Research article Special Issues

A new discussion concerning to exact controllability for fractional mixed Volterra-Fredholm integrodifferential equations of order r(1,2) with impulses

  • In this article, we look into the important requirements for exact controllability of fractional impulsive differential systems of order 1<r<2. Definitions of mild solutions are given for fractional integrodifferential equations with impulses. In addition, applying fixed point methods, fractional derivatives, essential conditions, mixed Volterra-Fredholm integrodifferential type, for exact controllability of the solutions are produced. Lastly, a case study is supplied to show the illustration of the primary theorems.

    Citation: Marimuthu Mohan Raja, Velusamy Vijayakumar, Anurag Shukla, Kottakkaran Sooppy Nisar, Wedad Albalawi, Abdel-Haleem Abdel-Aty. A new discussion concerning to exact controllability for fractional mixed Volterra-Fredholm integrodifferential equations of order r(1,2) with impulses[J]. AIMS Mathematics, 2023, 8(5): 10802-10821. doi: 10.3934/math.2023548

    Related Papers:

    [1] Mary Jane Beilby, Sabah Al Khazaaly . Re-modeling Chara action potential: I. from Thiel model of Ca2+transient to action potential form. AIMS Biophysics, 2016, 3(3): 431-449. doi: 10.3934/biophy.2016.3.431
    [2] Marek K. Korzeniowski, Barbara Baird, David Holowka . STIM1 activation is regulated by a 14 amino acid sequence adjacent to the CRAC activation domain. AIMS Biophysics, 2016, 3(1): 99-118. doi: 10.3934/biophy.2016.1.99
    [3] Yoshiji Hantani, Hiroshi Imamura, Tsubasa Yamamoto, Akane Senga, Yuri Yamagami, Minoru Kato, Fusako Kawai, Masayuki Oda . Functional characterizations of polyethylene terephthalate-degrading cutinase-like enzyme Cut190 mutants using bis(2-hydroxyethyl) terephthalate as the model substrate. AIMS Biophysics, 2018, 5(4): 290-302. doi: 10.3934/biophy.2018.4.290
    [4] Hayet Houmani, Francisco J Corpas . Differential responses to salt-induced oxidative stress in three phylogenetically related plant species: Arabidopsis thaliana (glycophyte), Thellungiella salsuginea and Cakile maritima (halophytes). Involvement of ROS and NO in the control of K+/Na+ homeostasis. AIMS Biophysics, 2016, 3(3): 380-397. doi: 10.3934/biophy.2016.3.380
    [5] Lars H. Wegner . Cotransport of water and solutes in plant membranes: The molecular basis, and physiological functions. AIMS Biophysics, 2017, 4(2): 192-209. doi: 10.3934/biophy.2017.2.192
    [6] Charles O. Nwamba, Ferdinand C. Chilaka, Ali Akbar Moosavi-Movahedi . Cation modulation of hemoglobin interaction with sodium n-dodecyl sulphate (SDS) iv: magnesium modulation at pH 7.20. AIMS Biophysics, 2016, 3(1): 146-170. doi: 10.3934/biophy.2016.1.146
    [7] Nita R. Shah, Keni Vidilaseris, Henri Xhaard, Adrian Goldman . Integral membrane pyrophosphatases: a novel drug target for human pathogens?. AIMS Biophysics, 2016, 3(1): 171-194. doi: 10.3934/biophy.2016.1.171
    [8] Ashwani Kumar Vashishtha, William H. Konigsberg . The effect of different divalent cations on the kinetics and fidelity of Bacillus stearothermophilus DNA polymerase. AIMS Biophysics, 2018, 5(2): 125-143. doi: 10.3934/biophy.2018.2.125
    [9] Sudarat Tharad, Chontida Tangsongcharoen, Panadda Boonserm, José L. Toca-Herrera, Kanokporn Srisucharitpanit . Local conformations affect the histidine tag-Ni2+ binding affinity of BinA and BinB proteins. AIMS Biophysics, 2020, 7(3): 133-143. doi: 10.3934/biophy.2020011
    [10] Alexander G. Volkov, Yuri B. Shtessel . Propagation of electrotonic potentials in plants: Experimental study and mathematical modeling. AIMS Biophysics, 2016, 3(3): 358-379. doi: 10.3934/biophy.2016.3.358
  • In this article, we look into the important requirements for exact controllability of fractional impulsive differential systems of order 1<r<2. Definitions of mild solutions are given for fractional integrodifferential equations with impulses. In addition, applying fixed point methods, fractional derivatives, essential conditions, mixed Volterra-Fredholm integrodifferential type, for exact controllability of the solutions are produced. Lastly, a case study is supplied to show the illustration of the primary theorems.



    1. Introduction

    Thiel and his group measured the rise of Ca2+ in Chara cytoplasm (and resulting Cl channel activation) in response to threshold depolarization of the membrane potential difference (PD) [1,2,3]. They modified animal cell model describing formation of second messenger Inositol 1, 4, 5-triphosphate (IP3) [4,5], which activates Ca2+ channels on internal stores [6]. In our first paper [7] we set up PD rate of change equation, where the transient Ca2+ concentration increase activated Cl channels and the action potential (AP) form was calculated by numerical integration. To improve the fit of the model to AP data, we introduced prompt Ca2+ transient across plasma membrane. Sharp, positive-going PD spikes at the beginning of the AP data supported this modification. The feature was particularly visible in cells under saline stress, where the APs were spontaneous [7]. The modeling was initially inspired by observation of these prolonged APs in saline stressed cells. We were able to increase the model AP duration by decreasing the values of coefficients in the Hill equation, describing the Ca2+pumps on internal stores (see Figure 6a in [7]). The present paper provides detailed modeling of saline APs, finding that the form is quite variable: "many-or-none" rather than "all-or-none". To fit such diversity in form, some of the Ca2+ channel rate constants also needed modification at the time of saline stress. Different initial IP3 concentrations were explored. As in the first paper [7] the model suggests range of experiments, as well as prompting a discussion how to distinguish AP from other transients. The effect of spontaneous prolonged APs on the cell under salinity stress is discussed.


    2. Materials and Methods


    2.1. Materials

    As described in paper 1 [7] salt-sensitive Chara australis cells survive longer in salinity experiments, if pre-conditioned to osmotic stress in sorbitol artificial pond water (APW: NaCl 1 mM, KCl 0.1 mM, CaCl2 0.1 mM, neutral pH; with added 90 mM sorbitol) for at least 1hr before exposure to Saline APW (KCl 0.1 mM, CaCl2 0.1 mM, neutral pH, 50 mM NaCl). The spontaneous APs with prolonged durations were only observed in the Saline APW. The APs were selected from the whole PD record (data-logged at 0.1 s and stored as text files) and input into Mathematica 10 programs. The evolution of the saline AP form was documented in three cells, numbered in order of the modeling process: Cell 1 (4 APs, AP1 shown in Figure 5 of paper 1 [7]), Cell 2 (5 APs, four APs are shown in Figure 6b of paper 1 [7]) and Cell 3 (5 APs, AP5 from Cell 3 is shown in Figure 6a of paper 1 [7]). The fit parameters for all the APs are shown in Tables 13.

    Table 1a. Cell 1 fit parameters.
    Parameter APav, hyper AP1 (20 s) AP2 (34 min) AP3 (90 min) AP4 (140 min)
    νr 0.185
    γ0 0.1 s–1
    γ1 20.5 s–1
    p1' 8.5 μM s–1 8.89 μM s–1 9.3 red 11.1 blue 13.9 green 8.63 μM s–1 8.89 μM s–1 7.8 μM s–1
    p2' 0.035 μM 0.027 μM 0.027 red 0.025 blue 0.024 green 0.017 μM 0.007 μM 0.007 μM
    C0 1.56 μM
    k1 12.0 (μM s)–1
    k–1 8.0 s–1
    k2' 15.0 (μM s)–1
    k–2 1.65 s–1
    k3' 1.8 (μM s)–1 1.38(μM s)–1 1.41 red 0.8 blue 0.8 green 1.38(μM s)–1 1.98 (μM s)–1 1.98(μM s)–1
    k–3 0.04 s–1 0.64 s–1 0.635 red 0.47 blue 0.7 green 0.655 s–1 0.935 s–1 0.95 s–1
    IP3 2.5 μM 0.389 μM 0.49 red 1.0 blue 2.5 green 0.389 μM 0.389 μM 0.389 μM
    ka 2 s–1
    ki 2 s–1
    DCa2+ (period of application) 0.0355 (0.04–0.16 s) 0.023 (0.04–0.16 s) 0.04 (0.02–0.16 s) 0.035 (0.04–0.19 s) 0.035 (0.04–0.19 s)
     | Show Table
    DownLoad: CSV
    Table 1b. Parameters for the membrane transporters.
    Parameter APav, hyper AP1 (20 s) AP2 (34 min) AP3 (90 min) AP4 (140 min)
    κoi 140 s–1 140 s–1 102 s–1 65 s–1 40 s–1
    kio0 7000 s–1 7000 s–1 6000 s–1 6000 s–1 6000 s–1
    koi0 0.1 s–1
    κio 0.1 s–1
    Gbkg 0.5 S m–2 0.5 S m–2 1.5 S m–2 1.5 S m–2 1.5 S m–2
    V50+ 100 mV
    zg 1.0
    NKPK 6.5 × 10–7 m3 s–1
    [K+]cyt 100 mM
    [K+]o 0.1 mM
    [Cl]cyt 10 mM 50 mM
    [Cl]o 1.3 mM 50 mM
    [Ca2+]cyt 0.02 μM
    [Ca2+]o 0.1 mM
    GCl, max 10 S m–2 40 S m–2
    40 red
    43 blue
    43 green
    32 S m–2 25.5 S m–2 19.5 S m–2
     | Show Table
    DownLoad: CSV
    Table 2a. Cell 2 fit parameters.
    Parameter APav, AP1 (142 min) AP2 (160 min) AP3 (168 min) AP4 (245 min) AP5 (268 min)
    νr 0.185
    γ0 0.1 s–1
    γ1 20.5 s–1
    p1' 8.5 μM s–1 8.4 μM s–1 6.8 μM s–1 6.8 μM s–1 6.8 μM s–1 4.8 μM s–1
    p2' 0.035 μM 0.018 μM 0.02 μM 0.015 μM 0.018 μM 0.02 μM
    C0 1.56 μM
    k1 12.0 (μM s)–1
    k–1 8.0 s–1
    k2' 15.0 (μM s)–1
    k–2 1.65 s–1
    k3' 1.8 (μM s)–1 1.5 (μM s)–1 0.9 (μM s)–1 0.55 (μM s)–1 0.3 (μM s)–1 0.9 (μM s)–1
    k–3 0.04 s–1 0.55 s–1 0.21 s–1 0.21 s–1 0.24 s–1 0.1 s–1
    IP3 2.5 μM 0.5 μM 0.35 μM 0.35 μM 0.35 μM 0.3 μM
    ka 2 s–1
    ki 2 s–1
    DCa2+ (period of application) 0.0355 (0.04–0.16 s) 0.015 (0.04–0.16 s) 0.025 (0.04–0.18 s) 0.025 (0.04–0.18 s) 0.025 (0.04–0.18 s) 0.015 (0.04–0.14 s)
     | Show Table
    DownLoad: CSV
    Table 2b. Parameters for the membrane transporters.
    Parameter APav AP1 (142 min) AP2 (160 min) AP3 (168 min) AP4 (245 min) AP5 (268 min)
    κoi 140 s–1 IOH NOHPOH: 9 × 10–4 m3 s–1 IOH NOHPOH: 1 × 10–4 m3 s–1 IOH NOHPOH: 1 × 10–4 m3 s–1 IOH NOHPOH: 2 × 10–4 m3 s–1 IOH NOHPOH: 2 × 10–4 m3 s–1
    kio0 7000 s–1 zg+: 1 zg–: 0.6 zg+: 1 zg–: 0.6 zg+: 1 zg–: 0.6 zg+: 1 zg–: 0.6 zg+: 1 zg–: 0.6
    koi0 0.1 s–1 V50+: 350 V50–:– 170 mV V50+: 350 V50–: –170 mV V50+: 350 V50–: –170 mV V50+: 350 V50–: –170 mV V50+: 350 V50–: –170 mV
    κio 0.1 s–1
    Gbkg 0.5 S m–2 1.8 S m–2 1.8 S m–2 1.8 S m–2 1.8 S m–2 1.8 S m–2
    V50+ 100 mV
    zg 1.0
    NKPK 6.5 × 10–7 m3 s–1
    [K+]cyt 100 mM
    [K+]o 0.1 mM
    [Cl]cyt 10 mM 50 mM
    [Cl]o 1.3 mM 50 mM
    [Ca2+]cyt 0.02 μM
    [Ca2+]o 0.1 mM
    GCl, max 10 S m–2 20 S m–2 21 S m–2 20 S m–2 16 S m–2 16 S m–2
     | Show Table
    DownLoad: CSV
    Table 3a. Cell 3 parameters.
    Parameter APav AP1 (1 min) AP2 (18 min) AP3 (82 min) AP4 (84 min) AP5 (115 min)
    νr 0.185
    γ0 0.1 s–1
    γ1 20.5 s–1
    p1' 8.5 μM s–1 1.65 μM s–1 10.5 μM s–1 2.1 μM s–1 2.8 μM s–1 0.651 μM s–1
    p2' 0.035 μM 0.029 μM 0.045 μM 0.045 μM 0.045 μM 0.027 μM
    C0 1.56 μM
    k1 12.0 (μM s)–1
    k–1 8.0 s–1
    k2' 15.0 (μM s)–1
    k–2 1.65 s–1
    k3' 1.8 (μM s)–1 1.72 (μM s)–1 0.1 (μM s)–1 1.7 (μM s)–1 1.7 (μM s)–1 1.72 (μM s)–1
    k–3 0.04 s–1 0.054 s–1 1.4 s–1 0.6 s–1 0.6 s–1 0.695 s–1
    IP3 2.5 μM 2.5 μM 0.23 μM 2.5 μM 2.5 μM 2.5 μM
    ka 2 s–1
    ki 2 s–1
    DCa2+ (period of application) 0.0355 (0.04–0.16 s) 0.02 (0.04–0.16 s) 0.021 (0.04–0.18 s) 0.02 (0.04–0.18 s) 0.02 (0.04–0.18 s) 0.02 (0.04–0.18 s)
    AP1 Extra bump: Ip0 = 0.15 μM, turned on at 18.5 s.
    AP5 Extra bump: Ip0 = 0.0065 μM, turned on at 53.05 s.
     | Show Table
    DownLoad: CSV
    Table 3b. Parameters for the membrane transporters.
    Parameter APav hyper AP1 (1 min) AP2 (18 min) AP3 (82 min) AP4 (84 min) AP5 (115 min)
    κoi 140 s–1 120 s–1 70 s–1 IOH: NOHPOH: 10 × 10–4 m3 s–1 IOH: NOHPOH: 10 × 10–4 m3 s–1 50 s–1
    kio0 7000 s–1 6000 s–1 zg+: 1 zg–: 0.6 zg+: 1 zg–: 0.6 6000 s–1
    koi0 0.1 s–1 V50+: 350 V50–: –170 mV V50+: 350 V50–: –170 mV
    κio 0.1 s–1
    Gbkg 0.5 S m–2 1.2 S m–2 1.5 S m–2 1.8 S m–2 1.8 S m–2 1.6 S m–2
    V50+ 100 mV
    zg 1.0
    NKPK 6.5 × 10–7 m3 s–1
    [K+]cyt 100 mM
    [K+]o 0.1 mM
    [Cl]cyt 10 mM 50 mM
    [Cl]o 1.3 mM 50 mM
    [Ca2+]cyt 0.02 μM
    [Ca2+]o 0.1 mM
    GCl, max 10 S m–2 27 S m–2 13 S m–2 70 S m–2 70 S m–2 13.5 S m–2
     | Show Table
    DownLoad: CSV

    2.2. Modeling the saline AP form

    The derivation of the Thiel-Beilby model is described in detail in paper 1 [7]. This model was used to simulate all the saline APs. The parameters are listed in Tables 13. The programs containing the fitted APs can be found on MJ Beilby website link "Action Potential models": http://newt.phys.unsw.edu.au/~mjb/APproj.html. The standard "APav" modeled to AP data obtained from cells in APW [7] was used to contrast the changes to AP form under salinity stress (see Figure 3, Figure 4 and Tables 13).

    The initial resting PD, RPD, for each cell was adjusted in the model to fit the experimental data. If the RPD was more negative than –100 mV, the appropriate level was obtained by manipulating the proton pump parameters and consequently the Ip/V characteristics (see Figure 1 in paper 1 [7]). At –100 mV the background current, Ibkg, dominated the resting membrane characteristics until the outward rectifier current, Iorc, became important above ~ –30 mV. Cells exposed to Saline APW for longer times depolarized to PDs above –100 mV and exhibited I/V characteristics that could be modeled by growing contribution from OH channels [8,9]. In this case, the amplitude of OH current, IOH, was adjusted to match the cell RPD (Figure 1). The IOH was simulated by the Goldmann-Hodgkin-Katz (GHK) equation, multiplied by the Boltzmann distribution of open probabilities, Po– and Po+, to make the PD-dependence stronger [10,11]. For mathematical details see equations 13, 14a, b in paper 1 [7]. The background and reasoning for modelling of IOH can be found in Beilby and Casanova [12].

    Figure 1. I/V characteristics of the most conductive transporters in Chara plasma membrane after long exposure to Saline APW: the OH channels become the dominant feature of the membrane conductance: IOH, (thin curved line), the background current, Ibkg, (thin straight line), the outward rectifier, Iorc, (long-dashed line) and the total current Itot (OH) (thick black line). The values of the model parameters were selected to match the RPD (see upward arrow) of the depolarized cells. The pump dominated I/V characteristics, Itot (pump), used in calculation of APav [7] are included for comparison and shown as thick gray line. Note the difference of RPD in pump dominated state (downward arrow) and OH channel dominated state (upward arrow).

    The cytoplasmic Cl concentration was increased to 50 mM in Saline APW to preserve the AP peak level (see Figure 3). The initial IP3 concentration, I0(see equation 2 in paper 1 [7]), did interact with the other parameters, affecting the AP form in a subtle manner (Figure 2a). The goodness of the model fit was judged by eye, aided by computation of the area difference: area under the data AP—the area under the simulated AP (Figure 2b).

    Figure 2. The effect of initial IP3 concentration I0. Data fitted (points) come from Cell 1, AP1, exposure to Saline APW of 20s. (a) AP model with I0 concentrations in μM: 0.389 (black), 0.49 (red), 1.0 (blue), 2.5 (green). For the values of the other model parameters, see Table 1, AP 1. (b) The goodness of fit judged by the area difference (area under data AP—area under model AP).
    Figure 3. Evolution of the AP form as function of exposure to Saline APW in Cell 1. (a) The spontaneous APs were recorded at 20 s, 34 min, 90 min and 140 min after the cell was challenged with Saline APW. The APav, fitted to cells in APW (dark blue profile), was included for comparison. As Cell 1 was quite hyperpolarized in Sorbitol APW, before exposure to saline, APav was adjusted through the pump model parameters (see APav, hyperin [7] and parameter values in Table 1). (b) The modeled APs were superimposed on the data as yellow lines, showing good correspondence (parameters in Table 1). Note the more gradual re-polarization phase in the early exposure APs.

    The prompt Ca2+ inflow across plasma membrane, ITRP, Ca, was simulated as in paper 1 [7]. The data-logging speed in the experiments was too slow to reveal detailed shape of the PD spike (see Figure 3 and Figure 4), so square current pulse was used and the fraction of Ca2+ reaching the channels on the stores was adjusted to fit the depolarizing phase of the AP.

    Figure 4. Evolution of the AP form as function of exposure to Saline APW in Cell 2. APav is included at the beginning of the series: AP1 (142 min saline exposure), AP2 (160 min saline exposure), AP3 (168 min saline exposure), AP4 (245 min saline exposure) and AP5 (268 min saline exposure). Note the changes in duration, with AP5 being the second shortest AP from the series despite very long saline exposure. Note also the multiple initial spikes in APs 2, 3 and 4.

    3. Results


    3.1. AP evolution with time in Saline APW

    Cell 1 AP1 demonstrated how fast the AP form responded to exposure to Saline APW. After only 20 s, with the RPD still very negative at –235 mV, the AP duration had more than doubled (Figure 3a, see also Figure 5a in paper 1 [7]). However, the repolarizing stage followed similar trend to APav (included in Figure 3a in blue). This gradual repolarization presented first modeling challenge, as changing the Hill coefficients, p1' and p2', in the Ca2+ pump model alone did not provide the right shape. The rate constants k3' and k–3, controlling the backward and forward transition between the activated Ca2+ channel state and final inactivated Ca2+ channel state (see equation 1 in paper 1 [7]), had to be modified to achieve reasonable fit (Table 1a, Figure 3b). AP2 (34 min of saline exposure) looked very similar to AP1, but the duration was slightly longer and the repolarization stage slightly shorter (see Table 4 for summary of AP data and Figure 3a). AP3 (90 min saline exposure) form had changed further: there was a distinct overshoot bulge at the beginning of the AP, reminiscent of the peak of APav, and the repolarization stage was more abrupt, giving the AP square appearance. AP4 (140 min saline exposure) was similar in initial stages, but had much longer duration of almost 6 s and repolarized even more abruptly (Figure 3a, Table 4). The membrane RPD has depolarized for the last two APs to –175 and –155 mV, respectively (Table 4). Figure 3b shows that modeled APs, superimposed on the data as yellow lines, provide satisfactory fit, describing the main features of each AP.

    Figure 5. Cell 3: AP1 and AP2 display great variation in form and duration, although AP2 fired at 18 min of saline exposure, while AP1 exposure was only 1 min. Note typical saline noise at the end of AP1 lasting until 800 s of the experiment [13]. Note also the unusual shape of AP1. The time axis shows time from the exposure to Saline APW.
    Table 4. Summary of AP parameters.
    Saline Exposures/min cell 1: 0.3, 34, 90, 140
    cell 2: 142, 160, 168, 245, 268
    cell 3: 1, 18, 82, 84, 115
    RPD/mV cell 1: –235, –178, –175, –155
    cell 2: –74, –88, –97, –81, – 87
    cell 3: –196, –177, –90, –75, –167
    Repolarization time/s cell 1: 8, 3.8, 2.2, 1
    cell 2: 2.9, 2.8, 1.9, 1.4, 1.2
    cell 3: 45, 3.2, 12, 10, 10, 2.9
    Spike peak/mV (multiple peaks in brackets) cell 1: –11, –12, –13, +46
    cell 2: +51, (+34, +48), (+8, +16), +57, +6
    cell 3: –28, +10, +16, –6, +51
    AP peak/mV Average: –38.9 ± 3.8 –29.0 ± 3.8 cell 1: –31 to –34, –38 to –48, –40 to –48, –35 to –52
    cell 2: –27 to –36, –26 to –35, –23 to –38, –24 to –38, –29 to –35
    cell 3: –46 to –52, –52 to –58, –2 to –11, –1 to –11, –32 to –48
    Duration/s Average: 12.8 ± 4.1 cell 1: 2.5, 3.2, 3.1, 5.7
    cell 2: 3.7, 2.9, 4.4, 7.3, 3.2
    cell 3: 19 + 6 (secondary peak), 5.8, 33, 30, 50
     | Show Table
    DownLoad: CSV

    The initial IP3 concentration, I0 (see equation 2 in paper 1 [7]) subtly influenced the AP form (Figure 2a). The best fit of the model for Cell 1 was found with I0 = 0.389 μM. The small changes in AP form for other concentrations are shown with different colors, the quality of the fit is expressed as area difference (see Methods, Figure 2b). Changing I0 required adjustment in p1', p2', k3', k–3 and GCl, maxparameters for the best fit (Table 1). Such parameter interaction increased complexity of the fitting process.

    The Cell 1 data suggest that the duration of AP may increase with saline exposure. Following the evolution of Cell 2 dispels that hypothesis: the durations of the five APs seem to vary at random, with the last AP5 being the second shortest (Figure 4, Table 4). Similarly in Cell 3 AP1 is much longer than AP2 (Figure 5). However, the repolarization once again became faster with longer saline exposures in both Cell 2 and Cell 3 APs (Figure 4, Table 4).


    3.2. Refractory period

    Cell 3 provided another insight. Apart from AP2, all other APs displayed long durations (Table 4, Figure 6). AP1 and AP5 both featured secondary peaks towards the end of the transient (Figure 6a, Figure 6b). The refractory period of Chara AP under normal conditions was found to be between 6 and 60 s [14]. Shepherd et al. 2008 [15] observed spontaneous repetitive APs in 100 mM NaCl/0.1 mM CaCl2 medium of 30 s duration and between 26 and 37 s apart. Consequently the secondary peaks in present data were treated as re-excitation and smaller amount of IP3 was introduced into the model. While the simulation produced secondary peaks, detailed fit was difficult (Table 3, Figure 6).

    Figure 6. (a) AP1 (1 min of saline exposure) and (b) AP5 (115 min saline exposure) of Cell 3 depolarized the membrane for comparatively long periods of 20 and 50 s. Note secondary peaks at about 20 s in (a) and 50 s in (b). These features were simulated by sudden increase in IP3 (Table 3) but were difficult to fit closely (light gray lines). AP5 is one of the longest Chara APs observed so far under saline stress.

    3.3. Statistical summary

    The data were collected from 14 spontaneous APs coming from 3 cells. The exposures to saline stress varied between 0.3 and 268 min, with RPDs ranging from –235 to –74 mV. The repolarization times mostly decreased for all cells as function of the saline exposure time. The initial spikes, thought to be due to prompt Ca2+ transient across plasma membrane, exhibited varying shapes and amplitudes. Some of the variation arises from insufficient data-logging speed (see methods). The maximal PDs of the measured spikes varied from –28 to +58 mV (Figure 3, Figure 4, Table 4). Some cells exhibited multiple spikes (Cell 2: APs 2, 3 and 4). The flat prolonged peaks of the APs were often not horizontal, so we defined maxima and minima for each AP (Table 4). The average level section of the AP varied between –29 and –39 mV (Table 4). The duration varied between 2.5 and 50 s, with average of 12.8 ± 4.1 s (Table 4). However, even the shortest saline APs had longer durations than APav.


    4. Discussion


    4.1. Can AP be distinguished from other transients?

    Under salinity stress, the APs in salt-sensitive Chara australis become "many-or-none", rather than "all-or-none" transient. The diversity involves bulges (e. g. Cell 1: APs 2–4) or dips (e.g. Cell 2: APs 1, 2 and 5, Cell 3: AP5) after the initial prompt spike. The flat part of the AP can slope in hyperpolarizing (Cell 1 APs) or depolarizing (Cell 2: AP 1 and 5) direction. Not only there is a diversity of form, but also the APs at similar saline exposures sometime display large differences (e. g. the two APs in Figure 5). This behavior could only be simulated with the model parameters p1', p2', k3' and k–3varying in almost random fashion (Tables 13). These parameters control the Ca2+ inflow into cytoplasm from the stores and pumping Ca2+ back into the stores. Plots of the parameters as function of saline exposure time display a lot of scatter (see supplementary Figure 1 on http://newt.phys.unsw.edu.au/~mjb/APproj.html). However, p1'and p2' mostly decreased with saline exposure time (with exception of Cell 3 p2' at 18, 82 and 84 min saline exposure), while k3' seemed initially unaffected (with exception of Cell 3 at 18 min saline exposure) and decreased at longer exposures, and k–3 increased and then decreased at similar exposures. Clearly more data are needed for conclusive analysis, especially with Cell 3 exhibiting very long APs, which required different combination of parameter values.

    Considering the diversity in form, can we be sure that these saline transients are in fact APs? Firstly, we can see the transition from APav after short exposures to Saline APW (Figure 3a, also Figure 5a in paper 1 [7]). Most saline APs start with the positive going spike of putative Ca2+ inflow across plasma membrane. Further, due to cytoplasmic Ca2+ rise, the characean AP is coupled to sudden stoppage of cytoplasmic streaming, which takes ~5 min to recover [16]. This effect was always noted at the time of the experiments. The AP peak, or the plateau in long APs, was found between –30 and –40 mV similar to APav. Finally, the Thiel-Beilby model can accommodate the main features of the diverse saline AP form. Thus we feel fairly confident, that the saline APs are produced by the same mechanisms as APav, but with some of it's many processes affected by salinity. Could we simulate the diverse saline AP form by another model? The alternative scenario is the inflow of Ca2+ from the outside through plasma membrane voltage-dependent channels. The prolonged Ca2+ inflow from the outside under saline stress would probably lead to greater depolarization of the AP peak than observed experimentally, but voltage clamp experiments might be necessary to investigate this possibility.

    Interestingly, the shape of the saline AP with the initial spike, the long plateau and the rapid recovery are reminiscent of cardio myocyte AP [17]. While the underlying channels in the myocyte AP are different (fast Na+ channel for the initial spike and two types of K+ channel for repolarization), there is also Ca2+ inflow, followed by more Ca2+ input from internal stores. In both systems the high internal Ca2+ transforms the electrical impulse into mechanical contraction of the heart muscle or the "green muscle" of the cytoplasmic streaming stoppage.

    While the re-excitation at the end of AP1 and AP5 of Cell 3 is plausible, as the refractory periods of those durations have been observed [14,15], there is also possibility of superposition of the saline noise [13]. This is more likely in AP1 (Figure 5), where the noise is visible at the end of excitation. However, the secondary bulge is similar to that at the beginning of the AP (Figure 6a) and unlike the spiky saline noise [13]. Alternatively, the long depolarization might produce small amounts of IP3, which would be normally insufficient to start an AP, but as there is still lot of Ca2+ in the cytoplasm, more Ca2+ channels are activated.


    4.2. The effect of prolonged APs on the salinity stressed cell

    Shepherd et al. [15] compared the effects of 100 mM NaCl with either 1.0 mM or 0.1 mM CaCl2, noting that low external Ca2+ caused depolarization to PDs above the AP threshold and spontaneous repetitive prolonged APs, which usually lead to cell death. In our experiments with Saline APW of 50 mM NaCl/0.1 mM CaCl2, spontaneous APs at shorter intervals also seemed to speed up the cell collapse with RPD close to zero and permanent cytoplasmic streaming cessation (e.g. Cell 2).

    Under normal circumstances, the APs signal mechanical deformation or injury to the cell and cessation of cytoplasmic streaming allows the wound healing process to start [18]. So, why is AP so damaging to the cell under salinity stress? The plateau in the saline AP between –30 and –40 mV activates the outward rectifier current (Figure 1) and cell loses K+ (and Cl through Ca2+-activated Cl channels) in each AP. With the pump getting gradually inactivated and the RPD depolarizing close to and eventually above –100 mV, the loss of K+ becomes irreversible. In a healthy cell, K+ is one of the most abundant inorganic cations, which activate enzymes, stabilize protein synthesis and contribute to cell turgor [19]. Under salinity stress Na+ enters the cytoplasm through the voltage-independent non-selective channels, that conduct the background current [15], and competes for K+ binding sites, being similar in physicochemical structure. However, the similarity is not close enough for Na+ to perform the same functions and K+-dependent metabolic processes are inhibited [20]. The mechanisms for salinity effects on the Ca2+ channels and pumps on the internal stores are not clear at present. Perhaps K+ stabilizes the channel and pump proteins and the gradual replacement by Na+ leads to somewhat random changes in their function. In general, plants need to maintain high cytoplasmic K+/Na+ ratio to be salt tolerant [19,20]. Cl is regarded as essential micronutrient, important in oxygenic photosynthesis and needed for turgor generation [21]. With the collapse of the proton electrochemical gradient due to salinity activation of H+/OH channels [8], the cell is no longer able to import Cl [22] and export Na+ [23]. Shepherd et al. [15] designated the spontaneous repetitive APs as "point of no return" at the time of saline stress. As Characeae are sister plants to ancestors of land plants [24,25], the lesson of AP contribution to salt stress may be also relevant to agriculturally important plants.


    5. Conclusion

    The detailed modeling of the Chara AP undergoing salinity stress revealed complex and variable form. The main features of the saline APs could be fitted by the Thiel-Beilby model, varying the parameters of the Ca2+ pump (Ca2+ removal from the cytoplasm), and the activation/inactivation of the Ca2+ channels on the inside stores (Ca2+ inflow into cytoplasm). The initial activating concentration of IP3 also influenced the AP form. Experiments to explore saline Chara AP:

    (1) The excitability of Chara cells can be irreversibly abolished by exposure to La3+ [26] and the cells can then be tested for longer survival in Saline APW, compared to untreated cells.

    (2) The external Ca2+ is important in cell survival in saline stress [15]. The saline AP can be explored with different Ca2+ content of saline medium. Voltage clamp experiments will resolve whether Ca2+ comes from the outside or internal stores.

    (3) Is Na+ the only ion that modifies the AP form? A range of ions similar to Na+ should be tested.


    Acknowledgments

    MJB thanks Dr. Vadim Volkov for stimulating discussions at the time of modeling of the saline APs and to referees for constructive comments.


    Conflicts of Interest

    All authors declare no conflicts of interest in this paper.




    [1] W. M. Abd-Elhameed, Y. H. Youssri, Numerical solutions for Volterra-Fredholm-Hammerstein integral equations via second kind Chebyshev quadrature collocation algorithm, Adv. Math. Sci. Appl., 24 (2014), 129–141.
    [2] J. Banas, K. Goebel, Measure of noncompactness in Banach spaces, In: Lecture Notes in Pure and Applied Matyenath, Marcel Dekker, New York, 1980.
    [3] K. Deimling, Multivalued differential equations, De Gruyter, Berlin, 1992.
    [4] C. Dineshkumar, R. Udhayakumar, V. Vijayakumar, K. S. Nisar, A. Shukla, A note concerning to approximate controllability of Atangana-Baleanu fractional neutral stochastic systems with infinite delay, Chaos Soliton. Fract., 157 (2022), 1–17. https://doi.org/10.1016/j.chaos.2022.111916 doi: 10.1016/j.chaos.2022.111916
    [5] Z. Fu, L. Yang, Q. Xi, C. Liu, A boundary collocation method for anomalous heat conduction analysis in functionally graded materials, Comput. Math. Appl., 88 (2021), 91–109. https://doi.org/10.1016/j.camwa.2020.02.023 doi: 10.1016/j.camwa.2020.02.023
    [6] J. W. He, Y. Liang, B. Ahmad, Y. Zhou, Nonlocal fractional evolution inclusions of order α(1,2), Mathematics, 209 (2019), 1–17. https://doi.org/10.3390/math7020209 doi: 10.3390/math7020209
    [7] S. Ji, G. Li, M. Wang, Controllability of impulsive differential systems with nonlocal conditions, Appl. Math. Comput., 217 (2011), 6981–6989. https://doi.org/10.1016/j.amc.2011.01.107 doi: 10.1016/j.amc.2011.01.107
    [8] M. Kamenskii, V. Obukhovskii, P. Zecca, Condensing multivalued maps and semilinear differential inclusions in Banach spaces, De Gruyter, 2001. https://doi.org/10.1515/97831108
    [9] K. Kavitha, V. Vijayakumar, R. Udhayakumar, C. Ravichandran, Results on controllability of Hilfer fractional differential equations with infinite delay via measures of noncompactness, Asian J. Control., 24 (2021), 1406–1415. https://doi.org/10.1002/asjc.2549 doi: 10.1002/asjc.2549
    [10] K. Kavitha, K. S. Nisar, A. Shukla, V. Vijayakumar, S. Rezapour, A discussion concerning the existence results for the Sobolev-type Hilfer fractional delay integro-differential systems, Adv. Differ. Equ., 2021 (2021), 467. https://doi.org/10.1186/s13662-021-03624-1 doi: 10.1186/s13662-021-03624-1
    [11] A. A. Kilbas, H. M. Srivastava, J. J. Trujillo, Theory and applications of fractional differential equations, Elsevier, Amsterdam, 2006.
    [12] X. Liu, Z. Liu, M. Bin, The solvability and optimal controls for some fractional impulsive equations of order 1<α<2, Abstr. Appl. Anal., 2014 (2014), 1–9. https://doi.org/10.1155/2014/142067 doi: 10.1155/2014/142067
    [13] Y. K. Ma, M. M. Raja, K. S. Nisar, A. Shukla, V. Vijayakumar, Results on controllability for Sobolev type fractional differential equations of order 1<r<2 with finite delay, AIMS Math., 7 (2022), 10215–10233. https://doi.org/10.3934/math.2022568 doi: 10.3934/math.2022568
    [14] Y. K. Ma, C. Dineshkumar, V. Vijayakumar, R. Udhayakumar, A. Shukla, K. S. Nisar, Approximate controllability of Atangana-Baleanu fractional neutral delay integrodifferential stochastic systems with nonlocal conditions, Ain Shams Eng. J., 14 (2023), 1–13. 101882. https://doi.org/10.1016/j.asej.2022.101882 doi: 10.1016/j.asej.2022.101882
    [15] Y. K. Ma, M. M. Raja, V. Vijayakumar, A. Shukla, W. Albalawi, K. S. Nisar, Existence and continuous dependence results for fractional evolution integrodifferential equations of order r(1,2), Alex. Eng. J., 61 (2022), 9929–9939. https://doi.org/10.1016/j.aej.2022.03.010 doi: 10.1016/j.aej.2022.03.010
    [16] K. S. Miller, B. Ross, An introduction to the fractional calculus and fractional differential equations, Wiley, New York, 1993.
    [17] M. M. Raja, A. Shukla, J. J. Nieto, V. Vijayakumar, K. S. Nisar, A note on the existence and controllability results for fractional integrodifferential inclusions of order r(1,2] with impulses, Qual. Theor. Dyn. Syst., 21 (2022), 1–41. https://doi.org/10.1007/s12346-022-00681-z doi: 10.1007/s12346-022-00681-z
    [18] M. M. Raja, V. Vijayakumar, A. Shukla, K. S. Nisar, S. Rezapour, New discussion on nonlocal controllability for fractional evolution system of order 1<r<2, Adv. Differ. Equ., 2021 (2021), 481. https://doi.org/10.1186/s13662-021-03630-3 doi: 10.1186/s13662-021-03630-3
    [19] M. M. Raja, V. Vijayakumar, A. Shukla, K. S. Nisar, N. Sakthivel, K. Kaliraj, Optimal control and approximate controllability for fractional integrodifferential evolution equations with infinite delay of order r(1,2), Optim. Contr. Appl. Met., 43 (2022), 996–1019. https://doi.org/10.1002/oca.2867 doi: 10.1002/oca.2867
    [20] M. M. Raja, V. Vijayakumar, Optimal control results for Sobolev-type fractional mixed Volterra-Fredholm type integrodifferential equations of order 1<r<2 with sectorial operators, Optim. Contr. Appl. Met., 43 (2022), 1314–1327. https://doi.org/10.1002/oca.2892 doi: 10.1002/oca.2892
    [21] M. M. Raja, V. Vijayakumar, A. Shukla, K. S. Nisar, H. M. Baskonus, On the approximate controllability results for fractional integrodifferential systems of order 1<r<2 with sectorial operators, J. Comput. Appl. Math., 415 (2022), 1–12. https://doi.org/10.1016/j.cam.2022.114492 doi: 10.1016/j.cam.2022.114492
    [22] M. M. Raja, V. Vijayakumar, New results concerning to approximate controllability of fractional integrodifferential evolution equations of order 1<r<2, Numer. Meth. Part. D. E., 38 (2022), 509–524. https://doi.org/10.1002/num.22653 doi: 10.1002/num.22653
    [23] H. Mönch, Boundary value problems for nonlinear ordinary differential equations of second order in Banach spaces, Nonlinear Anal.-Real, 4 (1980), 985–999. https://doi.org/10.1016/0362-546X(80)90010-3 doi: 10.1016/0362-546X(80)90010-3
    [24] D. O'Regan, R. Precup, Existence criteria for integral equations in Banach spaces, J. Inequal. Appl., 6 (2001), 77–97. https://doi.org/10.1155/S1025583401000066 doi: 10.1155/S1025583401000066
    [25] A. E. Ofem, A. Hussain, O. Joseph, M. O. Udo, U. Ishtiaq, H. Al Sulami, et al., Solving fractional Volterra-Fredholm integro-differential equations via A iteration method, Axioms, 11 (2022), 470. https://doi.org/10.3390/axioms11090470 doi: 10.3390/axioms11090470
    [26] A. E. Ofem, U. Udofia, D. I. Igbokwe, A robust iterative approach for solving nonlinear volterra delay integro-differential equations, Ural Math. J., 7 (2021), 59–85. https://doi.org/10.15826/umj.2021.2.005 doi: 10.15826/umj.2021.2.005
    [27] G. A. Okeke, A. E. Ofem, T. Abdeljawad, M. A. Alqudah, A. Khan, A solution of a nonlinear Volterra integral equation with delay via a faster iteration method, AIMS Math., 8 (2022), 102–124. https://doi.org/10.3934/math.2023005 doi: 10.3934/math.2023005
    [28] G. A. Okeke, A. E. Ofem, A novel iterative scheme for solving delay differential equations and nonlinear integral equations in Banach spaces, Math. Method. Appl. Sci., 45 (2022), 5111–5134. https://doi.org/10.1002/mma.8095 doi: 10.1002/mma.8095
    [29] R. Patel, A. Shukla, J. J. Nieto, V. Vijayakumar, S. S. Jadon, New discussion concerning to optimal control for semilinear population dynamics system in Hilbert spaces, Nonlinear Anal.-Model., 27 (2022), 496–512. https://doi.org/10.15388/namc.2022.27.26407 doi: 10.15388/namc.2022.27.26407
    [30] R. Patel, A. Shukla, S. S. Jadon, Existence and optimal control problem for semilinear fractional order (1,2] control system, Math. Method. Appl. Sci., 2020, 1–12. https://doi.org/10.1002/mma.6662
    [31] I. Podlubny, Fractional differential equations, An introduction to fractional derivatives, fractional differential equations, to method of their solution and some of their applications, San Diego, CA: Acad. Press, 1999.
    [32] H. Qin, X. Zuo, J. Liu, L. Liu, Approximate controllability and optimal controls of fractional dynamical systems of order 1<q<2 in Banach spaces, Adv. Differ. Equ., 73 (2015), 1–17. https://doi.org/10.1186/s13662-015-0399-5 doi: 10.1186/s13662-015-0399-5
    [33] C. Ravichandran, D. Baleanu, On the controllability of fractional functional integro-differential systems with an infinite delay in Banach spaces, Adv. Differ. Equ., 291 (2013), 1–13. https://doi.org/10.1186/1687-1847-2013-291 doi: 10.1186/1687-1847-2013-291
    [34] L. Shu, X. B. Shu, J. Mao, Approximate controllability and existence of mild solutions for Riemann-Liouville fractional Stochastic evolution equations with nonlocal conditions of order 1<α<2, Fract. Calc. Appl. Anal., 22 (2019), 1086–1112. http://dx.doi.org/10.1515/fca-2019-0057 doi: 10.1515/fca-2019-0057
    [35] X. B. Shu, Q. Wang, The existence and uniqueness of mild solutions for fractional differential equations with nonlocal conditions of order 1<α<2, Comput. Math. with Appl., 64 (2012), 2100–2110. http://dx.doi.org/10.1016/j.camwa.2012.04.006 doi: 10.1016/j.camwa.2012.04.006
    [36] X. B. Shu, F. Xu, Upper and lower solution method for factional evolution equations with order 1<α<2, J. Korean Math. Soc., 51 (2014), 1123–1139. https://doi.org/10.4134/JKMS.2014.51.6.1123 doi: 10.4134/JKMS.2014.51.6.1123
    [37] A. Shukla, V. Vijayakumar, K. S. Nisar, A new exploration on the existence and approximate controllability for fractional semilinear impulsive control systems of order r(1,2), Chaos Soliton. Fract., 154 (2022), 1–8. https://doi.org/10.1016/j.chaos.2021.111615 doi: 10.1016/j.chaos.2021.111615
    [38] S. Sivasankaran, M. M. Arjunan, V. Vijayakumar, Existence of global solutions for second order impulsive abstract partial differential equations, Nonlinear Anal.-Theor., 74 (2011), 6747–6757. https://doi.org/10.1016/j.na.2011.06.054 doi: 10.1016/j.na.2011.06.054
    [39] Z. Tang, Z. Fu, H. Sun, X. Liu, An efficient localized collocation solver for anomalous diffusion on surfaces, Fract. Calc. Appl. Anal., 24 (2021), 865–894. https://doi.org/10.1515/fca-2021-0037 doi: 10.1515/fca-2021-0037
    [40] V. Vijayakumar, C. Ravichandran, K. S. Nisar, K. D. Kucche, New discussion on approximate controllability results for fractional Sobolev type Volterra-Fredholm integro-differential systems of order 1<r<2, Numer. Meth. Part. D. E., 2021, 1–19. https://doi.org/10.1002/num.22772
    [41] V. Vijayakumar, K. S. Nisar, D. Chalishajar, A. Shukla, M. Malik, A. Alsaadi, et al., A note on approximate controllability of fractional semilinear integro-differential control systems via resolvent operators, Fractal Fract., 6 (2022), 1–14. https://doi.org/10.3390/fractalfract6020073 doi: 10.3390/fractalfract6020073
    [42] J. R. Wang, M. Feckan, Y. Zhou, On the new concept of solutions and existence results for impulsive fractional evolution equations, Dynam. Part. Differ. Eq., 8 (2011), 345–361.
    [43] J. R. Wang, X. Li, W. Wei, On the natural solution of an impulsive fractional differential equations of order q(1,2), Commun. Nonlinear. Sci., 17 (2012), 4384–4394. https://doi.org/10.1016/j.cnsns.2012.03.011 doi: 10.1016/j.cnsns.2012.03.011
    [44] X. Wang, X. B. Shu, The existence of positive mild solutions for fractional differential evolution equations with nonlocal conditions of order 1<α<2, Adv. Differ. Equ., 159 (2015), 1–15. https://doi.org/10.1186/s13662-015-0461-3 doi: 10.1186/s13662-015-0461-3
    [45] Q. Xi, Z. Fu, T. Rabczuk, D. Yin, A localized collocation scheme with fundamental solutions for long-time anomalous heat conduction analysis in functionally graded materials, Int. J. Heat Mass Tran., 64 (2012), 2100–2110. https://doi.org/10.1016/j.ijheatmasstransfer.2021.121778 doi: 10.1016/j.ijheatmasstransfer.2021.121778
    [46] Y. H. Youssri, R. M. Hafez, Chebyshev collocation treatment of Volterra-Fredholm integral equation with error analysis, Arab. J. Math., 9 (2020), 471–480. https://doi.org/10.1007/s40065-019-0243-y doi: 10.1007/s40065-019-0243-y
    [47] Y. Zhou, Basic theory of fractional differential equations, World Scientific, Singapore, 2014. https://doi.org/10.1142/9069
    [48] Y. Zhou, Fractional evolution equations and inclusion: Analysis and control, Elsevier, New York, 2015.
    [49] Y. Zhou, J. W. He, New results on controllability of fractional evolution systems with order α(1,2), Evol. Equ. Control The., 10 (2021), 491–509. https://doi.org/10.3934/eect.2020077 doi: 10.3934/eect.2020077
  • This article has been cited by:

    1. Mary J. Beilby, Virginia A. Shepherd, Marketa Absolonova, The role of H+/OH− channels in saline pathology of Chara australis: brief history, 2018, 165, 2381-8107, 45, 10.1080/23818107.2017.1356745
    2. Qi Guo, Lei Liu, Bronwyn J. Barkla, Membrane Lipid Remodeling in Response to Salinity, 2019, 20, 1422-0067, 4264, 10.3390/ijms20174264
    3. Vladimir Sukhov, Ekaterina Sukhova, Vladimir Vodeneev, Long-distance electrical signals as a link between the local action of stressors and the systemic physiological responses in higher plants, 2019, 146, 00796107, 63, 10.1016/j.pbiomolbio.2018.11.009
    4. S. Scherzer, W. Federle, K. A. S. Al-Rasheid, R. Hedrich, Venus flytrap trigger hairs are micronewton mechano-sensors that can detect small insect prey, 2019, 5, 2055-0278, 670, 10.1038/s41477-019-0465-1
    5. V. S. Sukhov, E. M. Sukhova, D. A. Ratnitsyna, M. A. Grinberg, L. M. Yudina, V. A. Vodeneev, Theoretical Analysis of the Influence of Fluctuations in the Activity of the Plasma Membrane H+-ATPase on Low-Temperature-Induced Electrical Responses in a Plant Cell, 2020, 14, 1990-7478, 298, 10.1134/S1990747820030125
    6. Vilma Kisnieriene, Indre Lapeikaite, Vilmantas Pupkis, Mary Jane Beilby, Modeling the Action Potential in Characeae Nitellopsis obtusa: Effect of Saline Stress, 2019, 10, 1664-462X, 10.3389/fpls.2019.00082
    7. Ingo Dreyer, Nutrient cycling is an important mechanism for homeostasis in plant cells, 2021, 187, 0032-0889, 2246, 10.1093/plphys/kiab217
    8. M. J. Beilby, Chara braunii genome: a new resource for plant electrophysiology, 2019, 11, 1867-2450, 235, 10.1007/s12551-019-00512-7
    9. Michael R Blatt, A charged existence: A century of transmembrane ion transport in plants, 2024, 0032-0889, 10.1093/plphys/kiad630
    10. Michael R. Blatt, 2024, Chapter 83, 0340-4773, 10.1007/124_2024_83
    11. Vilmantas Pupkis, Judita Janužaitė, Indrė Lapeikaitė, Vilma Kisnierienė, Inositol hexakisphosphate (IP6) enhances the electrical excitability of Characean Nitellopsis obtusa, 2024, 14, 2667064X, 100618, 10.1016/j.stress.2024.100618
    12. Elizaveta Kozlova, Lyubov Yudina, Ekaterina Sukhova, Vladimir Sukhov, Analysis of Electrome as a Tool for Plant Monitoring: Progress and Perspectives, 2025, 14, 2223-7747, 1500, 10.3390/plants14101500
    13. Maria Stolarz, Integration of Plant Electrophysiology and Time-Lapse Video Analysis via Artificial Intelligence for the Advancement of Precision Agriculture, 2025, 17, 2071-1050, 5614, 10.3390/su17125614
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1876) PDF downloads(85) Cited by(7)

Article outline

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog