Timescale-invariant representation of acoustic communication signals by a bursting neuron

Acoustic communication often involves complex sound motifs in which the relative durations of individual elements, but not their absolute durations, convey meaning. Decoding such signals requires an explicit or implicit calculation of the ratios between time intervals. Using grasshopper communication as a model, we demonstrate how this seemingly difficult computation can be solved in real time by a small set of auditory neurons. One of these cells, an ascending interneuron, generates bursts of action potentials in response to the rhythmic syllable-pause structure of grasshopper calls. Our data show that these bursts are preferentially triggered at syllable onset; the number of spikes within the burst is linearly correlated with the duration of the preceding pause. Integrating the number of spikes over a fixed time window therefore leads to a total spike count that reflects the characteristic syllable-to-pause ratio of the species while being invariant to playing back the call faster or slower. Such a timescale-invariant recognition is essential under natural conditions, because grasshoppers do not thermoregulate; the call of a sender sitting in the shade will be slower than that of a grasshopper in the sun. Our results show that timescale-invariant stimulus recognition can be implemented at the single-cell level without directly calculating the ratio between pulse and interpulse durations

Coplanar back contacts for thin silicon solar cells

The type of coplanar back contact solar cell described was constructed with interdigitated n(+) and p(+) type regions on the back of the cell, such that both contacts are made on the back with no metallization grid on the front. This cell construction has several potential advantages over conventional cells for space use namely, convenience of interconnects, lower operating temperatures and higher efficiency due to the elimination of grid shadowing. However, the processing is more complex, and the cell is inherently more radiation sensitive. The latter problem can be reduced substantially by making the cells very thin (approximately 50 micrometers). Two types of interdigitated back contact cells are possible, the types being dependent on the character of the front surface. The front surface field cell has a front surface region that is of the same conductivity type as the bulk but is more heavily doped. This creates an electric field at the surface which repels the minority carriers. The tandem junction cell has a front surface region of a conductivity type that is opposite to that of the bulk. The junction thus created floats to open circuit voltage on illumination and injects carriers into the bulk which then can be collected at the rear junction. For space use, the front surface field cell is potentially more radiation resistant than the tandem junction cell because the flow of minority carriers (electrons) into the bulk will be less sensitive to the production of recombination centers, particularly in the space charge region at the front surface

A one-pot hydrothermal synthesis of sulfur and nitrogen doped carbon aerogels with enhanced electrocatalytic activity in the oxygen reduction reaction

A one-pot, hydrothermal synthesis of nitrogen and sulfur dual doped carbon aerogels is presented, derived from our previously published hydrothermal carbonization approach. Two co-monomers, S-(2-thienyl)-L-cysteine (TC) and 2-thienyl carboxaldehyde (TCA), were used for sulfur incorporation, giving rise to distinct morphologies and varying doping levels of sulfur. Nitrogen-doping levels of 5 wt% and sulfur-doping levels of 1 wt% (using TCA) to 4 wt% (using TC) were obtained. A secondary pyrolysis step was used to further tune the carbon aerogel conductivity and heteroatom binding states. By comparing solely nitrogen-doped with nitrogen- and sulfur-doped carbon aerogels, it was observed that the presence of sulfur improves the overall electrocatalytic activity of the carbon material in both basic and acidic media. This study of the synergistic effect of combined sulfur- and nitrogen-doping in the catalysis of the “oxygen reduction reaction” (ORR) is expected to be significant to future research concerning the improvement of heterogeneous, metal-free, carbon-based catalysts

Does the Analysis of Separate Bands of Echo Intensity Strengthen the Relationship to Muscle Function?

Ultrasound echo intensity (EI) has been proposed as a method of assessing muscle quality through the use of non-invasive imaging. Traditionally, EI is assessed as the mean pixel brightness that ranges from 0-255 within an area of interest. However, it may be reasonable to consider that additional portions of the ultrasound EI signal (i.e., bands of signal) may provide novel insight to muscle function. The determination of which band of signal may be more related to a given functional outcome may increase the sensitivity of EI. Thus far, there is no research analyzing the association between EI bands and fatigue. PURPOSE: The purpose of this study was to compare relationships between mean echo intensity and unique bands of ultrasound signal of the vastus lateralis with metrics of whole muscle performance in healthy adults. METHODS: Twenty-four participants (mean ± age = 22 ± 3.9 yrs; BMI = 25.7 ± 3.4 kg/m2), completed two visits to the laboratory. On the first visit, subjects completed Brightness mode (B-mode) ultrasound imaging and were familiarized with the fatigue assessment. Between two and seven days later, subjects returned for the testing visit. B-mode ultrasound was used to image the vastus lateralis (VL) at 50% muscle length. The VL cross-sectional area was traced using the polygon tool. As much of the muscle was selected without selecting any of the surrounding fascia. Each pixel is assigned a brightness value from 0-255 based on gray scale; 0 representing true black and 255 is pure white. Mean EI was quantified from within the selected portion of the image. Echo intensity bands were calculated in pixel value intervals of 0-49, 50-99, 100-149, 150-199, 200-255. The percentage of pixels per band compared to the total number of pixels in each image was assessed by: (number of pixels in each band/ total number of pixels in the selected portion of the image)*100. For the fatigue assessment, participants completed 100 repeated, maximal, isokinetic muscle actions (120°/sec). Isokinetic peak torque was analyzed offline using custom written software by selecting individual torque peaks from each muscle action. Initial and final isokinetic peak torque were calculated by averaging the highest 3 of the first 5 and the highest 3 of the last 5 contractions. Isokinetic peak torque percent decline (%Decline) was calculated by: %Decline = (initial – PT – final - PT)/initial - PT. Pearson’s correlation coefficient (r) was used to assess the relationship between each EI band and %Decline as well as mean EI and %Decline. The Stieger’s Z procedure was used to compare the correlation coefficients between mean EI and each EI band. RESULTS: There were no significant correlation between mean EI and %Decline (r=0.03, p=0.88) or any of the EI bands and %Decline (r=-0.07-0.3, p=0.16-0.89). Additionally, there were no significant relationships between the mean EI and any of the EI bands (z=0.001-0.88, p=0.38-0.99). CONCLUSION: The findings suggest that unique bands of ultrasound signal do not offer different relationships compared to overall mean EI when assessing fatigue from repetitive isokinetic muscle actions

Reliability of Differing Muscle Size and Quality Analysis Techniques

Brightness-mode (B-mode) ultrasonography is a popular tool to examine anatomical cross-sectional area (ACSA) and echo intensity (EI). Muscle ACSA and EI provide valuable insight into muscle function due to their unique mechanisms which influence performance. Manually analyzing ultrasound images potentially increases variability which may increase error, thus decreasing the reliability of manual image analysis. Recently an automated program was created to improve reliability and reduce the time of ultrasound image analysis. PURPOSE: The purpose of this study was to investigate the reliability of manual compared to automatic ultrasound analyses of muscle cross-sectional area and echo intensity. METHODS: Twenty-two participants (mean ± SD age = 24 ± 4 yrs; BMI = 24.19 ± 3.26 kg/m2) volunteered for this study. The participants completed one visit to the laboratory consisting of two data collection trials separated by 10 minutes. Ultrasound scans were taken with a B-mode ultrasound imaging device and image settings were held constant (i.e., depth = 6 cm, frequency = 12 MHz, gain = 52 dB). For each trial, participants remained supine while ACSA scans of the vastus lateralis (VL) were taken at 50% the length of the proximal to distal musculo-tendon junctions. The ACSA of the VL was manually analyzed by an experienced technician with ImageJ using the polygon tool and tracing the area of interest. Echo intensity was quantified as the mean pixel brightness of the traced portion of the image. Images were automatically analyzed with the Deep Anatomical Cross-Sectional Area (DeepACSA) program which is an algorithm that is designed to automatically trace the area of interest of an ultrasound image. Test-retest reliability statistics (i.e., intraclass correlation coefficient [ICC] model 2,1, standard error of measure expressed as a percentage of the mean [SEM%], and the minimal differences [MD] values needed to be considered real) were calculated for trials 1 and 2. One-way repeated measures analysis of variance determined differences in trial 1 compared to trial 2. RESULTS: Manual analyses of ACSA (ICC2,1 = 0.98, SEM (%) = 3.39%, MD = 2.09 cm2, p = 0.046) were more reliable than automatic analyses (ICC2,1 = 0.87, SEM (%) = 12.33%, MD = 7.77 cm2, p = 0.216). Manual analyses of EI (ICC2,1 = 0.73, SEM (%) = 6.44%, MD = 10.83 cm2, p = 0.514) had similar reliability to the automatic analyses (ICC2,1 = 0.88, SEM (%) = 3.60%, MD = 6.30 cm2, p = 0.003). CONCLUSION: These results suggest that this automated analysis program may be less reliable compared to the manual analysis of muscle ACSA of the VL. Conversely, DeepACSA displayed similar reliability for EI of the VL when compared to the manual analysis

Test-Retest Reliability of Automatic and Manual Image Analyses of Muscle Size

Brightness-mode (B-mode) ultrasound is a non-invasive imaging modality that has risen in popularity. In research settings, B-mode ultrasound is often used to assess skeletal muscle size via the quantification of the anatomical cross-sectional area (ACSA). Typically, these images are analyzed by an experienced investigator using open-source software, though it is a time-consuming process that may introduce implicit bias into the analysis. Recently, a novel, automatic ultrasound image analysis tool has been developed which may reduce bias and increase the reliability of ultrasound ACSA image analysis. PURPOSE: The purpose of the project was to compare the test-retest reliability of manual and automatic ACSA quantification techniques. METHODS: Nine participants (mean ± SD: age = 25 ± 3 years; BMI = 23.96 ± 2.62 kg/m2) completed one laboratory visit where each participant had non-invasive ultrasound imaging performed on their rectus femoris (i.e., RF) for two data collection trials separated by 10 minutes. For each participant, ultrasound image settings were held constant (i = 6 cm, frequency = 10 MHz, gain = 52 dB). All images were manually analyzed by an experienced technician using an open-source image analysis tool. The investigator would carefully select only the surrounding muscle fascia of the RF. Automatic analyses were performed using DeepACSA, a deep learning approach for the assessment of ACSA. Both manual and automatic analyses were conducted on all images. Analysis of variance (ANOVA) was conducted to compare differences between trials and test-retest reliability (i.e., intraclass correlation coefficients [ICC] model 2,1, standard error of measure expressed as a percentage of the mean [SEM%], and the minimal differences [MD] values needed to be considered real) were calculated from the ANOVA output. RESULTS: The manual analyses of ACSA (p = 0.20, ICC2,1 = 0.84, SEM (%) = 11.67%, MD = 1.75 cm2) were more reliable than the DeepACSA analyses (p = 0.13, ICC2,1 = 0.47, SEM (%) = 30.28%, MD = 4.70 cm2). CONCLUSION: The results of the present investigation suggest that the DeepACSA approach may be less reliable compared to the manual quantification of RF muscle size. Future studies should investigate using a larger sample size and additional muscle groups