to image per image parameter tuning. We show how our method can enable the downstream accurate detection of spots. The architecture of DeepSpot is inspired by small object detection approaches. It incorporates dilated convolutions into a module specifically designed for the Context Aggregation for Small Object (CASO) and uses Residual Convolutions to propagate this information along the network. This enables DeepSpot to enhance all RNA spots to the same intensity and thus circumvents the need for parameter tuning. We evaluated how easily spots can be detected in images enhanced by our method, by training DeepSpot on 20 simulated and 1 experimental datasets, and have shown that more than 97% accuracy is achieved. Moreover, comparison with alternative deep learning approaches for mRNA spot detection (deepBlink) indicated that DeepSpot allows more precise mRNA detection. In addition, we generated smFISH images from mouse fibroblasts in a wound healing assay to evaluate whether DeepSpot enhancement can enable seamless mRNA spot detection and thus streamline studies of localized mRNA expression in cells.

Ecological validity of masking release with speech-like stimuli

Comodulated masking noise and binaural cues can facilitate detecting a target sound from noise. These cues can induce a decrease in detection thresholds, quantified as comodulation masking release (CMR) and binaural masking level difference (BMLD), respectively. However, their relevance to speech perception is unclear as most studies have used artificial stimuli different from speech. Here, we investigated their ecological validity using sounds with speech-like spectro-temporal dynamics. We evaluated the ecological validity of such grouping effect with stimuli reflecting formant changes in speech. We set three masker bands at formant frequencies F1, F2, and F3 based on CV combination: /gu/, /fu/, and /pu/. We found that the CMR was little (< 3 dB) while BMLD was comparable to previous findings (~ 9 dB). In conclusion, we suggest that other features may play a role in facilitating frequency grouping by comodulation such as the spectral proximity and the number of masker bands.

The effect of the preceding masking noise on monaural and binaural release from masking

When a target tone is preceded by a noise, the threshold for target detection can be increased or decreased depending on the type of a preceding masker. The effect of preceding masker to the following sound can be interpreted as either the result of adaptation at the periphery or at the system level. To disentangle these, we investigated the time constant of adaptation by varying the length of the preceding masker. For inducing various masking conditions, we designed stimuli that can induce masking release. Comodulated masking noise and binaural cues can facilitate detecting a target sound from noise. These cues induce a decrease in detection thresholds, quantified as comodulation masking release (CMR) and binaural masking level difference (BMLD), respectively. We hypothesized that if the adaptation results from the top-down processing, both CMR and BMLD will be affected with increased length of the preceding masker. We measured CMR and BMLD when the length of preceding maskers varied from 0 (no preceding masker) to 500 ms. Results showed that CMR was more affected with longer preceding masker from 100 ms to 500 ms while the preceding masker did not affect BMLD. In this study, we suggest that the adaptation to preceding masking sound may arise from low level (e.g. cochlear nucleus, CN) rather than the temporal integration by the higher-level processing.