Use of Open Source Hardware and Software Platforms to Quantify Spectrally Dependent Differences in Photochemical Efficiency and Functional Absorption Cross Section within the Dinoflagellate Symbiodinium spp.

Abstract

Active chlorophyll a fluorescence is an essential tool for understanding photosynthetic activity within cnidarian/dinoflagellate symbioses. Fluorescence measurement is typically achieved by utilizing a blue or red monochromatic excitation light source. However, algal photosynthetic pigments can differ in their absorption spectra, potentially leading to excitation wavelength dependent measurements of maximal and light acclimated PSII photosynthetic quantum yield (Fv/Fm or Fq ′ /Fm ′ ) and functional absorption cross section (σ PSII or σ PSII ′ ). Here we utilized an open source hardware development platform to construct a multispectral excitation fluorometer to assess spectrally dependent differences in photochemistry within four different Symbiodinium species (two of each ITS2-type A4 and B1). Multivariate analysis of light acclimated photochemical signatures showed separation between most alga types. These spectrally dependent differences in light acclimated PSII efficiency and PSII functional absorption cross section likely reflect changes in light harvesting compounds, their connectivity to the PSII reaction centers and the balance between photochemical and non-photochemical fluorescence quenching. Additionally, acclimation to low (20 µmol photons m −2 s −1 ) and high (200 µmol photons m −2 s −1 ) light conditions was examined in two of these symbionts types (ITS-2 type A4 and B1) As expected, chlorophyll a cell −1 decreased under high light acclimation in both symbionts. However, only A4 saw a subsequent reduction in absorbance whereas cellular volume decreased in the B1 (S. minutum) symbiont. In response to high light acclimation, Fv/Fm was significantly lower at all excitation wavelengths for the B1 symbiont where as efficiencies remained the same for A4. However, high-light acclimated Fq ′ /Fm ′ levels decreased in both symbionts, but only when measured using the 615 or 625 nm excitation wavelengths. Non-photochemical quenching within the antennae bed was downregulated under high light acclimation in the A4 symbiont, but only when measured using the 505 and 530 nm excitation wavelengths. Such changes in Fq ′ /Fm ′ and antennae bed quenching highlight the Hoadley and WarnerSymbiodinium Multispectral Chlorophyll a Fluorescence benefits of spectrally resolved photochemical measurements. Additionally, the utilization of Arduino and Bitscope hardware exemplifies the potential of open source development platforms for construction of highly customizable instrumentation for photosynthetic research.

Host–symbiont combinations dictate the photo-physiological response of reef-building corals to thermal stress

“…Coral skeletons contribute to substantial photon path length enhancement and higher scalar irradiance, while the coral tissue may also attenuate such light 24,84 and both factors can markedly affect σ PSII. In addition, σ PSII is a wavelength dependent variable that tends to be lower when comparing values from in hospite algae against those collected from optically thin algal cultures 83,85 . Nevertheless, when measured in the blue spectrum, recent evidence shows that the difference in σ PSII (λ) between symbionts in hospite and in culture are closely correlated and largely dependent on light availability.…”

Development of a low-cost and portable smart fluorometer for detecting breast cancer cells

“…The table is divided into two categories based on the sensing mechanism used: image based and signal based. The devices mentioned in image based category detects tuberculosis [5,24], imaging assays [26], breast cancer [4] and living cells [27] while the devices that are presented in the signal based category detects chlorophyll [28][29][30]and Escherichia [32]. In addition to these devices, we have also included commercial microscope/fluorometer for comparison.…”

A Control System of SEMG Signal Based on Deep Learning

“…Literature [1,2] studies the acquisition and preprocessing of signals, literature [3,4] studies the recognition and classification of signals, and has achieved some results, but there are still some problems in the above literature, including the accuracy of continuous multi actions recognition, and the content of the research is discrete, so this paper proposes the integration of the current popular deep learning technology, designs and realizes a complete set The whole EMG signal control system. In this paper, only convolutional neural network method is used to classify gestures [5][6][7], and the classification results are matched to the motors which control the degree of freedom of the manipulator.…”