J. Specifically, smaller HOMO-LUMO  gaps of the couplers and shorter coupler lengths, as well as shorter linking bond lengths, can attain stronger magnetic interactions. In addition, a diradical with an extensively π-conjugated structure is beneficial to spin transport and can effectively promote magnetic coupling, yielding a large |J| accordingly. That is, a larger spin polarization can give rise to a stronger magnetic interaction. The sign of J for these studied diradicals can be predicted from the spin alternation rule, the shape of the singly occupied molecular orbitals (SOMOs), and the SOMO-SOMO energy gaps of the triplet state. This study paves the way for the rational design of magnetic molecular switches.Redox-induced magnetic transformation in organic diradicals is an appealing phenomenon. In this study, we theoretically designed twelve couples of diradicals in which two nitroxide (NO) radical groups are connected to the redox-active couplers including p-benzoquinonyl, 1,4-naphthoquinyl, 9,10-anthraquinonyl, naphthacene-5,12-dione, pentacene-6,13-dione, hexacene-6,15-dione, pyrazinyl, quinoxalinyl, phenazinyl, 5,12-diazanaphthacene, 6,13-diazapentacene, and 6,15-diazahexacene. As evidenced at both the B3LYP and M06-2X levels of theory, the calculations reveal that the magnetic reversal can take place from ferromagnetism to antiferromagnetism, or vice versa, by means of redox method in these designed organic magnetic molecules. It was observed that p-benzoquinonyl, 1,4-naphthoquinyl, 9,10-anthraquinonyl, naphthacene-5,12-dione, pentacene-6,13-dione, and hexacene-6,15-dione-bridged NO diradicals produce antiferromagnetism while their dihydrogenated counterparts exhibit ferromagnetism. Similarly, pyrazinyl, quinoxalinyl, phenazinyl, 5,12-diazanaphthacene, 6,13-diazapentacene, and 6,15-diazahexacene-bridged NO diradicals present ferromagnetism while their dihydrogenated counterparts show antiferromagnetism. The differences in the magnetic behaviors and magnetic magnitudes of each of the twelve couples of diradicals could be attributed to their distinctly different spin-interacting pathways. It was found that the nature of the coupler and the length of the coupling path are important factors in controlling the magnitude of the magnetic exchange coupling constant  J is largely dependent on the interaction between two spin sources. The larger the |J| value is, the stronger the magnetic coupling interaction between two spin sources is. Positive and negative J values represent the ferromagnetic and antiferromagnetic interactions between two spin sources, respectively.Organic magnetic materials have received considerable attention due to their advantages, which include being lightweight, environmentally friendly, and simple to manufacture, as well as their potential applications in the fields of optics, electricity, and magnetism ,3,4,5,6.J can be changed through a redox reaction. Furthermore, we discussed diaza effects on magnetic coupling characteristics in diaza-benzo[k]tetraphene-bridged NO-based diradicals and noticed that further dielectron oxidation can significantly change the magnitude or sign of J [Organic diradicals with switchable spin states or magnetic properties have a wide range of applications in organic spintronics, molecular electronics, data storage devices, and so on ,20,21. Iign of J . Overall1a, 2a, 3a, 4a, 5a, and 6a, as illustrated in +) reduction (or dihydrogenation) process, 1a, 2a, 3a, 4a, 5a, and 6a can convert to 1b, 2b, 3b, 4b, 5b, and 6b, respectively. In addition, we also selected pyrazine, quinoxaline, phenazine, 5,12-diazanaphthacene, 6,13-diazapentacene, and 6,15-diazahexacene as coupling units with redox peculiarity and the NO radicals as spin centers to construct six other novel diradicals, that is, 1c, 2c, 3c, 4c, 5c, and 6c, as displayed in 1c, 2c, 3c, 4c, 5c, and 6c could undergo a 2e-2H+ process to convert to 1d, 2d, 3d, 4d, 5d, and 6d, respectively. The calculated results indicate that magnetic conversions between ferromagnetic and antiferromagnetic exchange coupling take place for twelve couples of designed diradicals  upon reduction or oxidization. We found that the differences in the magnetic behaviors and magnetic magnitudes of each of the twelve couples of diradicals could be attributed to their distinctly different spin-interacting pathways [J| accordingly [2a, 2b, 3a, 3b, 2c, 2d, 3c, and 3d . It is worth mentioning that nitroxide-based diradicals have been extensively studied both experimentally and theoretically to date [Inspired by the abovementioned research findings, in this study, we chose p-benzoquinone, 1,4-naphthoquinone, 9,10-anthraquinone, naphthacene-5,12-dione, pentacene-6,13-dione, and hexacene-6,15-dione as couplers with redox activity and the relatively stable NO radicals as spin centers to construct six novel diradicals, that is, pathways ,28,29,30pathways ,34,35 anordingly ,37, the ordingly , and the to date ,40,41. T1a ↔ 1b, 2a ↔ 2b, 3a ↔ 3b, 4a ↔ 4b, 5a ↔ 5b, 6a ↔ 6b, 1c ↔ 1d, 2c ↔ 2d, 3c ↔ 3d, 4c ↔ 4d, 5c ↔ 5d, and 6c ↔ 6d). The detailed data including the energies of the closed-shell singlet (CS), broken-symmetry (BS) open-shell singlet, and triplet (T) states; the <S2> values; the energy gaps between the pure singlet state and triplet state (ΔEST); and the spin coupling constants J of all studied diradicals calculated at the B3LYP/6-311++G level of theory are presented in J values estimated at the UM06-2X/6-311++G level are also shown in J values are relatively accurate by adopting B3LYP and M06-2X functionals, and the magnetic conversion through the redox modulation method occurs in these twelve couples of diradicals. Compared with the M06-2X functional, the B3LYP functional tends to overestimate J [1a, 2a, 3a, 4a, 5a, and 6a produce antiferromagnetism, while their dihydrogenated counterparts 1b, 2b, 3b, 4b, 5b, and 6b exhibit ferromagnetism. Analogously, the diradicals 1c, 2c, 3c, 4c, 5c, and 6c present ferromagnetism, while their dihydrogenated counterparts 1d, 2d, 3d, 4d, 5d, and 6d show antiferromagnetism. Moreover, we notice that the estimated |J| values for the diradicals with antiferromagnetism decrease gradually with an extension of the coupling path between two spin centers, while the J values of the diradicals with ferromagnetism decrease first and then increase. Specifically, the |J| values follow the order 1a > 2a > 3a > 4a > 5a > 6a and 1d > 2d > 3d > 4d > 5d > 6d, whereas the J values are in the orders 1b > 2b > 3b < 4b < 5b < 6b and 1c > 2c > 3c < 4c < 5c < 6c. The structural and electronic properties of the coupler associated with the geometric character of a specific diradical influence the effective magnetic coupling between two spin centers. It should be noted that J values calculated for the other four couples of bis(tert-butyl)-nitroxide-based diradicals  at the B3LYP/6-311++G level. It is further proved that redox induction can indeed modulate the magnetic transformation. The magnitudes of J for these bis(tert-butyl)-nitroxide-based diradicals are basically comparable to that of the corresponding nitroxide-based ones except for 2b′. This deviation is due to the poor planarity of 2b′.The redox-induced magnetic reversal in pure organic diradical systems is exceedingly attractive. In this section, we discuss in detail the redox-induced magnetic transformation for twelve couples of NO-based diradicals , the longer coupler lengths of 2b (J = 417.2 cm−1) and 3b (J = 408.0 cm−1) weaken their magnetic couplings gradually. Compared with 3b, the smaller HOMO-LUMO gaps of the couplers for 4b, 5b, and 6b enhance their magnetic interactions successively; their corresponding HOMO-LUMO gaps are 3.13, 2.47, 1.97, and 1.61 eV, respectively , 2a (1.383 Å), and 3a (1.392 Å) increase gradually, and the corresponding magnetic coupling decreases successively. A similar situation occurs with 1d, 2d, and 3d. On the contrary, the averaged linking bond lengths of 3b (1.387 Å), 4b (1.384 Å), 5b (1.381 Å), and 6b (1.379 Å) decrease with the extension of the coupler, and the corresponding magnetic coupling increases in sequence. The diradicals of 3c, 4c, 5c, and 6c present a similar case. It is worth mentioning that the shortest linking bond of 1d (1.354 Å) leads to the strongest magnetic interaction (−1267.5 cm−1) among the studied diradicals. The linking bond lengths between the coupler and two spin centers of all NO-based diradicals shown in 2a′, 2b′, 3a′, 3b′, 2c′, 2d′, 3c′, and 3d′ are also displayed in 3a ↔ 3b and 3c ↔ 3d.The geometric optimizations in eraction ,16. WithJ| value. As shown in 1d, 2d, 3d, 4d, 5d, and 6d, and the corresponding magnetic interaction successively weakens. Specifically, 37.0%, 23.7%, 17.8%, 16.4%, 15.3%, and 15.1% of spin densities are delocalized to the coupler for 1d, 2d, 3d, 4d, 5d, and 6d, corresponding to −1267.5, −589.4, −302.5, −184.4, −98.5, and −66.4 cm−1 in the respective order. By contrast, the average spin density delocalization into the coupler increases gradually with an extension of the coupler for the ferromagnetic diradicals 3b, 4b, 5b, and 6b, and the corresponding magnetic interaction is enhanced successively. In detail, the spin polarizations toward the coupler are 17.6%, 19.9%, 21.6%, and 22.5% for 3b, 4b, 5b, and 6b, respectively, which correspond to 408.0, 464.5, 576.6, and 740.7 cm−1 in sequence  is likely to result in a larger S-T energy gap (ΔEST), and there is a linear correlation between the two.Furthermore, aside from the spin alternation rule, the SOMO effect and the SOMO-SOMO energy level splitting are also taken into account to predict the magnetic properties of diradicals. Borden and Davidson  explainewo SOMOs . It seemSS) is less than 1.5 eV, the two nonbonding electrons would prefer to occupy different degenerate orbitals with a parallel-spin configuration to minimize their electrostatic repulsion, thus resulting in a triplet ground state. Subsequently, Constantinides and coworkers [SS > 1.3 eV are clear singlets via the investigation of a series of 4nπ antiaromatic linear and angular polyheteroacenes. In addition, Zhang et al. [SS are different for different m-phenylene-bridged diradicals. In this work, the SOMO-SOMO energy gaps of 1a, 2a, 3a, 4a, 5a, and 6a are relatively small (0.13–0.52 eV) but give rise to antiferromagnetic interaction. The deviation is due to the existence of two carbonyl groups in these diradicals, which inhibits the spin polarization, resulting in a strong repulsion of electrons and thus a small ΔESS. As shown in SS and ΔEST for 1a, 2a, 3a, 4a, 5a, and 6a. After dihydrogenation, the diradicals 1b, 2b, 3b, 4b, 5b, and 6b, with quite small ΔESS values, are known to present ferromagnetic couplings naturally, corresponding to 0.04, 0.33, 0.43, 0.49, 0.54, and 0.54 eV, respectively. With regard to 1c, 2c, 3c, 4c, 5c, and 6c, their ΔESS values are also very small (0.02–0.37 eV), thus resulting in ferromagnetic interaction. After dihydrogenation, the SOMO-SOMO energy gaps of 1d, 2d, 3d, 4d, 5d, and 6d are comparatively large (0.46–1.09 eV), leading to antiferromagnetic couplings. In particular, 1d, 2d, and 3d present the singlet ground states with ΔESS values of 1.09, 0.96, and 0.89 eV, respectively, which are in close proximity to 1.3 eV. It is worth mentioning that there is a good linear relationship between ΔESS and ΔEST for 1c, 2c, 3c, 4c, 5c, 6c, 1d, 2d, 3d, 4d, 5d, and 6d, as shown in SS tends to have a large positive ΔEST, while a diradical with a large ΔESS has a small or negative ΔEST. We tabulate the energy levels of two SOMOs of the triplet state for the studied diradicals in Hoffmann  proposedoworkers  suggesteg et al.  indicateJ values for the studied diradicals. An expression of the magnetic exchange coupling constant is written as J = (EBS − ET)/(<S2>T − <S2>BS), where EBS and ET refer to the energies of the BS and T states, respectively and <S2>BS and <S2>T denote the average spin square values of these spin states, respectively. This expression was developed by Yamaguchi and coworkers [J values. When J is positive, the high-spin or ferromagnetic coupling state is the ground state; when it is negative, the low-spin or antiferromagnetic coupling state is the ground state. Ginsberg [EST = <S2>T J, using the abovementioned J, with ΔEST = ES − ET. All of these DFT calculations were performed using the Gaussian 09 suite of the program [In general, both the coupler and radical groups are essential for a diradical. In this context, we theoretically designed and investigated twelve organic molecular switches, each of which consists of two nitroxide groups and a redox-property-tunable coupler. The molecular geometric optimizations, frequency analyses, and energy calculations of the closed-shell (CS) singlet, broken-symmetry (BS) open-shell singlet, and triplet (T) state of all the diradicals were carried out at the (U)B3LYP/6-311++G level of theory. All optimized geometries were confirmed to be minimum on their potential energy surfaces, showing no imaginary frequency. For the BS state calculations, to generate the appropriate BS wave functions, we used “guess = mix” keywords. The use of “guess = mix” keywords allows the HOMO and the LUMO to be mixed, and this removes the α,β spatial symmetries and generates a new initial guess . Additiooworkers ,47 and cGinsberg  develope program .1a ↔ 1b, 2a ↔ 2b, 3a ↔ 3b, 4a ↔ 4b, 5a ↔ 5b, 6a ↔ 6b, 1c ↔ 1d, 2c ↔ 2d, 3c ↔ 3d, 4c ↔ 4d, 5c ↔ 5d, and 6c ↔ 6d). The computational results reveal that the antiferromagnetic interactions of 1a, 2a, 3a, 4a, 5a, and 6a decrease with a noticeable elongation of the coupler. In particular, the presence of two carbonyl groups in their couplers hampers spin polarization and weakens the magnetic coupling between two spin centers greatly, resulting in small |J| values. Their magneto-structural correlations are bound to superexchange pathways. After dihydrogenation, the ferromagnetic couplings of 1b, 2b, 3b, 4b, 5b, and 6b decrease first and then increase with the extension of the coupler. The shorter coupler lengths and linking bond lengths of 1b and 2b and the smaller HOMO-LUMO gap of the coupler, as well as the larger spin polarization for 3b, 4b, 5b, and 6b, are critical in enhancing their corresponding magnetic couplings successively. The magnetic conversion of these six couples of diradicals before and after dihydrogenation is attributed to the different spin coupling channels. Before dihydrogenation, the spin transmission through the coupler is blocked due to the presence of two carbonyl groups, but, after dihydrogenation, the spin transport follows the spin alternation rule, resulting in magnetic switching.In conclusion, we theoretically designed twelve couples of NO-based diradicals, each of which can undergo the magnetic transformation between ferromagnetism and antiferromagnetism via redox induction (1c, 2c, 3c, 4c, 5c, and 6c decrease first and then increase as the length of the coupler extends. The shorter coupler lengths and linking bond lengths of 1c and 2c and the smaller HOMO-LUMO gap of the coupler, as well as the larger spin polarization for 3c, 4c, 5c, and 6c, are crucial in promoting their corresponding magnetic couplings progressively. After dihydrogenation, the antiferromagnetic couplings of 1d, 2d, 3d, 4d, 5d, and 6d gradually weaken as the length of the coupler increases. The spin polarization plays a key role in controlling their magnetic couplings, and their corresponding average spin density delocalization into the coupler decreases in sequence. The magnetic transformation of these six couples of diradicals occurs due to the number of bonds in the spin-interacting pathway through the coupler changing from even to odd after dihydrogenation.Similarly, the ferromagnetic interactions of 1b, 2b, 3b, 4b, 5b, 6b, 1c, 2c, 3c, 4c, 5c, and 6c could store magnetic information as an “ON” state at the high-spin ground state, while 1a, 2a, 3a, 4a, 5a, 6a, 1d, 2d, 3d, 4d, 5d and 6d, could store information as an “OFF” state in the low-spin ground state. We believe that this work would open a new prospect for the rational design of such redox-regulated diradicals and find further technical applications in magnetic molecular switches. Of course, we still need to make great attempts to explore the magnetic conversion mechanisms through the redox process in a real system.The ground states of the twelve couples of diradicals can be explained on the basis of the spin alternation rule, the SOMO effect, and the SOMO-SOMO energy gaps of the triplet states. In the spin-trapped solution matrix,


Neuromodulation technology has provided novel therapeutic approaches for diseases caused by neural circuit dysfunction. Transcranial focused ultrasound (FU) is an emerging neuromodulation approach that combines noninvasiveness with relatively sharp focus, even in deep brain regions. It has numerous advantages such as high precision and good safety in neuromodulation, allowing for modulation of both peripheral and central nervous systems. To ensure accurate treatment targeting in FU neuromodulation, a magnetic resonance acoustic radiation force imaging (MR-ARFI) sequence is crucial for the visualization of the focal point. Currently, the commonly used 2D Spin Echo ARFI (2D SE-ARFI) sequence suffers from the long acquisition time, while the echo planar imaging ARFI (EPI-ARFI) sequence with a shorter acquisition time is vulnerable to the magnetic field inhomogeneities. To address these problems, we proposed a spatiotemporal-encoded acoustic radiation force imaging sequence  in this study. The displacement at the focal spot obtained was highly consistent with that of the SE-ARFI sequence. Our research shows that SPEN-ARFI allows for rapid image acquisition and has less image distortions even under great field inhomogeneities. Therefore, a SPEN-ARFI sequence is a practical alternative for the treatment planning in ultrasound neuromodulation. In SPEN-MRI, the traditional RF pulse used to excite spins is replaced by a linear chirp pulse, and a spatiotemporal encoding gradient is applied in the phase encoding direction. As a result, the phase profile of the spins has a quadratic shape, which increases the bandwidth of acquisition gradient in the phase encoding direction. Only the phase stable point and its adjacent area contribute to the SPEN-MRI signal, which reduces chemical shift artifacts and distortions caused by inhomogeneous magnetic fields, as compared to traditional SE-EPI or GRE-EPI images. Similar to SE-EPI, SPEN-MRI requires continuous application of fast-switching magnetic field gradients, but SPEN-MRI uses a special chirp pulse , thenThe linear scanning pulse in SPEN-MRI produces a parabolic phase profile. Unlike RF pulses traditionally used in MRI sequences with a fixed central frequency, the central frequency of the chirp pulse used in SPEN-MRI sequences changes linearly with time, producing quadratic phase modulation  can then be expressed aswhere R = ΔO/T represents the rate of change of the frequency of the chirp pulse. The phase that is accumulated during the excitation and the magnetic field generated by the chirp pulse can be expressed asy  between the opening time of FU and the application of DEG was investigated to maximize the displacement encoding. The averaged displacement Δx corresponding to the DEG can be obtained according to the following equationThe incorporation of a Displacement Encoding Gradient (DEG) is crucial for SPEN-ARFI imaging , Shanghai, China] with a 32-channel RF head coil. The FU transducer used to induce local displacements was an 800 kHz single-element design  with a focal depth of 5 cm. The instantaneous input electrical power used to generate the ARF was set to 170 Watts. For both agar phantom and ex vivo porcine brain, the ultrasound transducer was fixed above the object to be scanned, the duration of the FU ARF pulse was 21.76 ms, and images were acquired with the FU system turned on and off.All of the experiments were conducted in a whole-body 3.0T MRI system [ex vivo porcine brain images was acquired in the coronal orientation to determine the location of the target, followed by acquiring images through the target in the axial orientation. Since imaging times were short (125 ms per slice), phase changes that may be potentially produced by temperature changes were insignificant. The resistance of SPEN-ARFI and EPI-ARFI sequences to inhomogeneous magnetic fields was then assessed using the same parameters, and the image distortions were visually evaluated. Finally, the quantitative comparison experiment of SPEN-ARFI and SE-ARFI sequence was conducted using the ex vivo porcine brain, the same slice was scanned, and the FU parameters were kept consistent throughout the experiment. The maximum displacement was determined by calculating the average phase difference of nine pixels at the focal point, and the resulting average values obtained by the SE-ARFI sequence and SPEN-ARFI sequence were compared.First, we aimed to evaluate the imaging quality of the SPEN sequence in comparison to commonly used sequences by scanning the same slice of a water phantom with a FU transducer, the parameters used in this experiment are detailed in ex vivo porcine brain imaging were kept consistent, TE1 55 ms, TE2 125 ms, TE ranging from 55 to 125 ms, chirp pulse duration Te 20 ms and bandwidth 30.7 kHz, Ge 3 mT/m, DEG 50 mT/m, DEG duration 6 ms, slice number 8, and spacing between slices 7.5 mm. The SPEN-ARFI, EPI-ARFI, and SE-ARFI in this study used these common parameters: field of view (FOV) 200 × 200 mm, flip angle 90°, slice thickness 5 mm, TR 1,000 ms, and acquisition matrix 64 × 64. The TE of SE-ARFI was 50 ms, and the TE of EPI-ARFI was 100 ms in the comparison experiments.The SPEN-ARFI sequence parameters used in agar phantom and a) in the y direction acquired at time ta  Core (TM) i7-10875H CPU @ 2.30 GHz and a Nvidia GeForce RTX 2060 4GB. Images were reconstructed offline from the K-space data sampled in Cartesian coordinates by using programs coded in MATLAB software. To obtain the baseline phase information, we calculated and compensated for the quadratic phase according to the corresponding chirp pulse parameters during image reconstruction. The signal S(tThe position-related quadratic term can be removed by the following equationa) is the signal acquired at time ta after removing the quadratic term that has the largest influence on the visualization of the focal point, ρ0(y) is the spin density at position y , it can be seen that a large bandwidth was chosen in this study, which allowed the SPEN-ARFI sequence to exhibit robustness against magnetic field inhomogeneities and obtain images with high spatial resolution albeit at the cost of reduced SNR in the images. Conversely, selecting a smaller bandwidth would diminish the benefits of SPEN-ARFI but improves the imaging SNR. Nonetheless, SPEN-ARFI has several limitations. Eddy currents can result in stripe artifacts on the phase map, as shown in The present study presents a novel technique for acoustic radiation force imaging called the SPEN-ARFI sequence. Compared to conventional acoustic radiation force imaging methods, SPEN-ARFI offers superior imaging speed and enhanced tolerance toward magnetic field inhomogeneity. Overall, the SPEN-ARFI sequence represents a promising development in the field of acoustic radiation force imaging.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.in-vitro porcine brain, commercially available at supermarkets) and so this did not need to be approved by the IRB and IACUC.Ethical review and approval was not required for the animal study because this study does not involve investigations in human subjects or living animals (we just use an JZ, HX, QH, and QG contributed to conception and design of the study. DK and XQ organized the database. XQ and YD did sequence tests. XQ wrote the first draft of the manuscript. JS, XH, and NR wrote some manuscripts. All authors participated in the revision, reading, and approval of the submitted version of the manuscript.


Components of the climate system, however, can show stepwise shifts from one regime to another, as a nonlinear response of the system to a changing forcing2. Here we show that the Arctic sea ice regime shifted in 2007 from thicker and deformed to thinner and more uniform ice cover. Continuous sea ice monitoring in the Fram Strait over the last three decades revealed the shift. After the shift, the fraction of thick and deformed ice dropped by half and has not recovered to date. The timing of the shift was preceded by a two-step reduction in residence time of sea ice in the Arctic Basin, initiated first in 2005 and followed by 2007. We demonstrate that a simple model describing the stochastic process of dynamic sea ice thickening explains the observed ice thickness changes as a result of the reduced residence time. Our study highlights the long-lasting impact of climate change on the Arctic sea ice through reduced residence time and its connection to the coupled ocean–sea ice processes in the adjacent marginal seas and shelves of the Arctic Ocean.Manifestations of climate change are often shown as gradual changes in physical or biogeochemical properties A simple model describes the stochastic process of dynamic sea ice thickening, shows how reduced residence time affects changes in ice thickness and highlights the enduring impact of climate change on the Arctic Ocean. Such a retreat in summer sea ice results in an increase of open water areas and prolongation of ice-free conditions in the adjacent marginal seas and shelves of the Arctic Ocean5. The adjacent, ice-free seas absorb more solar energy during summer and store heat in the upper ocean6. The heat enhances ice melt in the marginal ice zone in summer and delays the onset of new ice formation in the autumn7. These processes influence Arctic-wide sea ice properties through large-scale sea ice motion carrying the ice from the marginal seas to the central Arctic, a process known as Transpolar Drift (TPD) Stream8.The extent of Arctic sea ice exhibited negative trends both in summer and winter over the last three decades9, that is, the Beaufort, Chukchi, East Siberian and Laptev seas. It is then transported across the Arctic to sustain the perennial ice pack  and an increased uniformity of ice thickness distribution . The timing of the shift follows a drop in Arctic-wide sea ice residence time in 2005 and 2007. We introduce a simple model describing the stochastic process of dynamic ice thickening and explain the relationship between the changes in Arctic sea ice residence time and thickness distribution.21. Typically, ice thickness distribution in the Fram Strait is bimodal . Figure The reduction of summer sea ice concentration in areas of sea ice formation, on the other hand, correlates with the reduction in residence time , northward ice drift in the Siberian sector increased by 42% (from 1.2 cm s−1 to 1.7 cm s−1) and the TPD accelerated by 37% (from 2.3 cm s−1 to 3.2 cm s−1), respectively. The acceleration of the TPD shortened the residence time of sea ice in the central Arctic Ocean by about four months on average. Concurrent shorter residence times in the ice formation areas and acceleration of the TPD led to a reduction of the mean residence time of sea ice by 1.6 years (from 4.3 to 2.7 years) after 2007.Acceleration of ice drift speed in the Arctic Ocean24  km). Dynamic forcing, on the other hand, increases the fraction of deformed thick ice forming the tail of the thickness distribution. Divergence, convergence and shear of wind and ocean currents cause mechanical fracturing of ice floes. When the forcing is convergent and once the local internal stress in the ice pack has exceeded the threshold, dynamic ice thickening occurs by ridging and/or rafting29.The relationship between the observed regime shift Figs.  and 2 an31. These characteristics enable us to formulate the process by a proportionate ice thickening of stochastic ice thickness, X, as,i denotes the time index counting dynamic ice thickening events and ai−1 is the proportionate thickening increment due to the event at i−1. The increment represents the stochasticity of the dynamic thickening process. After m-times dynamic growth events, stochastic ice thickness is given by:X0 is the initial ice thickness, that is, sea ice without dynamic thickening, and Π is a product operator. Taking the natural logarithm of equation  also varies in space and differs between events. Another characteristic of dynamic thickening is its dependence on ice thickness. Thicker ice has a larger potential to get thicker as it can exert stronger compressive force on the ice forming ridges and raftingentclass1pt{minimaX0, ln a0, ln a1, …, and ln ai are uncorrelated (the thickening at each dynamic event is a stochastic process), the probability function of ln Xm gives a normal distribution for large m , hence the probability function of Xm gives a log-normal distribution (Gibrat’s law33). The shape of the distribution varies with m, that is, the number of dynamic growth events. The larger the m, the larger the variance. If we assume that the number of dynamic growth events m is proportional to the residence time of ice floes in the Arctic Ocean on annual timescales, a longer residence time leads to larger variance, that is, a larger fraction of deformed ice. As the number of such events has not considerably changed in the last three decades34, m can be linearly related to the residence time of sea ice in the Arctic Basin.As ln Xm for different m (m) gives higher modal peak and less deformed sea ice. The simple proportionate thickening process conceptually explains (1) the shape of the observed ice thickness distribution . This series of events initiated intensive and widespread ice–albedo feedback in the Alaskan and Siberian sectors in the summer43, which resulted in a perennial increase of ocean heat content in areas of ice formation  in this area before entering the TPD  communities and their diversity49. Ridged sea ice supports higher biomass48 and represents safe havens for organisms to hide from predators50. The amount of ridges and deformed ice has also consequences for human activity. Thinner, more level ice is less challenging for ship navigation than in thicker, deformed ice and, along with less ice/shorter ice seasons in general, may allow for an increase in Arctic maritime traffic. Finally, interdisciplinary studies in the Atlantic sector of the Arctic and downstream of the Fram Strait outflow are needed to shed light on the consequences of the described sea ice regime shift and its impacts on physical and biogeochemical processes.Impacts of this regime shift in Arctic sea ice on the pan-Arctic environment are extensive and require further investigation. Thinner and less deformed sea ice causes reduced momentum exchange between ice and ocean, contributing to reduced mixing in the upper ocean underneath areas that are fully covered with ice. This may affect entrainment of heat and nutrients from subsurface to surface ocean with a potential consequence on the biogeochemical cycles involving higher trophic levels. By contrast, however, sea ice retreat in marginal ice zones and continuing weakening of the cold halocline in the Atlantic sector allows for more turbulent mixing and winter convection in the upper ocean54. The raw data were processed to ice draft using procedures described in earlier literature56. The accuracy of each draft measurement ranges from 0.1 m (ice profiling sonars (IPS) deployed after 2006) to 0.2 m (ES300 instruments before 2006), while the uncertainty of each individual measurement is not subject to bias errors and the summary error statistics of monthly values are less than 0.1 m57.Sea ice draft data were obtained from upward looking sonar (ULS) moored in the East Greenland Current in the western Fram Strait. The dataset continuously covers the last three decades (1990–2019) with some short temporal gaps. Four ULSs were zonally aligned at approximately 79° N from 3° W to 6.5° W Fig. . The lat52. We applied the motion vectors from 1984 to 2019 for the backward trajectory calculation. Sea ice concentration data were taken from the Global Sea Ice Concentration Climate Data Records of the EUMETSAT OSI SAF51 (OSI-409 v.1.2 for the period from January 1984 to April 2015 and OSI-430 v.1.2 for the period after April 2015). The ice concentration data were used for the sea ice trajectory calculations and to analyse spatial and temporal changes of ice concentration.The daily mean sea ice motion product provided by the National Snow and Ice Data Center  was applied for backward trajectory calculation of sea ice floes, estimating residence time of sea ice in the Arctic and analysis of spatial average of ice drift speed. The product was derived from a combination of various ice motion estimates from remote sensing and tracks of ice-tethered buoys58) were used to examine temporal variation in sea surface temperature in areas of ice formation  in each cell was defined by an average of all available measurements for every 3-month period . If the number of available data in a cell was less than four, a missing value was assigned to the cell. The difference in summer sea surface temperature  between two periods (1990–2006 and 2007–2019) was used for the analysis  (ERA5). Daily and monthly mean sea-level pressure and 10 m wind were used in the analyses.Sea surface temperatures obtained from the NOAA/NESDIS/NCEI Daily Optimum Interpolation Sea Surface Temperature v.2.1 (ref. O(104) samples were used to derive the distribution functions on a monthly basis ; after 2006, O(106) samples were used . The number of samples used were sufficient to derive thickness distribution on a monthly basis57. The number of data in each bin were divided by the total number of measurements to derive the distribution function. The open water fraction  was excluded when deriving the function. Distribution functions, including the open water fraction, are shown in Extended Data Fig. 62). Although the ratio has some seasonal variability and might have slightly changed due to changes in ice density and snow load in different seasons and years, we assumed that the change was not considerable for the aim of the current analyses. A composite time series of ice thickness distribution . The number of data samples (ice draft measurements) used to derive the distributions varied from time to time. From 1990 to 2005, ion Fig.  was obtaO10 samples 63. Bootstrapping is a family of resampling techniques used to derive uncertainties on various complex estimators for large datasets and uses random sampling with replacement64. The presence of autocorrelation in ice draft series from IPS (ULSs deployed after 2006) suggested using the moving block bootstrap approach63. The method splits the original monthly series of ice drafts O(106) samples of length N into N − K + 1 overlapping blocks of length K each. The block length was set to 30 samples that approximately corresponded to a distance of 10 m covered by ice travelling at 0.3 knots. It roughly corresponded to the lower limit of a horizontal spatial scale of ice ridges. For ES300 instruments (ULSs deployed until 2005) with a lower sampling rate of 240 s, ordinary bootstrapping was used.The uncertainties on the estimates of monthly fractions of ice thicker than a threshold and position of the modal ice peak were assessed numerically using a moving block bootstrap approachM steps of bootstrap sampling, N/K blocks were drawn at random, with replacement, from the constructed set of N − K + 1 blocks, making a new bootstrap data sample for the month. A Gaussian noise of N was further added to the data to account for measurement uncertainty. The mean and s.d. of the fractions of thicker ice and modal ice peak position were then calculated directly from the M estimates derived at each step of the procedure. The results suggested that the monthly coefficient of variation or a ratio of the s.d. of the estimate to its mean, for the IPS data varies from 1% to 3% for both fractions, being on average lower (1–2%) for a fraction of ice greater than 4 m thick. For ES300, the coefficient of variation was slightly higher at about 4(6)% for the fractions of ice thicker than 4(5) m. The same applied to the position of the modal peak, which showed a coefficient of variation of 0–3% being typically closer to 0 for the IPS data. It suggested that the selected bin width was large enough to accommodate uncertainties related to the approach and data. For the ES300 data, the monthly s.d. of the modal peak position was higher, up to 30 cm, and the coefficient of variation was within 9%. Therefore, we postulate that the inferred uncertainties are far too low to have any noticeable influence on the results of the shift detection analysis.At each of σf and μf are the fitting parameters, x is the ice thickness bin and F is the distribution function. To detect the second peak of the distributions that represents multi-year ice travelled across the Arctic Basin, a cut-off threshold was introduced. The threshold was used to exclude thin sea ice fraction, which is supposedly formed in the vicinity of the Fram Strait and is not representing basin-wide changes of ice properties in the Arctic. We defined the threshold by the minimum between the first and second peak of each monthly mean distribution. A set of two consecutive negative gradients (towards thicker bins) followed by two consecutive positive gradients was used to detect the minimum (after applying 3-bin smoothing), while a threshold of 1.53 m (corresponding to 1.3 m of ice draft) was applied when an estimated threshold was thicker than 3 m. Function values ranging lower than the threshold were set to zero (zero case) or excluded from the fitting (NaN case). A least-square minimization was applied to fit a log-normal function to the distribution. In general, the fitted log-normal functions represent the distribution very well. The NaN case slightly underestimates the modal peak, while the zero case captures the peak very well. Examples of distribution functions, together with cut-off thresholds and fitted log-normal functions, are shown in Extended Data Fig. x) = exp (2μf + σf2) (exp (σf2) − 1), for the last three decades are summarized in Fig. σf and μf are summarized in Extended Data Fig. As statistical measures of ice thickness distributions, we examined modal thickness and variance. The monthly mean ice thickness distributions  were fitted to log-normal functions:23 was applied to all time series. The method identifies discontinuities in a time series using a data-driven approach that does not require an a priori assumption on the timing of the regime shifts. The method first identifies potential change points sequentially by checking if the anomaly of the data point is statistically significant from the mean value of the current regime. If it is significant, the following data points are sequentially used to assess the confidence of the shift, using a regime shift index (RSI). RSI represents a cumulative sum of normalized deviations from the hypothetical mean level for the new regime, for which the difference from the mean level of the current regime is statistically significant according to a Student’s t-test. If the RSI is positive for all points sequentially within the specified cut-off length, the null hypothesis of a constant mean is rejected. This led us to conclude that the regime shift might have occurred at that point in time65. If multiple data are available at a certain point in time , the mean value is applied in the time series. Before testing, the temporal gaps of the time series were interpolated by the average of all available data  at the same time and advected backwards in time. The calculations started on the 15th of every month from 1990 to 2019. Daily sea ice motion vectors from the NSIDCv4 were used to update daily position of ice floes backwards in time. Ice motion vectors at the respective floe positions were calculated by interpolation of surrounding points with Gaussian-type weighting . Each trajectory calculation was performed 6 years back in time, while it was terminated if no motion vector was available within a 25-km distance or sea ice concentration at the floe position was lower than 15%. The sea ice concentration at the ice floe position was obtained from OSI-409/OSI-430 with a Gaussian-type weighting . The position of each trajectory termination was used to define the location of ‘initial sea ice formation’. Trajectories shorter than three months were excluded from the analysis because they represent ice floes formed in the vicinity of the Fram Strait.53. We used 83 buoy tracks that arrived in the Fram Strait from 2000 to 2018 and calculated the corresponding pseudo-buoy tracks backwards in time. The comparisons showed that the mean error of the daily pseudo-buoy positions can be reasonably approximated by a linear function of backtracking days19, error = 50 + (backtracking days)/2 km. We applied this empirical formula as an error of the daily position of the backward trajectories from 0 to 500 backtracking days, which corresponds to a 200 (300) km error after 300 (500) backtracking days. Note that this error estimate may underestimate the uncertainty because IABP buoy tracks have been included in the NSIDCv4 ice motion product. However, comparisons between non-IABP buoys and pseudo-buoy tracks derived from the NSIDCv4 with error estimates by a bootstrap method showed that pseudo-tracks are largely parallel to the corresponding buoys and the error does not monotonically increase over time66. The estimated error circles (approximately 300 km) of ice formation location in the present study are sufficiently small compared to the polygons in Fig. Uncertainty of the daily position of the pseudo-floes was assessed by comparisons with ice-tethered buoy tracks obtained from the International Arctic Buoy Program (IABP)The residence time of sea ice in the Arctic basins was defined by the period from the start to the termination date of each trajectory. We calculated an average of residence time of eight pseudo-ice floes that arrived in the Fram Strait at the same time and used it to define the mean residence time of ice floes for each month. The uncertainty of the residence time was defined by the s.d. of the residence time of the eight pseudo-ice floes.X0 and ln a0, ln a1, .., ln ai are uncorrelated; thus, the probability function of Xm for large m is given by:ν and σ2 are the mean and variance of the population distribution of ln am (including X0), respectively67.The log-normal form of the ice thickness distribution can be obtained from a simple proportionate growth process, 31. The third point represents the fact that the thinner part of the pack ice is preferentially ridged/rafted when a dynamic event occurs69. This also takes into account the effect of ice thickness changes on the dynamic thickening process, for example, the thinner ice condition in recent years increases the likelihood of ice deformation.In this study, we provide a concept and description of a stochastic model that formulates sea ice thickening associated with dynamic ice deformation. The model formulates three features of dynamic sea ice thickening by ridging and/or rafting: (1) dynamic ice thickening is a stochastic process ; (2) thicker ice has a larger potential to get thicker than thinner ice at a dynamic event (proportionate ice thickening); and (3) thinner ice has a higher probability of dynamic deformation due to its weaker ice strength . The first point consists of two types of stochasticity in the dynamic ice thickening process. One is areal stochasticity, corresponding to the fact that ice deformation only occurs for a small fraction of the pack ice while the rest of the ice is unchanged when a dynamic event occurs. The other is thickening stochasticity, representing the fact that the thickness gain by ridging/rafting varies in space and differs between events. The second point represents a sea ice characteristic that thicker ice is tolerant and can exert stronger compressive force on the ice forming ridges and/or rafts; hence, more energy is potentially available for the dynamic thickeningX is the stochastic ice thickness at a certain location, i denotes the time index counting sporadic dynamic events  and ai−1 is the conditional proportionate thickening increment due to the event at i−1. The increment ai−1 is a stochastic variable representing both areal and thickening stochasticity. They are implemented as:b is a proportionate thickening constant, ri is a stochastic thickening increment that represents the thickening stochasticity of i-th dynamic event and αi is the areal probability of dynamic thickening that represents areal stochasticity and gives the probability of ice thickening occurrence. This formula indicates that when a dynamic event occurs, α(%) area of pack ice experiences dynamic thickening (ridging/rafting), while the rest (1 − α(%)) is unchanged . The thickness gain, bri, in the dynamic thickening area, is also a stochastic variable: the possible maximum gain is b while the minimum is 0  (thickening stochasticity).The two first points can be formulated by a proportionate thickening of stochastic ice thickness:b = 0.4. The choice of b = 0.4 implies that sea ice in the ridging/rafting area gains 0.4 m thickness at maximum (0.2 m on average) when the ice is initially 1-m thick, while it gains a 1.2 m thickness at maximum (0.6 m on average) when initially the ice is 3-m thick. The value of the parameter b comes from a recent high-resolution observation (5 × 5 m resolution covering a 9-km2 area) of single ice deformation event north of Svalbard, which describes changes in the sea ice freeboard just before and right after a storm event70. According to this study, the change in sea ice freeboard in a converging area is 0.07 m (from 0.36 m to 0.43 m) on average, corresponding to 0.58 m dynamic thickening of ice by ridging and/or rafting (assuming the freeboard to thickness ratio = 8.35)62. The gain relative to the mean ice thickness is estimated by b = 0.58/1.45 = 0.4 (the mean ice thickness in the survey area = 1.45 m). We applied the value for the proportionate thickening constant, b, as the first approach to develop the model. Although the study captured detailed spatial change in the sea ice freeboard, the estimate of b comes from a one-time event and hence needs further assessments by future observations. It should be noted that b = 0.4 is an areal average estimated from the observations, whereas we applied b as the upper bound of the proportionate thickening. This is because the probability function of the stochastic thickening increment, ri, is not known so far; hence, we assumed a constant probability of the increment between 0 and b, potentially causing excessive thickening near the upper bound. The effect of the choice of b is discussed below.We applied the proportionate thickening constant as α, is included to take the third point into account, that is, thinner ice has more chance to be ridged and/or rafted than thicker ice when a dynamic event occurs. To implement this feature, α is given by a function of ice thickness: the areal probability is inversely proportional to the stochastic ice thickness Xi:The areal probability of dynamic thickening, 70. According to the high-resolution survey of a single dynamic event, thickening occurred in 4% of the survey area with a mean ice thickness of 1.45 m. This formula also needs further evaluation by comparing with future observations that address the relationship between areal probability of deformation and ice thickness.The formula indicates that 1-m thick ice experiences dynamic thickening at 4% areal probability, while 3-m ice experiences dynamic thickening at 2% areal probability. Our first implementation of this formula is based on an observational estimate of areal fraction of dynamic thickeningm, that is, external forcing that could cause mechanical fracturing of sea ice and consequent ridging and/or rafting. We used the number of Arctic cyclones that passes over the ice pack as a first-order indicator of the number of dynamic events. Typically 90–130 cyclones per year occur in the Arctic Ocean 71. A typical size of an Arctic cyclone is approximately 3 × 106 km2 (mean radius of approximately 103 km)71, which covers approximately one third of the ice-covered area of the Arctic Ocean. We therefore assumed that one-third of all cyclones hits the ice pack at a certain location in the Arctic, that is, the ice pack experiences approximately 40 dynamic events per year. This corresponds to approximately 80–240 dynamic deformation events for the typical residence time of sea ice in the Arctic /(ρiceLf). As the model does not include a process that forms new thin ice by lead opening, inclusion of the thermal forcing term without a compensating term makes the modal peak very steep after few years, that is, no ice exists in thickness ranges thinner than the thermal equilibrium thickness. To alleviate such an excessive modal peak generation and to take into account the insulating effect of the snow pack that substantially delays thermodynamic ice growth, we applied a moderate value, c = 0.015, which is about one-third of the value estimated from c = Δtκice (Tf − Ts)/(ρiceLf), where Δt ≅ 9 d (corresponding to 40 dynamic events per year) and annual mean surface air temperature of Ts = 263 K.In addition, examples shown in Fig. X0. This is also a stochastic variable, having a normal distribution for simplicity:μ0 = 1.0 and σ0 = 0.25 are applied in the examples . Figure The initial condition of the ice thickness distribution in Fig. b (the proportionate thickening constant); α ; and m (the number of dynamic events). In this study, we briefly describe the sensitivity of the ice thickness distributions to these parameters. In general, a smaller (larger) thickening constant b decelerates (accelerates) the dynamic thickening process, that is, a smaller b gives a smaller variance and steeper modal peak of thickness distribution if α and m are fixed. However, a large value of b  makes the distribution bimodal because the possible thickness gain at each dynamic event is far from the modal thickness and the ridged/rafted ice tends to generate another peak apart from the mode. Therefore, the possible and realistic range of b should be examined further together with the probability density function of the thickening increment r by high-resolution observations in the future. The areal probability of dynamic thickening, α, also affects the evolution of the dynamic thickening process. A larger α promotes dynamic thickening because a larger fraction of pack ice can be deformed at one event. The thickness dependency of the probability, equation (b and α affect the progress of dynamic thickening in the model, we obtained similar ice thickness distributions with a log-normal form sooner or later, that is, smaller b and α can be compensated by a large m, the number of deformation events, indicating a robustness of the formulation. The resulting shape of the distribution, its temporal evolution (Fig. The current formulation contains three parameters to describe the dynamic ice thickening process: ion Fig.  and its ion Fig.  suggest Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-022-05686-x.Peer Review File


Plasmodium falciparum cysteine-rich protective antigen (PfCyRPA) is an invasion complex protein essential for erythrocyte invasion. In contrast to several previously clinically tested merozoite vaccine candidate antigens, PfCyRPA is not polymorphic, making it a promising candidate antigen for blood stage vaccine development.PfCyRPA adjuvanted either with the glucopyranosyl lipid A (GLA) containing adjuvants GLA-LSQ, GLA-SE, GLA-Alum or with Nanoalum. ELISA and indirect immunofluorescence assays (IFA) were used to analyse elicited IgG titers and the P. falciparum growth inhibitory activity was determined with a standardized in vitro [3H]-hypoxanthine incorporation assay.Mice and rabbits were immunized with vaccine formulations of recombinantly expressed PfCyRPA doses of 5, 25 and 100 µg, but with 100 µg of antigen, only two immunizations were required to reach this titer.In the mouse experiments, the GLA adjuvanted formulations were superior to the Nanoalum formulation with respect to antibody titer development, IFA sero-conversion rates and in vitro parasite growth-inhibitory activity. In rabbits, the highest titers of parasite growth inhibitory antibodies were obtained with the GLA-SE formulation. Comparable mean ELISA IgG endpoint titers were reached in rabbits after three immunizations with GLA-SE adjuvanted PfCyRPA formulated with the human-compatible adjuvant GLA-SE represents an attractive vaccine candidate for early clinical testing in a controlled P. falciparum blood stage challenge trial.The online version contains supplementary material available at 10.1186/s12936-023-04638-8. Plasmodium falciparum transmission -hypoxanthine incorporation assay -hypoxanthine was added to each well. Cultures were incubated for further 24 h before they were harvested onto glass-fiber filters and washed with distilled water. The radioactivity was counted using a Betaplate liquid scintillation counter. The results were recorded as counts per minute per well at each IgG concentration and expressed as percentage of the untreated controls. A four-parameter sigmoidal dose-response curve was fitted to the relationship between log10 values of the antibody concentrations and percentage inhibition, and then used to interpolate IC50 values. Data were processed and analysed using Microsoft Excel 2016 and GraphPad Prism 8. Statistical analysis was conducted by the ordinary one-way ANOVA with Tukey’s test for multiple comparisons using GraphPad Prism 8 software to calculate p values.The parasite growth-inhibitory activity of purified total serum IgG antibodies was determined using a standardized in vitro -hypoxanthine incorporation assay . Purified IgG from the unadjuvanted immunization group showed only low growth-inhibitory activities yielding no IC50 values at the tested IgG concentrations. Statistical comparisons of the mean parasite growth-inhibitory activities at an IgG concentration of 2.5 mg/mL indicated significantly higher activities of the GLA-SE group when compared to the GLA-Alum and unadjuvated groups, but not when compared with the GLA-LSQ immunization group . Blood samples were taken pre-immune and after each immunization and analysed by ELISA against PfCyRPA  can elicit antibodies that inhibit invasion of P. falciparum merozoites [PfCyRPA and the in vitro parasite growth inhibitory activity of the elicited antibodies in a standard GIA were assessed.The only currently approved malaria vaccine RTS,S/AS01 (Mosquirix), which is targeting the sporozoite antigen efficacy . Such a rozoites , 19–24. PfCyRPA shows very limited genetic diversity and immunogenicity. Reasons for this limited immunogenicity are not clear. They may be related to specific features associated with the PfCyRPA protein sequence or to the short period, during which PfCyRPA is exposed in the context of the invasion complex on the surface of the merozoites. However, when delivered together with a suitable adjuvant as recombinant protein to the immune system, PfCyRPA can induce strong parasite growth-inhibitory antibody responses. In mice, two immunizations with a GLA-containing adjuvant were sufficient to induce high antibody titers. To reach comparable titers, three immunizations with a Nanoalum formulation were required. Nevertheless, IgG preparations of the GLA-containing adjuvant groups showed higher GIA activities compared to the Nanoalum group. In mice receiving the antigen without adjuvant, the antibody titer remained low after the second immunization, but increased about tenfold in response to a third immunization, resulting in substantial GIA activity. In contrast, both antibody titer and GIA activity in rabbits receiving the antigen without adjuvant remained low after the third immunization. The significantly highest GIA activity in rabbits was elicited by a formulation containing the GLA-SE adjuvant, which may thus be suitable for the clinical testing of an adjuvanted PfCyRPA formulation. Use of the GLA-SE adjuvant may be more cost-effective than a virosomal formulation [PfCyRPA. However, as sero-conversion in the 5 µg group was slower, these data are suggesting to test antigen doses between 20 and 100 µg in a clinical dose finding study. Immunogenicity data in both mice and rabbits are speaking for a testing of a two-dose regimen. While for the experiments described here, PfCyRPA recombinantly expressed in mammalian (HEK293) cells was used, it has been shown recently, that PfCyRPA produced in High Five insect cells has comparable immunological properties [PfCyRPA and preparations expressed in insect cells are not inferior in eliciting parasite growth inhibitory antibodies [PfCyRPA production process in insect cells thus appears to be a suitable basis for vaccine production [In contrast to the ‘classical’ blood-stage vaccine candidate antigens, mulation . No markoperties . The hostibodies . The scaoduction .PfRH5 is the only component of the invasion complex, which has entered into clinical testing [PfCyRPA with PfRipr or PfRH5 may in particular reduce the probability that parasite immune escape variants emerge. Potency of the immune response against the three proteins depends on dose and delivery system and a combination may result in additive or synergistic effects [PfCyRPA delivered with the human-compatible adjuvant GLA-SE induces high titers of parasite-inhibitory antibodies. This formulation represents an attractive vaccine candidate for early clinical testing in a controlled P. falciparum blood stage challenge trial.So far,  testing –27. Comb effects , 28–31.  effects , 32. TakAdditional file 1: Table S1. Humoral immune responses elicited in rabbits with adjuvanted formulations of recombinant PfCyRPA were compared using a one-way ANOVA with Tukey’s test for multiple comparisons using GraphPad Prism 8 software to calculate p values. ****p < 0.0001, **p < 0.005, *p < 0.05; ns not significant. Figure S1. Comparisons of the mean parasite growth-inhibitory activities of purified total IgG from individual rabbit serum samples at IgG concentration of 2.5 mg/mL for each formulation group. Only significant differences between groups are shown. **p < 0.005, *p < 0.05.


A construction site has specific risks and organizational conditions requiring on-site safety inspections. Paperwork inspections have important limitations that can be overcome by substituting paper records with digital registers and using new information and communication technologies. Although academic literature has provided several tools to carry out on-site safety inspections adopting new technologies, most construction sites are not currently ready to adopt them. This paper covers this need of on-site control by providing an application that uses a simple technology accessible to most construction companies. The main objective and contribution of this paper is to design, develop, and implement a mobile device application (App), named “RisGES”. It is based on the model of risk that grounds the Construction Site Risk Assessment Tool (CONSRAT) and on the related models that connect risk with specific organizational and safety resources. This proposed application is aimed to assess the on-site risk and organizational structure by using new technologies and considering all relevant resources and material safety conditions. The paper includes practical examples of how to use RisGES in real settings. Evidence for the discriminant validity of CONSRAT is also provided. The RisGES tool is at once preventive and predictive since it yields a specific set of criteria for interventions intended to decrease the levels of risks on-site, as well as to detect improvement needs in the site structure and resources for increasing the safety levels.  Most construction safety studies begin by highlighting the high levels of accidents and unsafe conditions , which iA Safety Management System (SMS) is essential to managing construction safety, and it always includes safety performance measurement and evaluation ,5. SafetOnly a few studies deeply explore this topic despite its importance . The effMany innovative technologies are available since current research has reinforced the proposal for using new technology and information systems to improve safety conditions on-site . CurrentAccording to Tanvi-Newaz et al. , these nIt should also be noted that the construction industry consists of mostly small and medium enterprises (SMEs). According to EUROSTAT , the conOwing to the requirements and limitations of the new technologies mentioned by Tanvi-Newaz et al. , their uAny technology and its implementation must be based on clear and specific messages or instructions; otherwise, any investment in on-site inspection will be inefficient because it is likely that the information will never reach the person(s) who must implement the intervention.One of the most important indicators involving safety management issues are safety inspections and observations . In consAcademic research has provided a few tools to assess on-site risk levels, but they do not take into account the on-site organizational conditions affecting risk and neither provide clear guidelines nor a specific set of criteria for interventions ,23. TherTo fill this gap, this article proposes a new procedure that follows the nature of the existing tools in the literature, by adopting a broad multiperspective approach. An App in line with the one suggested by Lin et al.  and ZangSummarizing, the main goal of this study is to provide the details of a new and original technological application that is grounded in previous theoretical and empirical studies. The App developed and reported in this paper is adaptable to the special conditions of each site, and it can capture the sites’ structure and the available on-site resources. Using this information as input, the App facilitates the keys for early intervention by generating a proposal for a set of criteria for action to improve both the organizational resources and the on-site risk conditions. Additionally, regarding the description, explanation, and presentation of the application, this paper also reports examples of how to run the application for three different real construction sites.The paper is structured as follows. The Methods section offers the theoretical basis and the validation process of the App. The Results section reports several practical examples of the application of RisGES, including graphical examples of the App’s output. In the Discussion and Conclusions section, the results and main conclusions are drawn. Finally, in the Appendices are some examples of reports generated by RisGES as a result of on-site inspections.This study aims to develop a safety management system for construction sites through an App with a user-friendly interface, which adopts the new Information and Communication Technologies (ICT)  trends tBased on our previous research, the theoretical App’s core is proposed as a relational structure model composed of on-site risk variables and organizational factors. The App puts into operation in digital format the CONSRAT instrument (Construction Site Risk Assessment Tool) , a tool CONSRAT is an existent instrument that considers each construction site as a unit of analysis where potential risk synergies emerge when individual risk variables coexist on-site . It takes a site risk approach using ten risk variables (RV) to assess main live conditions and ten organizational risk-related variables (OV). Five of the ten risk variables are considered alarm variables . The second report comprises overall information but includes specific guidelines for on-site interventions. As shown in The other set of graphs  required an intervention on the material conditions on-site and on its resources, according to their level of complexity. In addition, in “The risk of fall from height is critical and unacceptable, it must be immediately revised and corrected and controlled permanently. Controlling this risk is crucial for the safety on-site, current levels must be immediately corrected. Here it must be considered whether implement measures to stop operations until the level of this risk has been corrected”.The default model of the report generated by the App can be edited and personalized by adding, for example, mention of a specific protection problem or work development. In a similar way, the report provides specific comments for each of the risk variables in accordance with their observed behavior. Those comments are the basic criteria for determining the needed interventions.Next, the report of the inspection provides some recommendations to be addressed concerning the organizational and resource variables see  based onAs a result of the site report, the main interventions at the level of organizational complexity and resources could be the following:Complexity: The high levels of risk in general and specifically the risk of falling from height and the one related to auxiliary means and machinery can be related to the complexity of the site. It is mainly related with a complexity derived from the planning and design of the work and internal organizational structure. This is materialized with the type of contracting  and affects the location of the workers in the perimeter of slabs without protection. Resources: The high levels of risk detected on-site, in particular the risks of compliance with the safety plan, general conditions, fall from height, and auxiliary means, may be related to the lack of resources on-site. Specifically, the resources that can be most affected are the promoter’s resources (support and own resources for site safety) and the builder’s resources (available resources according to the type of constructor and the adequacy and proficiency of the person in charge of the site), the coordination resources , preventive functions , and adequacy of the safety plan at the site (presence and adequacy of contents). As it can be seen, the graphics and reports focus globally on the interventions at the construction sites and facilities, and the main ways to implement the recommended intervention.The main objective of this research is to design, develop, and implement an application for mobile devices that help practitioners to conduct a solid assessment of the risk at construction sites and identify broad areas where the health and safety responsibilities must address interventions to reduce the level of risk. The application, named RisGES, is built upon the theoretical model by Forteza et al.  that ideAs it has been reviewed, the basis for the theoretical model by Forteza et al.  is to idThe RisGES App shares similar advantages with other computerized systems designed to conduct safety inspections, for example, the Smart Inspect created by Rey et al. . First, One potential and very interesting utility of RisGES for construction sites is that its value is higher when the database of assessments is larger. It is true, that any isolated assessment and its corresponding report has value, it can also serve as a catalyst for corrective actions. However, the larger the number of evaluations of a particular work and the more works stored in the database, the greater the potential of RisGES. This is due to the utilities described in This paper has shown that the use of RisGES along with other digital and computerized technology can improve the efficiency of safety assessment and risk management for construction sites. In this sense, it represents a relevant contribution in the direction pointed out by Rey et al.  since itAnother benefit of RisGES in the field of research is related to the potential of the accumulated data. As the use of RisGES grows and the App gains popularity in the construction industry, a huge database can be built with the cooperation of the sector. This database can be a good first step to developing sound studies of the precursors of accidents in construction sites. The usefulness of such a research line is clear, not only for a company where RisGES is systematically used at all of its sites, but also for researchers comparing the effective H&S management between different construction companies. As Tanvi-Newaz  stated, Despite the benefits of the RisGES App, several improvements need to be undertaken to establish future empirical studies. At a theoretical level, the models of risk assessment must incorporate aspects of safety climate, as it is a factor that undoubtedly can increase risk and can be affected by managerial actions. A more comprehensive model of risk assessment is needed to incorporate safety climate issues; once proposed and validated, the RisGES App must incorporate the evidence found to account for the role of safety culture at construction sites, assess its risk impact, and identify the correct improvement actions at a managerial level. Another action to be taken in the future is to analyze in depth the user’s experience with RisGES and obtain valuable feedback from professionals, which is needed to refine and improve the application as needed.This study shows how the RisGES App has been designed upon theoretical and empirical models that served to assess the risk at construction sites, on one hand, and the relationships of on-site risk factors with organizational factors, on the other hand. RisGES includes all the insights from theoretical and empirical models and, taking advantage of ICT, allows any technician or competent practitioner to undertake an easy evaluation of the risk on-site and provide a clear focus toward critical improvement actions that need to be performed. The RisGES App can be taken as an instrument to bridge the gap between the management of safety (preventive policies) and the management of organizations (endowment strategy). Although the RisGES App is currently under testing and refining, we believe it is a good, easy, and attractive tool to enrich safety management systems. Summarizing, with RisGES, practitioners have: (a) appropriate information to rapidly correct on-site safety problems; (b) a more accurate understanding of the safety conditions on-site; (c) a database of risk on-site and linked organizational factors useful for evidence-based decision-making; and (d) reinforcement of the relationships among different stakeholders related to health and safety management. Therefore, RisGES can be considered as a tool that helps to deploy strategies for effective health and safety management and, as a consequence, helps to reduce the extremely high accident rates in the construction industry.


TWIN software developed for this purpose using genetic algorithms. Several examples of optimization of the contrary-rotating double screw extrusion process parameters, i.e., the extrusion throughput, and minimize the plastic melt temperature and the plastic melting length.A novel computer optimization system for the contrary-rotating double-screw extrusion of plastics was developed in this study. The optimization was based on the process simulation performed with the use of the global contrary-rotating double-screw extrusion software TSEM. The process was optimized using the GASEO Nowadays, computer simulations facilitate the development of polymer processing techniques and enable the prediction of the process course based on process parameters, i.e., material data, geometry data, and operating data. Simulations, however, do not allow researchers to conduct this process in reverse, i.e., to compute the process parameters to obtain the optimal output parameters.Extrusion is the most used mass technique in the polymer processing industry. It is used for manufacturing films, sheets, pipes, and profiles, as well as for specialized operations, e.g., for compounding, reinforcing, pelletizing, etc. Extruders are divided into single-screw and double (or twin)-screw machines. Double-screw machines can be co-rotating or contrary-rotating (or counter-rotating).Underwood  and VerbTherefore, process simulations with statistical support seem to be better. The first optimizations using simulations were performed by Tadmor and Klein , MaddockCovas and Gaspar-Cunha proposed a new strategy for extrusion optimization based on genetic algorithms. They developed procedures for the optimization of single-screw extrusion ,15,16 an-Optimization variables are coded;-Searching for a solution starts from some population, which means the probability of getting stuck at a local extreme is low;-The rules of selection are probabilistic;-Objective functions are used, and derivatives are not necessary.Genetic algorithms are characterized by the following features:So far, there is a lack of optimization procedures for contrary-rotating double-screw extrusion, although computer models of this process have been recently proposed ,32,33.TWIN software developed for this purpose using genetic algorithms. Several examples of the parameter optimization of the contrary-rotating double-screw extrusion process were studied in order to maximize the flow rate, i.e., the extrusion throughput, and minimize the plastic melt temperature and the plastic melting length, which are presented in the following sections.In this paper, a novel computer optimization system for the contrary-rotating double-screw extrusion of plastics was developed. Optimization was based on the process simulations performed with the use of the global contrary-rotating double-screw extrusion software TSEM . The proDouble (or twin)-screw extrusion is divided into co-rotating extrusion (the screws are rotating in the same direction), and contrary-rotating (or counter-rotating) extrusion (the screws are rotating in the opposite direction ).Contrary-rotating double-screw extruders are mainly applied for processing thermally sensitive plastic, e.g., polyvinyl chloride (PVC). Co-rotating double-screw extruders have specialized applications, e.g., for compounding, mixing, filling, and reinforcing plastics. The scheme of contrary-rotating double-screw extrusion is depicted in Contrary-rotating double-screw extruders, compared with single-screw extruders, provide better feeding capability, as they can feed the machine with materials in the form of powder or with materials exhibiting slip properties. In contrary-rotating extruders, the material essentially does not flow from one screw to the other, as in co-rotating extruders. In the case of contrary-rotating machines, there is a co-rotating movement in the inter-screw gap, so high shear stresses are not produced in this area, as in co-rotating machines.Contrary-rotating extruders are usually fed with dosing (metered feeding or starve feeding). The flow of plastics in these machines is completely different from the flow in the single-screw and co-rotating extruders. This flow results from a positive displacement mechanism, which does not appear in other extruders. The degree of positive displacement is dependent on the degree of screw meshing. It takes place most fully in closely intermeshing counter-rotating machines.c, the flight flow Qf, the pressure (tetrahedron) flow Qt, and the side flow Qs. The C-chamber was formed by six surfaces, namely the screw root surface, the inner barrel surface, the side surfaces of the screw flights (twice), and the front surfaces of the screw flights (twice). There were distinguished leakage flows between these surfaces, i.e., the calendering flow Qc between the screw root and the screw flight; the flight flow Qf over the screw flights, through the clearance between the screw flight and the barrel; the back pressure inter-screw flow Qt (the tetrahedron flow) through the tetrahedral clearance between the flight flanks of screws, in the radial direction; and the side flow Qs through the side gap between the flight flanks of screws, in the tangential direction.The material in the contrary-rotating extruder was transported in a C-shaped chamber b, and soSome fundamental books present the state of the art in the modeling of polymer extrusion, e.g., ,37,38,39The fundamentals of contrary-rotating double-screw extrusion were first discussed a hundred years ago ,46,47,48Contrary-rotating extruders are entirely different from single-screw extruders, as well as from co-rotating machines. They were first presented by Kiesskalt  and ScheInvestigations of solid plastic conveying and plastic melting in contrary-rotating machines are very limited ,52. WilcWhite et al. ,57,58,59Lewandowski et al.  proposedThe model we used in the studies presented here was based on our previous experimental investigations, which were discussed in the literature ,62. In tBased on these observations, models were developed for melting in both those regions, i.e., in the calendering gap and in the melt layer. Melting was initiated between the screws by the friction on the pellets, which was caused by the calendering stresses between the screws. The melting action at the barrel was induced by the barrel temperature being higher than the melting point and was propagated by the viscous dissipation heating of the melt film produced.The model we used was experimentally validated for various screw configurations, various operating conditions, and various materials, namely polypropylene (PP), low-density polyethylene (LDPE), and high-density polyethylene (HDPE). This was presented in the literature, e.g., ,33,34,59An example of computations with the use of TSEM software (and experimentation) is shown in Another example of computations with the use of TSEM software (and experimentation) is shown in TWIN software (Genetic Algorithm Screw Extrusion Optimization) has been developed to solve the optimization issue. Simulations with the use of the TSEM (Twin-Screw Extrusion Model) software [TWIN software is characterized by TSEM-specific data exchange between the simulation program and the optimization program.The GASEOsoftware  were theTWIN software, combined with the TSEM simulation software, enables process optimization with any number of optimized parameters and various criteria of optimization criteria. The accuracy of searching for the optimized response surface is determined by the number of divisions of the data range, which results from the length of the series of these numbers in a binary form. In GASEOTWIN software, the length of binary series is adjustable, and its maximum length is equal to 255 characters.The GASEO8 = 0.9996 and occupied a surface equal to 16.62% of the entire surface of this “roulette wheel”. The genotype Ge2 had the lowest objective function F2 = 0.1772, occupying a surface equal to 2.95% of the entire surface of the “roulette wheel”.A “roulette wheel” concept was used for selection with an elitist strategy . In thisThe optimization procedures were determined using GA parameters, i.e., the number of optimized variables, the size of the initial population, the length of chromosomes, the probability of crossover and mutation, and the points of crossover.Optimization was performed for the contrary-rotating double-screw extrusion of polypropylene (PP). The calculations were carried out for a Lesitritz LSM 30/34 modular intermeshing contrary-rotating double-screw extruder. This is the machine intended for compounding. We considered the flow in the screw configurations depicted in 3 (solid) and 0.739 g/cm3 (melt), and a melt flow rate (MFR) of 2.7 g/10 min . The rheological flow properties of PP were determined on the basis of capillary rheometry and modeled using the Klein equation as follows:A0, A1, A11, A12, A2, and A22 are the model parameters .Polypropylene (PP) has a density of 0.904 g/cmQmax (kg/h), minimize the plastic temperature at the die outlet outT (°C), and minimize the plastic melting length plL (mm). Contrary-rotating double-screw extruders are often used for the extrusion of thermally sensitive polymers; thus, minimizing the plastic temperature is important. Moreover, minimizing the temperature means minimizing power consumption. Minimizing the plastic melting length provides a sufficiently large flow space for a good mixing process of the plasticized material.Optimization was carried out to maximize the extrusion output iw = 0.8), whereas in the second group (iw = 0.4).The optimized parameters were the screw rotation, the cylinder temperatures, and the extrusion throughput. These are presented in iF is the global objective function, i_normQ is the normalized flow rate (extrusion throughput), Qw is the weight of flow rate, out i_normT is the normalized plastic temperature at the die outlet, Toutw is the weight of plastic temperature at the die outlet, pl iL_norm is the normalized plastic melting length, Lplw is the weight of plastic melting length, and i is the number of the next value from the dataset.The global objective function is defined asThe optimization results are presented for various weights of the optimization criteria in II = 180 °C, TIII = 180 °C, TIV = 180 °C and 187.5 °C, and Q = 65.63 kg/h and 62.50 kg/h. The optimization results showed that the operating parameters of the process both in the case when the highest weight was assumed for the mass flow rate  and in the case when the highest weight was assumed for the lowest temperature of the material at the outlet of the die (Toutw = 0.8) were also close together. It follows that the selection of either of these sets of operating parameters . The simulation results using the optimal parameters for various weights of optimization criteria are presented in II, TIII, TIV, and Q) are depicted in Process simulations were performed for each set of optimal parameters  as shown on the screen of the GASEOTWIN program is depicted in An example of the optimization results (for criteria weights TWIN software, developed for this purpose using genetic algorithms.A novel computer optimization system for contrary-rotating double-screw extrusion of plastics was developed. Optimization was based on the process simulation performed with the use of the global contrary-rotating double-screw extrusion software TSEM. The process was optimized using the GASEOAn example of using this system was presented to show the possibilities of the developed system. Optimization was carried out in order to maximize the flow rate, i.e., the extrusion throughput, minimize the plastic melting temperature at the die outlet, and minimize the plastic melting length. These parameters are important when using double-screw extruders. Contrary-rotating extruders are often used for the extrusion of thermally sensitive plastics; thus, minimizing the plastic temperature is important. Moreover, minimizing the plastic temperature means minimizing power consumption. Minimizing the plastic melting length provides a sufficiently large flow space for the good mixing of the plasticized material. Additionally, these optimization criteria were used with various weights, which depend on the manufacturer’s requirements and are decided by the manufacturer. In this example, the optimized parameters were the screw rotation, the cylinder temperatures, and the extrusion throughput, that is, the basic operating parameters of the process. Of course, optimization can be also performed for the optimization of the geometry of screws.To our knowledge, this is the first optimization software for contrary-rotating double-screw extrusion that uses process simulations. It enables the optimization of extrusion process parameters using various optimization criteria with various weights.


Antibiotics at sub-inhibitory concentrations are often found in the environment. Here they could impose selective pressure on bacteria, leading to the selection and dissemination of antibiotic resistance, despite being under the inhibitory threshold. The goal of this study was to evaluate the effects of sub-inhibitory concentrations of gentamicin on environmental class 1 integron cassettes in natural river microbial communities. Gentamicin at sub-inhibitory concentrations promoted the integration and selection of gentamicin resistance genes (GmRG) in class 1 integrons after only a one-day exposure. Therefore, sub-inhibitory concentrations of gentamicin induced integron rearrangements, increasing the mobilization potential of gentamicin resistance genes and potentially increasing their dissemination in the environment. This study demonstrates the effects of antibiotics at sub-inhibitory concentrations in the environment and supports concerns about antibiotics as emerging pollutants. Even treatment of wastewater cannot eliminate antibiotics, and residual concentrations are released into the environment4. Consequently, antibiotics can be found in environmental settings, although usually at relatively low concentrations5. These concentrations are often considered to be sub-inhibitory, being too low to induce a significant inhibition of bacterial growth6. However, sub-inhibitory concentrations of antibiotics can trigger the SOS response7, select for antibiotic resistance9 and stimulate horizontal gene transfer12 in vitro. Therefore, sub-inhibitory concentrations of antibiotics might induce the selection and dissemination of antibiotic resistance in the environment14 and promote the dissemination of environmental resistance genes to human pathogens15.The use of antibiotics to treat bacterial infections in humans and animals generates a flow of antibiotic residues into the environment20. However, the members of a complex community differ in their response to antibiotics, with some members being inhibited and some members benefiting from the same antibiotic concentration21. Therefore, there is a rising concern that environmentally relevant antibiotic concentrations  can select for antibiotic resistance in the environment21. In addition, they could increase the dissemination of ARGs in the environment and to human bacteria. Since these putative sub-inhibitory concentrations kill fewer members of the bacterial community than inhibitory concentrations and it has been suggested that antibiotics at sub-inhibitory concentrations could have a signaling role in complex communities22, these low concentrations might create a risk for human health by triggering responses in environmental bacteria that lead to efficient dissemination of antibiotic resistance genes (ARGs) to a wider host range than inhibitory concentrations.The term “sub-inhibitory”, which was originally described for pure cultures, is often applied to complex communities24 and they are often used as proxies for anthropogenic pollution25. They can reversibly integrate antibiotic resistance genes in their cassette arrays via the class 1 integrase, IntI126. The gene cassettes integrated in class 1 integron arrays can subsequently be expressed from a promoter, Pc, located at the start of the array27. Therefore, gene cassettes located closer to the promoter benefit from a higher expression. In addition, class 1 integrase activity is regulated by the SOS response29 . Antibiotics that induce the SOS response at sub-inhibitory concentrations induced integrase expression in E. coli30. Therefore, the exposure of environmental bacteria to sub-inhibitory concentrations of antibiotics might induce structural changes in class 1 integrons and have an impact on the mobilization potential of antibiotic resistance genes. The goal of this study was to determine whether gentamicin at sub-inhibitory concentrations (as determined by growth inhibition in vitro and confirmed by a 16S rRNA gene qPCR in microcosms)21 affects the structure of class 1 integrons. We hypothesized that gentamicin at sub-inhibitory concentrations could: (a) induce the integration of GmRG in class 1 integrons by increasing the activity of the class 1 integrase, (b) select for class 1 integron cassettes that already contained GmRG at the beginning of the experiment and/or (c) induce a rearrangement of GmRG in class 1 integron cassettes to positions closer to the promoter so that they are more readily transcribed. To the best of our knowledge, this is the first study demonstrating that sub-inhibitory concentrations of antibiotics can induce changes in class 1 integrons in the environment, thus increasing the potential for mobilization and subsequent dissemination of ARGs.Class 1 integrons are key genetic elements in the dissemination of antibiotic resistance21. Briefly, 1L Rhône river water microcosms were exposed to sub-inhibitory (10 and 50 ng/ml) and inhibitory (800 ng/ml) concentrations of gentamicin for two days, as well as from non-polluted controls. Triplicates were made for each antibiotic concentration. DNA was extracted after 0, 1 and 2 days. In this study, the class 1 integrase gene (intI1) was amplified using HS463a (5′-CTGGATTTCGATCACGGCACG-3′) and HS464 (5′-ACATGCGTGTAAAT-CATCGTCG-3′) primers31. Quantitative PCR assays were done using the Corbett Rotor-Gene 6000  in a 20 µl volume containing GoTaq PCR Master Mix (Promega), 0.75 µM of each primer and 2 µl of DNA. Two non-template controls were also included in the assay. The standard sample was obtained from water DNA and cloned and transformed using the TOPO TA cloning Kit (Thermo Fisher Scientific). The standard was normalized to 108 copies/µl and standard curves were made in triplicate using tenfold serial dilutions (107–102 per µl of qPCR reaction). Amplification conditions were 95 °C for 2 min followed by 35 cycles of 95 °C for 15 s, 60 °C for 30 s and 72 °C for 30 s. Melting curves were generated by increasing the temperature from 60 to 95 °C. Primer efficiency was 1.02 and the linearity R2 coefficient was 0.98. The number of copies of intI1 were normalized by the number of copies of the 16S rRNA gene21 to determine the relative abundance of the class 1 integrase gene in water microcosms  were pooled prior sequencing to ensure sufficient input. Libraries were prepared from the 12 resulting samples using the Ligation Sequencing Kit SQK-LSK109 and the Native Barcoding Expansion 1–12 (Oxford Nanopore) according to the “Native Barcoding protocol” described by Oxford Nanopore. The normalized and pooled library was sequenced using a FLO-MIN106 (R9.4.1) flow cell. Sequencing depth obtained from each triplicate pool can be found in Table S1 in Supplementary Information. Sequences were basecalled using Guppy basecaller v6.0.1 high accuracy model (Oxford Nanopore). The percentage of GmRG in the reads was determined by blasting the sequences against the CARD database32  [paramters: -m edlib]. Reads that did not contain both primers in the correct arrangement were discarded. The primer-oriented reads were then clustered into amplicon-specific clusters using isONclust v0.0.6.133 [parameters: –ont –fastq | write_fastq –N 1]. Error correction was then performed on each cluster using isONcorrect v.0.0.834 with default parameters, and a single consensus sequence was generated for each cluster using spoa v4.0.735 [parameters: -r 0]. Finally, annotation of gene cassettes and attC recombination sites for each consensus sequence was performed using IntegronFinder v2.0rc636 [parameters: –local-max –gbk –calin-threshold 1].Class 1 integron cassettes had been previously amplified from river water DNA using MRG284 (5’- GTTACGCCGTGGGTCGATG-3’) and MRG285 (5′- CCAGAGCAGCCGTAGAGC-3′) primersPooled triplicates exposed to gentamicin at 50 ng/ml and at 800 ng/ml at day 0 were resequenced to increase sequencing depth. Two sequencing runs were performed (one per each triplicate pool) using the Ligation Sequencing Kit SQK-LSK109 and FLO-MIN106 (R9.4.1) flow cells. Sequencing depth obtained from each triplicate pool can be found in Table Correspondence and material requests should be addressed to Concepcion Sanchez-Cid: concepcion. sanchezcid-torres@ec-lyon.fr.aac(6’)-Ib7) and a quaternary ammonium compound resistance gene, qacG2, was identified in the sequence run after 1 day exposure  but not at day 1  except for 3 nucleotides -Ib7 and the one associated with the qacG2 gene in the cassette detected at day 1 was 58% -Ib7) in two different class 1 integron cassettes was 95.9%  showed 100% identity , a 1239 bp long cassette array containing a GmRG (99.5% of identity to aac(6’)-Ib7 gene (99.6% identity). This cassette with a single GmRG was more abundant in the nanopore reads at inhibitory concentrations with 392 reads at 50 ng/ml and 1151 reads at 800 ng/ml of gentamicin. The second array was 2930 bp long and contained three GmRG (one aac(6’)-Ib7; 100% identity and two aac(6’)-Ib genes; 99.6% and 99.5% identity respectively) and a beta-lactamase gene, OXA-256. This cassette was more abundant at sub-inhibitory concentrations than at inhibitory concentrations with 953 reads at 50 ng/ml and 399 at 800 ng/ml of gentamicin. Neither of the two cassettes were detected at the beginning of the experiment even after day 0 samples were resequenced to increase the sequencing depth ~ 38-fold increase for samples exposed to 50 ng/ml of gentamicin and ~ 23-fold increase for samples exposed to 800 ng/ml  and non-polluted controls, no observable effects were detected in the structure of class 1 integrons.In addition, two cassette arrays carrying GmRG were found in water microcosms exposed to both 50 ng/ml (sub-inhibitory) and 800 ng/ml (inhibitory) of gentamicin after a 2-day exposure  might be too low to have a selective potential on environmental class 1 integrons, the selective threshold is much lower than the inhibitory threshold44 and sub-inhibitory antibiotic pressure could contribute to the evolution of these genetic elements.Even though some variability was observed between triplicates, gentamicin at sub-inhibitory concentrations did not alter overall class 1 integron abundance . Although some structural changes were observed both at sub-inhibitory and inhibitory concentrations, an integration event was detected only at sub-inhibitory concentrations. Thus, these low concentrations of antibiotics could have a greater impact on class 1 integron evolution than inhibitory concentrations. These observations underline the concern that sub-inhibitory concentrations of antibiotics can pose a risk for human health13 and they could arguably be more dangerous than inhibitory concentrations48, since they can exert a selective pressure with less lethal effects on target bacteria. Therefore, future research should be focused on the impacts of antibiotics on antibiotic resistance selection in all the different members of the community, regardless of their effects on growth inhibition. The lack of detection of these cassettes  cannot be overruled. We did not observe any GmRG-containing class 1 integron cassettes at the beginning of the experiment (Table 49. Therefore, this mechanism could also be induced by sub-inhibitory pollution, although we have found no evidence to support this hypothesis here.Although our results only support the hypothesis that gentamicin at sub-inhibitory concentrations induced the integration of GmRG in class 1 integron cassettes, the other two mechanisms that we hypothesized could be induced by sub-inhibitory pressure (i.eE. coli via the SOS response50. As long as an antibiotic is present at a concentration high enough to induce a selective pressure on one member of the bacterial community, the SOS response could be activated in response to the DNA damage induced by antibiotic pollution. This activation could lead to an integration of resistance genes into class 1 integron cassette arrays, regardless of the potential of that antibiotic concentration to induce overall growth inhibition. However, we were not able to test this hypothesis in this study and further research should explore this question.Since the activity of the class 1 integrase, IntI1, is regulated by the SOS response, the increase of integration of GmRG in class 1 integron cassettes observed at both sub-inhibitory and inhibitory concentrations could be SOS-mediated. Previous research suggested that sub-inhibitory concentrations of aminoglycosides induce integrase activity in 52. In this study we have not addressed the dissemination potential of these integron cassettes in the environment and their potential recruitment by human microbiome bacteria. Further studies should evaluate the risks associated to these possibilities, both in the presence and absence of selective sub-inhibitory pressure.In conclusion, this study demonstrated that gentamicin at sub-inhibitory concentrations can have an impact on the structure and evolution of class 1 integrons in the environment and increase the mobilization potential of GmRG. Subsequently, the genes that were recruited into class 1 integrons under sub-inhibitory antibiotic pressure could be further disseminated between environmental bacteria and potentially to human microbiome bacteria, including human pathogensSupplementary Information 1.