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    In vitro antitumor, pro-inflammatory, and pro-coagulant activities of Megalopyge opercularis J.E. Smith hemolymph and spine venom

    Ethical statement
    All methods involving human samples were performed in accordance with Institutional guidelines and regulations. Volunteers donating blood samples for experiments in this study provided a signed informed consent and remained anonymous. The donor sample consent informs and the assay involving human samples were reviewed and approved by the Institutional Ethics Committee at Autonomous University of Nuevo Leon (UANL). Experiments related to the use of animals were reviewed and approved by the Institutional Committee for Research Ethics and Animal Welfare of “The College of Biological Sciences” (CEIBA) at UANL with application number CEIBA-2017-005, following Mexican regulation NOM-062-ZOO-1999 entitled Technical Specifications for the Production, Care and Use of Laboratory Animals, normative that aligns with the guidelines and basic principles in the NIH Guide for the Care and Use of Laboratory Animals. In addition, standard ethical guidelines for ascites tumor induction in mice and rats36 were followed for experiments involving tumor cells obtained from tumor-bearing mice.
    Reagents, culture media, and tumor cell line
    Penicillin–Streptomycin solution, and RPMI 1640 and AIM-V media were obtained from Life Technologies (Grand Island, NY). Fetal bovine serum (FBS), Actinomycin D, dimethyl sulfoxide (DMSO), and 3-[4,5-dimethyl thiazol-2-yl]-2,5-diphenyltetrazolium bromide (MTT) were purchased from Sigma-Aldrich (St. Louis, MO). Taq & Go Master Mix 5X, pGEM-T Easy plasmid, and all molecular biology reagents were obtained from Promega (Madison, WI). Oligonucleotides were synthesized by Integrated DNA Technologies (UNIPARTS S.A., Monterrey, N.L., Mexico).
    The tumor cell line L5178Y-R (mouse DBA/2 lymphoma) was obtained from The American Type Culture Collection (Rockville, MD), and maintained in culture flasks with RPMI 1640 medium supplemented with 10% FBS, 1% L-glutamine, and 0.5% Penicillin–Streptomycin solution (referred as complete RPMI 1640 medium) at 37 ºC, in a humidified atmosphere of 5% CO2 in air. Cellular density was kept between 105 and 106 cells/mL.
    Animals and tumor intraperitoneal implantation
    Six- to eight-week old BALB/c female mice were purchased from Harlan Mexico S.A. de C.V. (Mexico, D.F.). Regarding housing conditions, up to five animals per cage were kept in a pathogen- and stress-reduced environment at 24 °C, under a light–dark cycle (light phase, 06:00–18:00 h) in a One Cage 2100 System (Lab Products, Inc., Seaford, DE) and given water and food ad libitum36. Three mice were used for L5178Y-R lymphoma induction, which was performed by intraperitoneal (i.p.) administration of 0.2 mL of L5178Y-R tumor cells suspension (5 × 106 cells/mouse). After 13 d inoculation, mice were euthanized by cervical dislocation and peritoneal cavity ascites was collected. The ascites suspension was placed in a 50 mL tube containing 10 mL PBS for in vitro cytotoxicity assays37.
    Insect source and rearing conditions
    Venomous caterpillars were collected from the escarpment live oak Quercus virginiana var. fusiformis Mill. (Fagaceae) trees growing in the Cumbres National Park of Sierra Madre Oriental, in Monterrey, Nuevo Leon, located northeastern México at 25° 42′ 28.8″ N and 100° 22′ 11.4″ W. Insects collection was performed with a collaboration of Biological Science College (UANL) and the Environmental Education Program of the Wild-Life Cumbres National Park of Nuevo Leon State (Parques y Vida Silvestre, https://www.nl.gob.mx/servicios/programa-de-educacion-ambiental). Collected larvae and escarpment live oak leaves were placed inside of a 2-L glass jar with a 2 cm × 2 cm open square metallic cap, covered with wire mesh screen for air exchange. Collected material was transported to the laboratory for larval rearing. Jars with larvae and leaves were incubated at 25  ± 2 °C, 65% ± 5% relative humidity, and 16:8 h light:darkness cycles, inside of a rearing insect room. Larvae were fed on fresh escarpment live oak leaves, previously rinsed in tap water for 30 s. Incubated larvae were tested after reaching the fourth instar. Extra reared larvae were kept feeding until reaching the pupa stage, followed by adults’ emergence, in order to generate and maintain new insect colonies for further experiments.
    Caterpillar venom molecular identification
    DNA from three fourth instar caterpillar larvae was extracted, using the Wizard Genomic DNA Purification Kit (Promega) and following the isolating genomic DNA from tissue culture cells and animal tissue protocol. DNA extract was used as a template for PCR amplification of specific primers for the cytochrome oxidase subunit (COI) F1 5′AAC WYT ATA YTT TAT TTT TGG 3′ R and 5′TGT TGR TAW ARR ATW GGR TC 3′, designed from Genbank Megalopyge genus sequences.
    PCR was performed using GoTaq Green Master Mix (Promega) in a 50 µL volume, with 100 ng of DNA as template and 1 µM of forward and reverse primers. Thermal cycling conditions included an initial denaturation step at 94 °C for 10 min, followed by 35 cycles of denaturation at 94 °C for 40 s, annealing at 60 °C for 40 s, and elongation at 72 °C for 2 min.
    Amplified PCR products were ligated into pGEM-T Easy (Promega) in competent E. coli TOP-10 cells. Detected plasmids were purified using the Wizard Plus SV Minipreps DNA Purification System. Sanger sequencing was performed with standard vector M13F and M13R primers by the Instituto de Biotecnología at Universidad Nacional Autónoma de México. The sequence obtained was analyzed on platform Boldsystem.
    HEV and SSV spine setae samples
    HEV was obtained by performing a puncture on the third false leg from each larva head. Released fluid (~ 200 µL) was collected and centrifuged at 9,600 rpm for 2 min. The resulting supernatant protein content was quantified on a NanoDrop Lite kit and adjusted to 1 mg/mL. This was used as a stock for further dilution and dosage preparations38. In addition, SSV was obtained from four reared fourth instar venomous caterpillars, extracted according to da Silva et al.39. Spine setae were cut from the caterpillars’ integument, homogenized, sonicated in sterile PBS, and processed as described for HEV.
    HEV and SSV cytotoxicity against murine L5178Y-R lymphoma cells
    To determine the direct in vitro effect of HEV and SSV on tumor cell growth, L5178Y-R cell suspensions (from i.p. lymphoma grown in female BALB/c mice as explained above) were adjusted to 5 × 104 cells/mL in complete RPMI 1640 medium. We evaluated the antitumor effect of a broad range of concentrations of HEV and SSV, following the cytotoxicity assay previously described15. One hundred microliters of the cell suspensions were then added to flat-bottomed 96-well plates (Becton Dickinson, Lincoln Park, NJ), containing triplicate cultures (100 µL) of complete RPMI 1640 medium (unstimulated control), HEV or SSV (7.8–500 µg/mL)37, using 3.1–125 µg/mL Vincristine (Sigma-Aldrich), as positive control. After incubation for 44 h at 37 °C in 5% CO2, MTT (0.5 mg/mL, final concentration) was added, and cultures were incubated for additional 4 h. Cell cultures were then incubated for 16 h with 100 µL DMSO to dissolve formazan crystals, and optical densities (ODs) were read in a microplate reader (Bio-Tek Instruments, Inc., Winooski, VT) at 540 nm37. Percentage of cytotoxicity was calculated as follows:

    $$ % {text{ Cytotoxicity}}, = ,{1}00 – left[ {left( {{text{OD}}_{{{54}0}} {text{in HEV{-} or SSV{-}treated cells}}/{text{OD}}_{{{54}0}} {text{in untreated cells}}} right), times ,{1}00} right]. $$

    The Statistical Package for the Social Sciences version 17.040, was used to calculate the inhibitory concentration at 95% (IC95), selecting the Probit analysis.
    Apoptosis assay
    Cellular death type resulting from HEV- or SSV-mediated L5178Y-R cytotoxicity was determined according to Reyna-Martínez et al.41. For this, 3 × 106 cells were exposed to HEV or SSV IC50 using flat-bottomed, 24-well plates (Becton Dickinson), and incubated for 24 h under the same conditions as for the cytotoxicity assay. Treated cells were aliquoted into microtubes, washed by centrifugation at 9,600 rpm (Sorvall ST16R Centrifuge; ThermoScientific, Pittsburgh, PA), and suspended in 500 μL of complete RPMI 1640 medium. Cells were then stained adding 1 μL of 100 μg/mL acridine orange and 1 μL of 100 μg/mL ethidium bromide, and incubated for 5 min. Next, cultured cells were washed three times by centrifugation 9600 rpm with 1 mL PBS and suspended in 100 μL of PBS 1×, after which 10 μL of cell suspension samples were observed in a fluorescence microscope adapted with a rhodamine filter (540–570 nm), using Actinomycin D (800 ng/mL) as positive control.
    Uniform green stained cells were quantified as viable cells and spotty green or granular core cells were quantified as in early apoptosis. Orange dots or cells with large granules similar to those observed in early-apoptosis cells were quantified as in late apoptosis, whereas uniform orange hue cells were quantified as in necrosis42.
    Staining cells results were validated by the DNA degradation method41, where DNA like-ladder fragmentation indicates apoptotic activity, whereas DNA smear represents cell death by necrosis. DNA extracted from 1 × 106 cells per treatment were tested using the AxyPrep Multisource Genomic DNA Miniprep kit (Axygen) in 1% agarose gel electrophoresis at 100 V for one hour. The gel was then stained with 5 ng/mL ethidium bromide and analyzed on a GelDoc XR photo-documenter (Bio Rad, Berkeley, CA).
    Lymphocyte proliferation assay
    The effect of venom caterpillar HEV and SSV extracts on murine lymphocyte proliferation was determined by the MTT reduction colorimetric technique37. Two mice were euthanized and thymuses were immediately removed after mice death, a single cell-suspension was prepared by disrupting the organs in RPMI 1640 medium, as previously reported37. Cell suspensions were then washed three times in this medium, suspended, and adjusted to 1 × 107 cells/mL in complete RPMI 1640 medium. One hundred microliters of thymus cell suspensions were added to flat-bottomed 96-well plates (Becton Dickinson) containing triplicate cultures (100 µL) of complete RPMI 1640 medium (unstimulated control), HEV and SSV at 7.8, 15.6, 31.25, 62.5, 125, 250, and 500 µg/mL15,37, and the positive control Concanavalin A (6.25 μg/mL) for 48 h at 37 °C in 95% air-5% CO2 atmosphere. After 44 h of incubation, MTT (0.5 mg/mL, final concentration) was added, and cultures were incubated for additional 4 h. Cell cultures were then incubated for 16 h with 100 µL of DMSO and ODs, resulting from dissolved formazan crystals, were then read in a microplate reader (DTX 880 Multimode detector, Becton Dickinson, Austria) at 570 nm37. To calculate the lymphoproliferation index, the obtained values between the samples were compared. For this, values recorded by extracts treated cells were divided with the value given by Concanavalin A (tested as mouse T-cell mitogen) as follows: OD570 in treated cells/OD570 in Concanavalin A treated cells. Therefore, all values were compared with the control, where the lowest concentrations have a value of 1, since there was no difference compared with the control.
    Human peripheral blood mononuclear cells (hPBMC) cytokine response to M. opercularis extracts
    Cytokine production by hPBMC was measured after HEV and SSV extracts exposure. For this, hPBMC were isolated with Ficoll-Paque Plus (GE Healthcare, Uppsala, Sweden) and adjusted to 1 × 106 cells/mL in complete RPMI 1640 medium. One hundred microliters of the cell suspension were placed in a 96-well plate in the presence or absence (untreated control) of 100 μL of HEV or SSV M. opercularis extracts at 3.91, 7.81, 15.62, 31.25, 62.5, and 125 µg/mL15 in complete RPMI 1640 medium. Plates were then incubated at 37 °C for 48 h and centrifuged at 400 rpm for 5 min.
    Cell‐free supernatants were then subjected to IL-1β, IL-6, IL-8, and TNF-α levels determination by cytometric bead arrays (CBA) (BD Biosciences, San Jose, CA) on a BD Accuri C6 Flow Cytometer Sampler (BD Biosciences, Ann Arbor, MI), following manufacturer’s instructions, and data analyzed with the FCAP Array v3.0 (SoftFlow Inc.). Results were adjusted by subtracting the basal levels of cytokines from untreated hPBMC (negative control) and data analyzed by Prism 6 software (GraphPad Software Inc., La. Jolla, CA)43.
    Coagulation assay
    The effect of HEV and SSV activity on plasma coagulation was assessed, using the re-calcification time assay44, adapted for a microplate reader. For this, 1 mg/mL HEV and SSV reactive samples were prepared in 20 mM Tris–HCl buffer pH 7.4 and sterilized by filtration with a 0.22 μm micropore filter. Reactive samples consisted of 50 μL of citrated human plasma, 50 μL of HEV or SSV samples at 250 μg/mL (based on the concentration that produced maximal cytotoxicity in lymphoma cells), and 100 μL Tris–HCl buffer to a final volume of 200 μL. They were then incubated for 5 min at 37 °C, after which 10 μL of 150 mM CaCl2 were added for coagulative process re-activation, following the reaction during 23 min at 37 °C, and ODs were read in a microplate reader (Bio-Tek Instruments, Inc.) at 565 nm.
    Statistical analysis
    Results were expressed as means ± SD of triplicate determinations from three independent experiments. Statistical significance (p ≤ 0.05) was assessed by one-way analysis of variance and by the Student’s t test. More

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    Invasion front dynamics in disordered environments

    Effective medium approximation
    For a linearized version of Fisher , we first obtain the effect of disorder on invasion velocity using Effective Medium Approximation (EMA). Using the EMA approach, we can replace the spatially irregular diffusion constant with a uniform one in which the effective diffusivity is everywhere equal to (D_{e}) and can replace (D_0(1+xi f(x))) in Eq. (2)44. To obtain (D_{e}), one needs to discretize Eq. (2) using a finite-difference method, which leads to the following equation for the population density39:

    $$begin{aligned} frac{partial C_i(t)}{partial t}=sum _{jin {i}}W_{ij}[C_j(t)-C_i(t)]+RC_i(t) ;, end{aligned}$$
    (4)

    where j belongs to nearest neighbors of i and (W_{ij}=D_{ij}/delta ^2) stands for the density flow rate between units i and j with distance of (delta). Due to spatially irregular diffusion constant we have (W_{ij}=W_0(1+xi f_{ij})). Following44, one has

    $$begin{aligned} D_e=bigg ( int _{0}^{infty } frac{g(w) dw}{w} bigg )^{-1} end{aligned}$$
    (5)

    where g(w) is probability density function for (W_{ij}). Applying this approach to our case leads to

    $$begin{aligned} D_{e}=D_0(1-|xi |^2/3) end{aligned}$$
    (6)

    where (|xi |) stands for dimensionless magnitude of (xi) ((xi ^2) has a physical dimension of length, meter).
    Perturbation analysis for invasion front fluctuations
    The first step towards the study of dynamics of a propagating front is linearizing (3) by neglecting the (C^2) term in an environment with effect diffusion constant, (D_e), as:

    $$begin{aligned} frac{partial C}{partial t}=RC+frac{partial }{partial x} big ((D_e + xi f(x)) frac{partial }{partial x} Cbig ) end{aligned}$$
    (7)

    This is based on the fact that near the front, the cell density is (C ll 1). In other words, focusing on the dynamics of the front position, automatically grants us the possibility of linearizing (3).
    The construction of the solution can be proceeded according to a valuable insight given in the classic paper43, where the particle density is written as follows

    $$begin{aligned} C(zeta ,t) approx C_0(zeta +eta (t),t)+ delta C_1(zeta ,t) end{aligned}$$
    (8)

    where C is written in the comoving frame and (zeta = x-vt). It is assumed that (delta C_1 ll 1) and in the same order as the perturbing function. So that terms containing f(x) and (delta C_1) can be neglected. Furthermore, (C_0) is assumed to satisfy the linearized Eq. (3) with (xi =0), i.e.

    $$begin{aligned} frac{partial C_0(zeta ,t)}{partial t}-hat{Gamma } C_0(zeta )=,& {} frac{partial C_0(zeta ,t)}{partial t} nonumber \&-bigg (D_e frac{d^2}{dzeta ^2} + vfrac{d}{dzeta } + Rbigg )C_0(zeta ,t) = 0 end{aligned}$$
    (9)

    Which has the following solution

    $$begin{aligned} C_0(zeta ,t) = frac{1}{sqrt{4pi D_e t}}e^{-frac{1}{2}sqrt{frac{R}{D_e}}zeta }e^{-frac{zeta ^2}{4D_e t}} end{aligned}$$
    (10)

    The first term in (8) describes the effects of the perturbing function f(x) on the position of the propagating front, while the second term shows the change in the shape of the front. This approach has also been employed and well explained in a recent paper17. As shown in17,43, to determine the effective diffusion coefficient for the fluctuating front, it is sufficient to solve (3) using (8) for (eta (t)). Note also that since we are interested in the dynamics of the system in long times ((t gg frac{1}{R})), (v_e) can be assumed to be equal to (2sqrt{R D_e})45. Plugging (8) in Eq. (9) expressed in comoving coordinates and considering (xi =bar{xi }/D_e) yields

    $$begin{aligned} frac{partial delta C_1}{partial t} – hat{Gamma }delta C_1 + dot{eta (t)} C_0(zeta ,t) = bar{xi } bigg (f(zeta ) C’_0(zeta ,t)bigg )’ end{aligned}$$
    (11)

    Noting that the operator (hat{Gamma }) is not self-adjoint (The adjoint of (hat{Gamma }) is: (hat{Gamma ^dagger }=D_e dfrac{d^2}{dzeta ^2} – v_e dfrac{d}{dzeta } + R)) and following43, we multiply Eq. (11) from the left in the eigenfunction of (hat{Gamma ^dagger }) with 0 eigenvalue (which is (e^{sqrt{frac{R}{D_e}}zeta })) and integrate. Thus,

    $$begin{aligned} {,} & int _{-infty }^{infty } e^{sqrt{frac{R}{D_e}}zeta }frac{partial delta C_1(zeta ,t)}{partial t} dzeta + dot{eta (t)}int _{-infty }^{infty }e^{sqrt{frac{R}{D_e}}zeta }C’_0(zeta ,t) dzeta nonumber \&quad = bar{xi } int _{-infty }^{infty } e^{sqrt{frac{R}{D_e}}zeta }bigg (f(zeta ) C’_0(zeta ,t)bigg )’dzeta end{aligned}$$
    (12)

    Which yields

    $$begin{aligned} dot{eta }(t) =bar{xi } dfrac{int _{-infty }^{infty } e^{sqrt{frac{R}{D_e}}zeta }bigg (f(zeta ) C’_0(zeta ,t)bigg )’ dzeta }{int _{-infty }^{infty }e^{sqrt{frac{R}{D_e}}zeta }C’_0(zeta ,t) dzeta } end{aligned}$$
    (13)

    Which can further be simplified into

    $$begin{aligned} dot{eta }(t)=,& {} bar{xi }dfrac{int _{-infty }^{infty } e^{sqrt{frac{R}{D_e}}zeta }f(zeta ) C’_0(zeta ,t) dzeta }{int _{-infty }^{infty }e^{sqrt{frac{R}{D_e}}zeta }C_0(zeta ,t) dzeta } nonumber \=,& {} bar{xi } e^{-frac{R t}{4}}int _{-infty }^{infty } e^{sqrt{frac{R}{D_e}}zeta }f(zeta ) C’_0(zeta ,t) dzeta end{aligned}$$
    (14)

    Or equivalently,

    $$begin{aligned} eta (t) =bar{xi } int _{0}^{t} dtau e^{-frac{Rtau }{4}} int _{-infty }^{infty } dzeta e^{sqrt{frac{R}{D_e}}zeta }f(zeta ) C’_0(zeta ,tau ) end{aligned}$$
    (15)

    According to17, the effective diffusion would be given by

    $$begin{aligned} D_{C} = dfrac{langle eta ^2(t) rangle }{2t} end{aligned}$$
    (16)

    Or

    $$begin{aligned} D_{C}=,& {} frac{bar{xi }^2}{2t}int _{0}^{t}d{T_1}int _{0}^{t}d{T_2}int _{-infty }^{infty } C’_0(zeta ,T_1)C’_0(zeta ,T_2) nonumber \&times e^{-frac{R T_1}{4}}e^{-frac{R T_2}{4}}e^{2sqrt{frac{R}{D_e}}zeta } dzeta end{aligned}$$
    (17)

    where we have performed an ensemble average over (eta ^2(t)) using the fact that (langle f(x)f(y) rangle = delta (x-y)). A numerical calculation of (16) can be readily computed using any mathematical software. However, valuable insight can still be obtained from (17), if we use dimensionless parameters (tau _i=frac{T_i}{t}) and (sigma =sqrt{frac{R}{D_0}}zeta). In other words

    $$begin{aligned} D_{C}=,& {} bar{xi }^2 dfrac{sqrt{R}}{32pi D^{3/2}_0}int _{0}^{1}dtau _1int _{0}^{1}dtau _2int _{-infty }^{infty }dsigma nonumber \&times dfrac{left( 1+dfrac{sigma }{Rttau _1}right) left( 1+dfrac{sigma }{Rttau _2}right) }{sqrt{tau _1tau _2}}e^{-frac{sigma ^2}{4Rttau _1}}e^{-frac{sigma ^2}{4Rttau _2}}e^{sigma }e^{-R tfrac{(tau _1+tau _2)}{4}} end{aligned}$$
    (18)

    Equation (18) gives the effective diffusion coefficient for the stochastic behavior of the front. For a diffusive behavior, we would expect this effective diffusion coefficient to tend to a constant at large times. At large times, we can approximate the integral as follows,

    $$begin{aligned} D_{C} approx bar{xi }^2 dfrac{sqrt{R}}{32pi D^{3/2}_e}int _{0}^{1}dtau _1int _{0}^{1}dtau _2int _{-infty }^{infty }dsigma dfrac{1}{sqrt{tau _1tau _2}}e^{-frac{sigma ^2}{4Rttau _1}} e^{-frac{sigma ^2}{4Rttau _2}}e^{sigma }e^{-R tfrac{(tau _1+tau _2)}{4}} end{aligned}$$
    (19)

    Luckily, Eq. (19) can be evaluated exactly to yield

    $$begin{aligned} D_{C}&approx bar{xi }^2 dfrac{sqrt{R}}{32pi D^{3/2}_e} nonumber \&quad frac{2 pi (2 R t-1) text {Erf}left( frac{sqrt{R t}}{2}right) -2 pi e^{2 R t} text {Erf}left( frac{3 sqrt{R t}}{2}right) +2 pi e^{2 R t} text {Erf}left( sqrt{2} sqrt{R t}right) +8 sqrt{pi } e^{-frac{1}{4} (R t)} sqrt{R t}-4 sqrt{2 pi } sqrt{R t}}{R t} end{aligned}$$
    (20)

    Where Erf(x) is the error function. As (trightarrow infty) this gives the following simple relation for the diffusion constant for the wave front

    $$begin{aligned} D_{C}(trightarrow infty ) = bar{xi }^2 dfrac{sqrt{R}}{8 D^{3/2}_e} end{aligned}$$
    (21)

    Substituting (xi =bar{xi }/D_e) and (D_e=D_0(1-|xi |^2/3)), we will get the following beautiful equation for the effective diffusion constant of the front at large times:

    $$begin{aligned} D_{C}(trightarrow infty ) =dfrac{1}{8} xi ^2 sqrt{R D_0(1- |xi |^2/3)}. end{aligned}$$
    (22) More

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    The impact of social ties and SARS memory on the public awareness of 2019 novel coronavirus (SARS-CoV-2) outbreak

    The early warnings of the outbreak
    As early as Dec 31st, 2019, when Wuhan Municipal Health Commission first informed the public about the emerging pneumonia cases21, most of the cities (326 out of 346) exhibited at least some awareness of the emerging SARS-CoV-2 outbreak (Fig. 2b). However, awareness then decreased until Jan 19th, 2020, one day before the Chinese Centre for Disease Control and Prevention confirmed human-to-human transmissions of the novel coronavirus9. Since Jan 20th, 2020, overall awareness increased by a magnitude of at least five, demonstrating significant awareness across all cities (Fig. 2b). Awareness remained low as the epidemic spread, falling close to its lowest point on the starting day of Chunyun (Jan 10th, 2020). Considering cities that showed initial novel coronavirus awareness levels at least 1.5 times that of the search term “common cold”, we found a total of 166 alert cities as early as Dec 31st, 2019 (48 cities at a tighter threshold of (C = 3.0) times, illustrated in Fig. 2a). However, awareness decreased significantly during Chunyun.
    Figure 2

    Public awareness over time. (a) The frequency distributions of cities that exhibit the first significant signal of awareness over time. The number of cities for which searches for the combined term “Wuhan” and “pneumonia” exceed (user2{ C} = 3) times the search term “common cold” is reported every day. (b) Public awareness on the topic of “pneumonia” over time. All 346 cities exhibit at least some searches of the term “pneumonia” during the initial outbreak period. Of these, 326 cities recorded searches about it as early as Dec 31st, 2019. Cities are divided into two groups according to whether or not they had reported SARS cases in 2003–04. The mean values of awareness magnitude were computed on a daily basis for two groups of cities respectively. Accordingly, a paired t-test was performed on those two time-series, and we found the cities that had reported SARS cases had greater of awareness (t-statistic: 3.56; degrees of freedom: 23; p  More

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    Host genetic variation explains reduced protection of commercial vaccines against Piscirickettsia salmonis in Atlantic salmon

    Fish and vaccines
    Two pedigree populations of Atlantic salmon (Salmo salar) called Fanad and Lochy were used in this study18 (Table 1). The two populations were managed separately and had different origins. Fish were provided in 2016 by the salmon fish farming company Salmones Camanchaca and pit tagged in April 2016 at an average weight of 26.4 ± 3.9 g and 30.2 ± 4.2 g, for populations Fanad and Lochy, respectively. During the freshwater growth period, salmon were immunized twice using commercial vaccines, following the strict Salmones Camanchaca protocols. First, fish were vaccinated by intraperitoneal (IP) injection with a pentavalent vaccine against P. salmonis, Vibrio ordalii, A. salmonicida, IPNV (infectious pancreatic necrosis virus) and ISAV (infectious salmon anemia virus). Second, fish were immunized by IP injection against P. salmonis using a monovalent live attenuated vaccine at the same time as the first vaccination. Since 2016, this double vaccination strategy has been a common practice in the Chilean salmon industry17. Fish were transferred as smolts to the Aquadvice experimental station in Puerto Montt, Chile. Unvaccinated fish were injected with PBS (phosphate-buffered saline) and used as control (Table 1). Prior to transferring the fish, a health check by RT-PCR was performed to verify that the fish were free of viral (ISAV and IPNV) and bacterial pathogens (Vibrio sp., Flavobacterium sp., P. salmonis, and Renibacterium salmoninarum). At the experimental station, all fish underwent a 15 days acclimatization period in seawater (salinity of 32% and a temperature of 15 ± 1 °C). Fish were fed daily ad libitum with a commercial diet.
    Calculation of Piscirickettsia salmonis LD50
    The median lethal dose (LD50) of P. salmonis (EM-90 type) was determined as previously described18. Briefly, animals from both populations were distributed in eight tanks of 350 L (n = 60 fish per tank). The LD50 was calculated in fish infected by IP injection with 200 μL of a P. salmonis suspension. Three dilutions were assessed from stock with concentrations of 1 × 106.63 TCID/mL (TCID = median tissue culture infective dose): 1 × 10–3 TCID/mL, 1 × 10–4 TCID/mL, and 1 × 10–5 TCID/mL. Controls were injected with 200 μL of PBS. Fish were monitored daily for 30 days, and mortalities were recorded. The presence of bacteria was assessed by qRT-PCR. In both infection scenarios, a single infection and coinfection, the highest dose of P. salmonis was used (1 × 10–3 TCID/mL) as a conservative measure because the fish grow about 100 g between LD50 and the main challenge (50 days).
    Infection design, trait of resistance and protection added by vaccine
    Fish were treated with two different types of infection, a single infection with P. salmonis (PS) or coinfection with both C. rogercresseyi and P. salmonis (CAL + PS) as previously described18. In short, infections against P. salmonis occurred at 822 ATU (accumulated thermal units) within the immunization period described by the vaccine manufacturer. Vaccinated and unvaccinated fish from populations Fanad and Lochy were equally distributed in four tanks of 6 m3, with two replicates per type of infection. For the single infection with P. salmonis, fish were IP injected. For the coinfection, fish were exposed first to sea lice and then to P. salmonis. A coinfection procedure was established based on our previous experience with this study model45,55. Infections with sea lice were performed by adding 60 copepodites per fish to each tank of coinfection. Copepodites were collected from egg-bearing females reared in the laboratory and confirmed as “pathogen-free” (P. salmonis, R. salmoninarum, IPNV, and ISAV) by RT-PCR diagnostic. After the addition of parasites, water flow was stopped for a period of 8 h, and tanks were covered to decrease light intensity, which favors a successful settlement of sea lice on fish55. A placebo procedure was applied to single infection tanks, keeping them in darkness and controlling the volume of water, temperature, oxygen levels, and fish density equivalent to those that were measured in coinfected tanks18. The secondary infection was performed with P. salmonis after seven days of sea lice infestation, and the establishment of the parasites was confirmed and quantified on all fish. Therefore, our experimental design had two types of treatments: (1) single infection (PS) or coinfection (CAL + PS); and (2) vaccinated or unvaccinated fish. Vaccinated and unvaccinated fish with a single infection were distributed in tanks 1 and 2, and vaccinated and unvaccinated fish with a coinfection were distributed in tanks 3 and 4. Further, fish were fasted for one day prior to each procedure to minimize the detrimental effects of stress on water quality parameters. Finally, fish were sedated with AQUI-S (50% Isoeugenol, 17 mL/100 L water) to reduce stress during handling. Fish were monitored daily for 30 days, and resistance to P. salmonis was measured individually as days to death. Protection added by vaccination was calculated as the difference of resistance between vaccinated fish and their unvaccinated full-sibs and represented under a single Genetic and Environment model (GxE, G = full-sib family; E = Vaccination treatment).
    Comparison of moribund and survivor fish
    Bacterial load, growth, and macroscopic lesions were evaluated in survivors and moribund fish. Moribund fish were obtained as dying fish when 50% of mortality was reached in both a single infection and coinfection treatments. Moribund fish were recognized and collected by three behavioral traits: lethargy, no response to stimuli, and slow swimming close to the tank wall. Resistance to P. salmonis was measured by days to death and mortality (alive versus dead) and monitored for 30 days15,45, survivors fish comprised those that lived at the end of experiment15. Forty fish were collected from each group of moribund and survivors, and from each treatment (PS and CAL + PS) and comparisons were performed between unvaccinated and vaccinated fish, twenty fish each group. However, due to the low number of unvaccinated survivors fish coinfected with P. salmonis and sea lice, it was not possible to compare with the vaccinated survivors fish.
    Specific growth rate (SGR)
    SGR was evaluated for moribund and survivors fish. The specific growth rate was calculated previous to infection, and post-infection as SGR = ((lnw2 − lnw1)*t−1)*100, where w2 corresponds to final weight, w1 to the initial weight, and t corresponds to the number of days between infection and death of the fish or the end of the trial if they survived56.
    Piscirickettsia salmonis load
    Piscirickettsia salmonis load was evaluated for moribund and survivors fish. P. salmonis load was estimated based on the amount of specific ribosomal RNA from the bacteria in the head kidneys of the infected fish, as measured by qRT-PCR. Dead fish were not used to evaluate bacterial load. Threshold cycle (CT) values from bacterial RNA was used as an indication of the bacterial load as previously described18. Head kidney samples were extracted from 20 moribund and survivors fish per group and preserved in RNAlater at − 80 °C until RNA extraction. RNA was extracted from tissue samples with the TRIzol reagent (Thermo Fisher Scientific, MA, USA) following the instructions provided by the manufacturer. DNA was removed through an additional step using a DNase incubation for 60 min at 37 °C. The quality of the RNA extraction was checked by visualizing the 28S and 18S rRNA bands resolved in 1% of agarose gels stained with SYBR Safe DNA gel stain (Invitrogen, CA, USA), and the total concentration of the RNA was measured spectrophotometrically in a MaestroNano device (MAESTROGEN, Hsinchu, Taiwan). One hundred nanograms of purified total RNA was used for the qRT-PCR reactions. The qRT-PCR reaction was prepared using the Brilliant III SYBR master mix (Agilent Technologies, CA, USA) by adding the template RNA, probes, and primers as described previously57. qRT-PCR was performed in the Eco Real-Time PCR system (Illumina, CA, USA), whose results were expressed in terms of CT. All samples were tested in triplicates and were calibrated to a plate standard that contained a combination of samples from all groups tested. Primers used for 23S gene of S. salar were forward primer TCTGGGAAGTGTGGCGATAGA and reverse primer TCCCGACCTACTCTTGTTTCATC.
    Necropsy analysis
    Macroscopic lesions from 20 fish per treatment were analyzed on moribund and survivors fish13; almost all survivors sampled fish were vaccinated, except one unvaccinated fish that survived to P. salmonis infection (data not shown). Fresh samples were analyzed by two veterinarians who were blinded to the treatments. Macroscopic lesions evaluated in the tissues were peeling or undergoing desquamation, congestion, and ecchymosis in the skin, paleness, and melanomacrophages in the gills, white hepatic nodules, hepatomegaly, spleen paleness, and splenomegaly. Macroscopic lesions were indicated as present or absent.
    Statistical analysis
    Significance levels of resistant to P. salmonis were obtained using a two-way ANOVA followed by a Tukey post-hoc test and unpaired t-test. The effects of populations and sex of fish on SGR and P. salmonis load were analyzed using a non-parametric Kruskal–Wallis test followed by a Dunn post-hoc test. Additionally, differences in the clinical signs of the P. salmonis infection between different treatments were analyzed using a non-parametric Chi-square proportion. All statistical analyses were performed using R Core Team (RStudio, Vienna, Austria). Graphs were designed with GraphPad Prism 8.0 software (GraphPad Software, CA, USA).
    Quantitative genetic analysis
    Each population in this study has a different genetic origin and has been managed as closed populations during the domestication process. Thus, (co) variance components of days to death were estimated independently for each population from the data of its genealogy (Table 1) using VCE 6.0 software by Groeneveld et al.58.
    Heritability of days to death was estimated using the following univariate animal model:

    $$y = upsilon 1 , + X_{1} t + X_{2} i + X_{3} v + Za + e,,,,{text{Model}},1$$

    where y is the vector of the trait days to death, μ is the overall mean effect, t is the fixed effect of tank; i is the fixed effect of type of infection; v is the fixed effect of group of vaccination; a is the random effects vector of animal effects, with a ~ N(0, σa2A); and e is the random vector of errors, with e ~ N(0, σe2Ie). X1, X2, X3, and Z are incidence matrices, and A is the numerator relationship matrix obtained from pedigree information. The magnitude of estimated heritability was established following the classification of Cardellino and Rovira59: low (0.05–0.15), medium (0.20–0.40), and high (0.45–0.60) and very high ( > 0.65).
    Genotype–environment interactions (GxE) were estimated by means of genetic correlations between the trait days to death measured in one environment (i.e., unvaccinated and single infection with P. salmonis) and the same trait measured in the other environment (i.e., vaccinated and coinfection).
    Genetic correlations were estimated using the following bivariate animal model:

    $$y_{1} ,;y_{2} = X_{1} d + X_{2} t(d) + X_{3} i(d) + X_{4} v(d) + Za(d) + e,,,,{text{Model}},1$$

    where, y1 and y2 are the data vectors for the traits of interest (days to death in vaccinated and unvaccinated fish); d is the fixed vector of trait effects; t(d), i(d), v(d), are the fixed effects of tank, type of infection and group of vaccination effects within trait, respectively; a(d) is the random vector of animal effects within trait, with a(d) ~ N(0, A ⊗ G); and e is the random vector of errors, with e ~ N(0, I ⊗ R). The matrix G is a 2 × 2 variance–covariance matrix between traits defined by a genetic additive correlation term, rg, and a genetic variance (σgj2) for each trait. The matrix R is an unstructured 2 × 2 residual variance–covariance matrix with a different variance for each trait (σej2), and a covariance between traits (σeij). All other terms were previously defined. Correlations were classified as low (0–0.39), medium (0.40–0.59), high (0.60–0.79), and very high (0.80–1), regardless whether it was positive or negative. Significance testing of the estimates of heritability and genetic correlation were approximate as suggest by Åkesson et al.60. Thus, any genetic parameter value was considered significantly different from zero with P  More

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