The effect of malathion in ice is a poorly researched area, and ice is an important habitat for organisms at the base of the food web. This study presents laboratory-controlled experiments designed to investigate the migration rule of malathion during lake freezing. Concentrations of malathion were determined in samples of melted ice and in under-ice water. The effects of the initial sample concentration, freezing ratio, and freezing temperature on the distribution of malathion in the ice–water system were investigated. The concentration effect and migration capacity of malathion during freezing was characterized by the concentration rate and distribution coefficient. The results showed that the formation of ice led to the concentration of malathion appearing as follows: concentration in under-ice water > concentration in raw water > concentration in ice. This implied that malathion tended to migrate from the ice to the under-ice water during the freezing process. The increase in the initial malathion concentration, freezing ratio, and freezing temperature caused a more pronounced repulsion of the malathion by the ice and increased the migration to the under-ice water. When the solution of malathion with an initial concentration of 50 μg/L was frozen at –9 °C and the freezing ratio reached 60%, the concentration of malathion in the under-ice water was concentrated to 2.34 times the initial concentration. The migration of malathion to under-ice water during freezing may pose a potential threat to under-ice ecology; therefore, the environmental quality and impact of under-ice water in icebound lakes needs to be given more attention.  Water being icebound is an important hydrological feature at high altitudes and latitudes , with moIn recent years, more and more researchers have focused on the environmental behavior of pollutants during lake freezing. It was found that during water freezing, the concentration of contaminants in under-ice water and sediments increases due to the migration of contaminants into under-ice water and sediments ,15. RapiThe environmental behaviors of persistent organic pollutants (POPs) such as the organochlorine drug β-hexachlorocyclohexane , polycycMalathion  is an orIt has been shown that the degradation rate of malathion is significantly reduced under low-temperature and dark conditions ,44; therIn order to simulate the natural icing process of lake water, an open and unidirectional cooling conduction freezing simulation device was designed . The uppThe main instruments were an ultra-performance liquid chromatography-electrospray ionization-triple quadrupole mass spectrometer (UPLC-ESI-MS/MS)  and ACQUITYTM UPLC BEN C8 column (2.1 mm × 50 mm × 1.7 μm). The instruments were used to detect malathion in samples. The experiment took place at the Micropollution Research Laboratory of Yantai University.Main reagents: malathion from AccuStandard, purity ≥ 97.9%; methanol, from J&K, HPLC grade, purity ≥ 99.9%; and acetonitrile, from J&K, HPLC grade, purity ≥ 99.9%.To investigate the migration rule of malathion under different freezing conditions, the three factors initial sample concentration , freezing ratio , and freeze temperature  were set as experimental variables.A malathion solution (pH = 7.0) of 1 L was prepared in the freezing simulator with ultrapure water according to the desired concentration. The malathion samples were placed in the low-temperature test chamber and under the desired freezing conditions. When the set experimental conditions were reached, the ice and the under-ice water were separated. The ice melted under room temperature conditions. Malathion concentrations were separately measured in under-ice water and ice melt water.The pH (pH = 7.0) of our solution did not change during the whole experiment. According to the conservation of mass, there was no degradation of malathion during the whole experiment. Our verification of the conservation of mass is shown in +); multiple reaction monitoring (MRM) mode; the collision gas was argon; the desolvention was nitrogen; the ion source temperature was 120 °C; the desolvention temperature was 350 °C; the desolvention flow rate was 300 L/h; and the cone gas flow rate was 50 L/h. The mass spectrometry parameters are shown in Mass spectrometry operating conditions: electrospray ionization source . To prevent interference from stray light, the equipment was shielded. The malathion ice sample was frozen in a quartz colorimetric container. The side wall and bottom of the container was wrapped with insulating cotton and frozen in the low temperature test chamber. The collected image was converted into a pseudo color standard image with a 16-bit floating point (gray) format through Photoshop (CS6).P was defined as the ratio of the concentration of malathion in the under-ice water and in the raw sample to characterize the effect of the freezing process on the concentration of malathion.wC is the average concentration of malathion in the raw sample and iwC is the concentration of malathion in the under-ice water.The concentration rate K [iC is the average concentration of malathion in the ice.The distribution coefficient K  was defiMalathion solutions with initial sample concentrations of 30 μg/L, 40 μg/L, 50 μg/L, 60 μg/L, and 70 μg/L were prepared and frozen at −9 °C until the freezing ratio was 45% for the separation of ice and water. The concentrations of malathion in the under-ice water and in the ice melt water were separately detected, and the effect of the initial concentration on the migration rule of malathion during the freezing process was analyzed by plotting p values of malathion at the initial sample concentrations of 30 μg/L, 40 μg/L, 50 μg/L, 60 μg/L, and 70 μg/L were 183.0%, 181.0%, 182.0%, 181,0%, and 180.0%, respectively, and the distribution coefficient K values were 0.044, 0.040, 0.032, 0.028, and 0.028, respectively. The results showed that as the concentration increases, the rejection of malathion by the ice increases, and the migration of malathion into the under-ice water was enhanced.As shown in Malathion solutions with an initial sample concentration of 50 μg/L were prepared, and freezing experiments were conducted at −9 °C. Ice and water were separated when the freezing ratio reached 15%, 30%, 45%, and 60%. Concentrations of malathion were detected in under-ice water and ice melt water. The effect of the freezing ratio on the migration rule of the malathion during freezing was analyzed by plotting p values of malathion  were 114.0%, 147.0%, 180.0%, and 234.0%, respectively, and the distribution coefficient K values were 0.059, 0.040, 0.035, and 0.033, respectively. The results showed that the rejection of ice from the malathion gradually increases with increasing freezing ratio, and the migration of the malathion to the water below the ice was enhanced.The concentration of malathion in ice tended to decrease and then increase with the percentage of freezing. The concentration of malathion in the under-ice water was significantly higher than its concentration in the raw sample. The concentration of malathion in the under-ice water showed an increasing trend with increasing freezing ratio. The concentration rate Malathion solutions with an initial sample concentration of 50 μg/L were prepared, and freezing experiments were conducted at −15 °C, −12 °C, −9 °C, −6 °C, and −3 °C, respectively. The ice and water were separated when the freezing ratio reached 45%. Concentrations of malathion were detected in the under-ice water and ice melt water. The effect of the freezing temperature on the migration rule of the malathion during freezing was analyzed by plotting p values of malathion  were 176.0%, 176.0%, 174.0%, 177.0%, and 175.0%, respectively, and the distribution coefficient K were 0.048, 0.034, 0.031, 0.028, and 0.013, respectively. The results showed that the rejection of the malathion by the ice became more pronounced with increasing freezing temperature, while the migration of the malathion to the under-ice water became stronger. As shown in The migration rule of the malathion during the simulated lake freezing indicated that the malathion migrated from the ice to the under-ice water during the freezing at different initial sample concentrations, freezing ratios, and freezing temperatures. Research on atrazine in the freezing process showed that freezing will lead to the increase in its concentration in the under-ice water . The higThe degradation pathways of malathion in water are hydrolysis, photolysis, and biodegradation. The rate of hydrolysis strongly depends on the temperature. The half-life of malathion increases with the decrease in water temperature. The half-life of malathion in water at 5 °C is 101.93 d . The degThe freezing process is the binding of malathion to ice molecules; the binding process releases energy, but the energy released by this process is smaller than that released when malathion binds to water clusters . It is kThe process of malathion "captured" by ice is influenced by both the ice growth rate and the malathion diffusion rate. When the ice growth rate is greater than the diffusion rate of malathion, malathion is not able to diffuse and is "captured" by the ice . The freThrough the observation of the distribution of malathion in ice, it can be found that although most of the malathion is expelled into the under-ice water during the process of freezing, a small part of the malathion is still "captured" in the ice . This diThe gradual thickening of the ice cover follows the continued growth of the ice. The thicker ice cover weakens heat transfer, which in turn leads to slower ice growth, meaning that the rate of ice growth decreases as the freezing ratio increases. Therefore, ice crystals can "capture" more malathion when the volume ratio of ice to water is low. However, as the freezing ratio of ice increases, the concentration of malathion in the under-ice water also gradually increases. This increases the contact opportunities between malathion and the ice molecules, which means that the ice can "capture" more malathion. In this study, the concentration of malathion in ice decreased and then increased as the volume ratio of ice to water increased . This inIn this paper, we simulated the freezing process of natural water by laboratory experiments. The effects of the freezing temperature, initial concentration, and freezing ratio on the migration of malathion during the water freezing process were investigated. The distribution of malathion during the icing process was analyzed. The presence of malathion in the ice was discovered by observing the structure of ice. Experiments showed that the ice growth process was not flat, but rather had a bulge; from the bulge, the ice grew downward. The bulge formed dendritic ice. As more and more dendritic ice grew, the dendrites connected with each other to form ice. However, if the connected dendritic ice was spaced relatively far apart, some malathion was "trapped" in the ice by the growth of dendritic ice during freezing. Nevertheless, its concentration in the ice was significantly lower than that of in the water. The distribution coefficient of malathion increased with the increase in initial sample concentration, freezing ratio, and freezing temperature; that is, the migration ability of malathion to under-ice water increased. When the solution of malathion with an initial concentration of 50 μg/L was frozen at –9 °C and the freezing ratio reached 60%, the concentration of malathion in the under-ice water was concentrated to 2.34 times the initial concentration, which is worthy of our attention. For shallow lakes at high latitudes and high altitudes, the proportion of ice in these lakes are high. The concentration of malathion in under-ice water will substantially increase with the proportion of ice. High levels of malathion pose a greater risk to human health and will adversely affect the already fragile under-ice water ecology during icebound periods. This poses new challenges for the management of water environments during the icebound periods of lakes.


These results further highlight the potential of CHPs as a foodborne hypoglycemic ingredient, providing a theoretical basis for the application of CHPs in the healthy food industry.Hydrolysates of coix seed prolamins (CHPs) have an excellent hypoglycemic effect and can effectively inhibit α-glucosidase, which is the therapeutic target enzyme for type 2 diabetes mellitus. However, its hypoglycemic components and molecular mechanisms remain unclear, and its stability in food processing needs to be explored. In this study, four potential α-glucosidase inhibitory peptides  were identified and screened from CHPs using LC-MS/MS and virtual screening techniques. The results of molecular docking showed that the four peptides mainly inhibited α-glucosidase activity through hydrogen bonding and hydrophobic interactions, with Pro and Leu in the peptides playing important roles. In addition, CHPs can maintain good activity under high temperatures (40~100 °C) and weakly acidic or weakly alkaline conditions (pH 6.0~8.0). The addition of glucose (at 100 °C) and NaCl increased the inhibitory activity of α-glucosidase in CHPs. The addition of metal ions significantly decreased the inhibitory activity of α-glucosidase by CHPs, and their effects varied in magnitude with Cu Diabetes is a chronic metabolic disease with a high incidence worldwide, manifested as abnormal fluctuations in blood sugar, accompanied by limited insulin secretion and insufficient sensitivity. The number of patients is rising year by year . Type IICoix larchryma-Jobi L. var. ma-yuan Stapf) is a kind of gramineous plant that can be used as both food and medicine. It has the pharmacological effect of strengthening the spleen, suppressing dampness, and improving T2DM disease [Coix seed  were purchased from Shanghai Yuanye Biotechnology Co., Ltd. . The citric acid, sodium benzoate, potassium sorbate, lemon yellow, carmine, glucose, sucrose, and xylitol (food grade) were purchased from Henan Wanbang Chemical Technology Co., Ltd. . The alcalase , flavourzym , papain , neutral , trypsin , and compound protease  were purchased form the Beijing Solarbio Technology Co., Ltd. .Coix seeds were purchased from Ganzhou Kangrui Agricultural Products Co., Ltd. . The dithiothreitol (DTT) and other analytical reagents were all purchased from Sigma-Aldrich Chemical Co. . The α-glucosidase and g for 20 min. Finally, equal volume of deionized water was added to the supernatant and precipitated. The precipitate was freeze-dried to obtain the coix seed prolamins.The method of Liu et al.  was refew/v). The coix seed prolamins suspension was treated using a water bath at 90 °C for 30 min, and then an ultrasonic cleaning device was used for ultrasonic treatment at 300 W, 40 kHz, and 25 °C for 30 min. Then, equal amounts of proteases were added to the solution at 8000 U/g, respectively, time of hydrolysis 2 h, with pH and temperature optimal for each protease [g for 20 min to obtain supernatant liquor.In the present study, we selected six commercial proteases for the enzymatic hydrolysis of coix seed prolamins. Firstly, coix seed prolamins were suspended in water , N is the concentration of the NaOH solution (mol/L), η is the total protein content (g/g) in coix seed prolamin samples, m is the mass of the coix seed prolamins sample (g), toth is the number of millimoles of peptide bonds per unit mass of raw barley protein (8.3 mmol/g), and T is the test temperature.According to Feng et al. , DH was c, Ab, As, and An, respectively. For details, see Determination of α-glucosidase inhibition was carried out using the pNPG approach, referring to the method provided by Huang et al. . Four grm/z) is 350–1800; resolution: 60,000; automatic gain control (AGC) target: 3 × 106; maximum injection time: 50 ms; (3) secondary mass spectrometry: resolution: 15,000; automatic gain control (AGC) target: 1 × 105; maximum injection time: 50 ms; isolation window: 1.6 m/z; normalized collision energy: 28. MaxQuant (version 2.1.2.0) software combined with UniProt database was used for mass spectrometry retrieval.The CHPs were first desalted using the C18 StageTip and then vacuum dried. All chromatographic analyses were performed on the nanoliter flow rate Easy nLC 1200 chromatography system . The mobile phase A was 0.1% formic acid solution. The mobile phase B was 0.1% formic acid acetonitrile solution. The peptides were separated from 2% to 100% mobile phase B in 60 min at a flow rate of 300 nl/min. Subsequently, the isolated peptides were analyzed using mass spectrometry with Q-Exactive Plus mass spectrometer . The conditions of mass spectrum are as follows: (1) the detection method is positive ion; (2) primary mass spectrometry: scanning range , net charge (pH 7.0) and hydrophobicity of the peptides were analyzed in PepDraw web-servers, available at http://pepdraw.com accessed on 15 July 2023. The toxicity of peptides was predicted via the ToxinPred website, available at https://webs.iiitd.edu.in/raghava/toxinpred accessed on 15 July 2023.The bioinformatics approaches allow for the screening and analysis of potential bioactive peptides . In thishttps://www.rcsb.org/structure/2QMJ, accessed on 20 July 2023) [The α-glycosidase crystal structure (PDB code: 2 QMJ) was the initial structure model of molecular docking. The crystal structure was obtained from the Protein Data (ly 2023) . Before The thermal and pH stability profile was determined according to Wong et al. , with soLikewise, the pH of the configured 10 mg/mL CPHs solution was adjusted to 2, 4, 6, 7, 8, 10, and 12, followed by incubation at 25 °C for 30 min. The pH of the solution was adjusted to 7 at the end of the incubation, and the α-glucosidase inhibition rate was determined.2, MgCl2, ZnSO4, CuSO4, and Fe2SO3 were added to CHPs solution to maintain their final concentrations at 50, 100, 150, 200, and 250 µg/mL for 2 h at 25 °C. The inhibition rate of α-glucosidase was measured after incubation.Here, we mainly investigated the effects of dietary sugars, NaCl, and metal ions on the α-glucosidase inhibitory activity of CHPs. First, the CHPs solution with a concentration of 10 mg/mL was configured, and then different amounts of food ingredients were added to the CHPs solution. The stability of activity for different edible sugars was determined. Certain amounts of glucose, sucrose, and xylitol were added to the solution of CPHs and brought to a concentration of 2, 4, 6, 8, and 10 g/100 mL. The solutions were incubated at 25 °C for 2 h and at 100 °C for 20 min, respectively. The stability of activity for different edible sugars and NaCl was determined. Certain amounts of NaCl and KCl, CaCln = 3). The statistical analysis was performed using IBM SPSS Statistics version 22.0. Data were analyzed using the ANOVA test, and means of significant differences were separated using Duncan’s test at the 0.05 level of probability.Data are presented as mean ± standard errors  to focus on the molecular mechanism of α-glucosidase inhibition. LFPSNPLA, FPCNPLV, HLPFNPQ, and LLPFYPN bind to α-glucosidase mainly through two forces: hydrogen bonding and hydrophobic interaction. The number of action sites is between 19 and 24 . Studiesp < 0.01). However, compared with 25 °C, it was found that the maintenance rate of α-glucosidase inhibitory activity was 85.95~93.78% in a temperature range of 40~100 °C. In addition, there was no significant change in the α-glucosidase inhibitory activity of CHPs-Alc at increasing temperature (70~100 °C) (p > 0.05). The above results showed that the α-glucosidase inhibitory activity of CHPs-Alc could still maintain a high level under high temperature heating conditions, and had high thermal stability. CHPs-Alc can better adapt to high temperature food processing or storage environments.When CHPs-Alc are incorporated into the food system as a functional component, it is necessary to consider the active stability of CHPs-Alc during food processing or storage. In conventional food processing, the identification of temperature and pH stability curves of active peptides helps to determine their suitability for heat treatment processing steps and food systems with specific acidity ,41. In gp > 0.05). However, the α-glucosidase inhibition rate decreased significantly under acidic (pH < 6) and alkaline (pH > 8) conditions (p < 0.05). Compared with pH 7.0, the inhibitory activity of α-glucosidase decreased between 30.09% and 58.94% at pH < 6 and pH > 8. Under alkaline conditions, cysteine, serine, and threonine in polypeptides are easily destroyed, and racemization may occur, which ultimately leads to the reduction of α-glucosidase inhibitory activity [The change in pH can affect the ionization state of the polypeptide, and strong acids and bases can induce non-specific degradation of the polypeptide, resulting in changes in the structure and biological activity of the polypeptide ,44. As sactivity . Also, aactivity . This alp < 0.05)  C1. This  < 0.05) C2. In ad < 0.05) C2. Accor+ and Cl− will destroy the charge balance of polypeptides in CHPs-Alc, resulting in a change in ionic strength in the solution system, thus enhancing their α-glucosidase inhibitory activity [The activity stability of CHPs-Alc at different concentrations of NaCl is shown in activity . Howeveractivity . Cys is activity . Therefo+, Ca2+, Mg2+, Zn2+, Cu2+, and Fe3+ [+, Ca2+, Mg2+, Zn2+, Cu2+, and Fe3+, the α-glucosidase inhibition rate of CHPs-Alc showed a decreasing trend. The inhibition rate of α-glucosidase of CHPs-Alc decreased with the increase in concentration, and the effect of different metal ions on α-glucosidase was different. Specifically, the degree of influence from large to small was Cu2+ > Zn2+ > Fe3+ > K+ > Mg2+ > Ca2+. Therefore, this also suggests that CHPs-Alc should avoid contact with Cu2+, Zn2+, and Fe3+ in food processing and storage to ensure the stability of the hypoglycemic activity.In the conventional food system, due to the addition of some salt or the use of metal containers, food ingredients inevitably come into contact with metal ions, such as Kand Fe3+ . When thand Fe3+ . Moreoveand Fe3+ . The cha2+ > Zn2+ > Fe3+ > K+ > Mg2+ > Ca2+) (p < 0.05). These results indicate that CHPs can be used as a hypoglycemic component with α-glucosidase inhibitory activity, and they are expected to be used in health food in the future. In the future, we can focus on the hypoglycemic effect of CHPs in vivo, further clarify the hypoglycemic mechanism of CHPs through animal tests, and provide data and theoretical support for the development and application of CHPs.In this study, four previously unreported α-glucosidase inhibitory peptides  were identified and screened from CHPs  via LC-MS/MS identification and virtual screening. The results of molecular docking showed that the peptide mainly binds to the active site of α-glucosidase through hydrogen bond and hydrophobic interaction force, in which Pro and Leu in the peptide played a key role in the formation of the hydrogen bond. In addition, the α-glucosidase inhibitory activity of CHPs remained high under heat treatment and pH 6.0~8.0 conditions. The addition of glucose (100 °C ambient temperature) and NaCl helped to enhance the α-glucosidase inhibitory activity of CHPs. However, the α-glucosidase inhibitory activity of CHPs decreased significantly after the addition of metal ions (from large to small Cu


Current cryopreservationmethods are not optimized for complex cell models, hence they arenot easily banked and not as widely used as 2D models. Here we usesoluble ice nucleating polysaccharides to nucleate extracellular iceand dramatically improve spheroid cryopreservation outcomes. Thisprotects the cells beyond using DMSO alone, and with the major advantagethat the nucleators function extracellularly and hence do not needto permeate the 3D cell models. Critical comparison of suspension,2D and 3D cryopreservation outcomes demonstrated that warm-temperatureice nucleation reduces the formation of  intracellular ice,and in the case of 2/3D models this reduces propagation of ice betweenadjacent cells. This demonstrates that extracellular chemical nucleatorscould revolutionize the banking and deployment of advanced cell models. For mammalian cells,the most common additive is DMSO, which helps dehydrate the cellsand protects them from osmotic damage. Recently, innovative cryoprotectantsthat mitigate damage pathways not addressed by DMSO have emerged,5 such as ice recrystallization inhibitors,8 macromolecular cryoprotectants10 and apoptosis inhibitors.12 While numerous compounds have now been examined for cryoprotectiveeffects many questions regarding the role of chemical additives incryobiology remain open.5Cryopreservationis essentialfor all biomedical and fundamental cell biology and is the key toolto ensure delivery of cell-based therapies and other biologics.13 and in multiwell plates (usedfor handling cells) until −15 °C.15A long-standingchallenge in cryobiology is the control of icenucleation. Aqueous solutions tend to supercool below their equilibriummelting point, especially in smaller volumes. In microlitre droplets,water does not tend to nucleate until below approximately −20°C16 This removal of water from cells reduces the likelihoodof fatal intracellular ice formation (IIF).17 The original studies on controlled rate cryopreservation employedseeding with ice crystals to ensure ice formed extracellularly.17 This method is difficult to implement at scale,and is often unnecessary for cryopreservation of milliliter scalevolumes, in which ice tends to form at warm temperatures even in theabsence of deliberately introduced ice nucelatiors.18 The optimum temperature for ice nucleation is not well establishedand may vary by cell type. For instance, Lauterboeck et al. foundthat a nucleation temperature of −10 °C was optimal formesenchymal stromal cells,19 while it hasbeen shown very recently that nucleation at the melting point wasmost beneficial for cryopreservation of human hepatocyte carcinomacells.18 Nevertheless, it is clear thatdeep supercooling during cryopreservation impairs cells recovery.Extracellular ice formation at warm temperatures enables cellulardehydration during cryopreservation by allowing mass transfer fromthe thermodynamically metastable, supercooled cell interior to thethermodynamically stable extracellularly formed ice.14 This is remarkable, as it shows that stimulatedice nucleation in the extracellular environment leads to protectionof the intracellular environment. Such a design principle is appealingas questions around equilibration time, cellular uptake and toxicitycan be easily mitigated, in contrast to intracellular delivery ofcryoprotectants.22 There are, however, few accessible materials whichcan nucleate ice at warm temperatures. Ice nucleating proteins from Pseudomonus syringae are potent nucleators of potentialcryobiological utlity,23 but have not beenisolated pure, due to the significant (insoluble) transmembrane domains.25 Feldspar can nucleate ice at warm temperatures26 as can silver iodide,27 butthese are not readily soluble in aqueous solutions and require segregationfrom the cells.28 Hence, these materialsare not easy to deploy in cryopreservation. Physical stimuli, suchas electrofreezing29 can induce ice nucleationbut are not practical to deploy and potentially impose additionalstress to the cells undergoing cryopreservation. A unique exampleof a soluble ice nucleator is a polysaccharide (which has not yetbeen fully characterized) present on the surface of some pollen grains.33 Murray et al. demonstrated that pollen washing water (PWW) is easilysterilizable (using filtration) and, as it is soluble, could be supplementedto DMSO-based cryopreservation, leading to significant increases inpost-thaw recovery of adherent cell monolayers at a range of freezingrates.15Cryopreservation of cells in smaller volumes of liquids often proveschallenging, however. Daily et al. demonstrated that induced ice nucleationin the extracellular space can increase post-thaw recovery of adherentprimary cell cultures in 96-well plates from 30 to 58%.35 For example, hepatocyte spheroids predict in vivo toxicologicalresponses more accurately than monolayers and hence can play a rolein reducing animal testing.37 The FDA modernizationact 2.0 has removed the requirement for animal testing in drug discovery,where suitable cell models are available.38 During cryopreservation of 3D cell models, uncontrolled ice nucleationleads to widespread damage due to extensive cell–cell contacts,which enable fatal intracellular ice to propagate.39 Due to this, DMSO cryopreservation of spheroids does notalways give high recovery/viability,41 and hencecomplicates standardization and replication.42 Induced ice nucleation has been shown to increase recovery to >80%(with high viability) but protein secretion was reduced compared tofresh.43 There is a clear need to improvemethods for 3D cell storage and distribution.Cellular spheroids (andorganoids) more accurately reproduce thein vivo niche than (2D) cell monolayers.Here we demonstratethat chemically triggered extracellular icenucleation reduces intracellular ice formation when supplemented intoDMSO cryopreservation media. For monolayers and spheroids, which haveextensive cell–cell contacts, large increases in post-thawrecovery were observed, in contrast to suspension cryopreservation.This shows that chemically triggered extracellular ice nucleationusing soluble nucleators mitigates intracellular damage by limitingthe propagation of ice between adjacent cells and is a potent toolto improve the cryopreservation of complex cellular models.46 and hence is a stepping stone to cryopreservation of 3D spheroids(explored below). Suspension cryopreservation was also conducted,which allows segregation of the importance of cell–cell andcell–substrate contacts in monolayers (which can promote icepropagation) versus suspension (no cell–cell contacts). Theactive nucleating agent, from Carpinus betulus (Hornbeam) pollen, was prepared and sterilized as previously reported.15To explore active ice nucleation for complex cellular model cryopreservation,we selected three common adherent cell lines; A549 , SW480 (human colon adenocarcinoma),and HepG2 (human Caucasian hepatocyte carcinoma). 2D monolayer cryopreservationis challenging with few technologies allowing significant cell recoverySupporting Information , demonstratingthat the ice nucleators present in the pollen sample used are similarto those investigated previously, which were shown to raise the nucleationtemperature in wells from −15 °C to −8 °Cin the conditions used for cryopreservation in the present study.15Example ice nucleation droplet freezingdata is included in the 16 Cell recovery was measured both with 10% DMSO alone (−IN)and with induced ice nucleation (cryoprotectant containing 10% DMSOand PWW) (+IN). Previous work has suggested this nucleator increasesthe temperature of ice nucleation in wells which is significant inthe cryobiological context.48 As would be expected, experimentsshow significant biological variability between individual measurements.To account for this variability and establish the statistical significanceof our results we have analyzed our data using a mixed-effects model.49 The cells in suspension showed only small differencesin post-thaw viability with stimulated ice nucleation (+IN), statisticallysignificant only for HepG2 cells (Table S1), but the monolayers all showed significantly increased cell viabilities. This alone is an important observationthat chemically inducing ice to form, in the extracellular media,can have such a major impact on the post-thaw viability of the cells.It also highlights that ice formation is not intrinsically detrimentalin cryopreservation but that it does need to be controlled.Supporting Information methods and results; Table S2).The magnitude of improvement in post-thaw metabolicactivity dueto stimulated ice nucleation varied between the different adherentcell lines. Variation also existed between experimental replicates.Both observations are to be expected in biological systems. This variationwas accounted for during statistical analysis via the applicationof a linear mixed effect models . Overall,these data raise the question: why does controlled icenucleation aid cryopreservation of monolayers but not suspended cells?A key driver for cell death during cryopreservation is fatal intracellularice formation (IIF). Damage to cells during cryopreservation can becaused by a range of mechanisms besides IIF,ularice 2A.48 It n frozen S2. Figur35 which are known to transmit intracellular ice. Both A549 and HepG2spheroids were prepared in U-bottom 96-well plates with either 4000or 8000 cells/spheroid . Spheroidswere cryopreserved in the plates to −80 °C in 10% DMSO,with or without stimulated ice nucleation. The spheroids were thawed,and allowed to recover for 24 h , before evaluating viability by total ATP content,reported relative to the ATP content of cells before freezing. TheATP content assay is a convenient and effective method for assessingcell viability. [Note this method can report >100% viability asthecells are allowed to recover and grow post thaw].With the above data showing that chemically inducedice nucleationimproves the recovery of cell monolayers by reducing fatal IIF andreducing IIF propagation, we proceeded to spheroid (3D) cryopreservation.Spheroid cryopreservation is challenging because traditional cryoprotectantsneed to permeate to the core of the spheroids, without inducing toxicity,in addition to the IIF challenges we have described above and in Tables S3 and S4). This analysis showed that induced nucleationincreased post-thaw viability from 26 to 55% (4000 cells) and 45 to76% (8000 cells) for A549 spheroids. This result suggests that largerA549 spheroids may cope with the cryopreservation process better,even after normalization for prefreeze spheroid size. This findingis somewhat surprising as it would generally be anticipated that largercell constructs would cope with cryopreservation less well. In comparison,in HepG2 cells no significant effect of spheroid size was found, resultingin an increase of cell viability of 50% , independent of spheroidsize (Table S5). For both cell lines, thisis a substantial increase, achieved by changing ice nucleation temperature,rather than the cryopreservation formulation or freezing-rate profile.To provide further evidence of the efficacy of this chemically inducedcryopreservation strategy, confocal microscopy of the thawed spheroidswas undertaken. Using live (green)/dead (red) staining, it can beseen that for cryopreservation of A549 spheroids in 10% DMSO alone,there are many more red-labeled cells (membrane damaged) comparedto when ice nucleation was induced . These observations supportthe hypothesis that IIF propagation between cells (which would damagemembranes) is effectively mitigated by the induced ice nucleation. induced 3B. Simil52 and cryopreservation53 can both cause production of ROS. These data suggest that inducingice nucleation decreases overall stress levels within the cells andthat the mechanical process of ice nucleation can also mitigate biochemicaldamage, contributing to the high levels of recovery achieved here.It is worth noting that some spheroids frozen in the (−IN)condition were physically disrupted, as shown in An additional reactive oxygenspecies (ROS) assay was also conductedon the A549 spheroids (8000 cells/spheroid) 4—ROS is Combined with monolayer and suspension cell data,these measurementssupport the hypothesis that soluble chemical nucleators can significantlyprotect multicellular structures during freezing by modulating theexternal environment to ensure ice forms at warmer (subzero) temperatures.This supports our early evidence that cell–cell and cell–substratecontacts lead to propagation of IIF and that by reducing the totalfatal IIF by inducing warm-temperature ice nucleation, the cryopreservationof 3D constructs is dramatically improved.18 Altogether, this work shows that the design,discovery, and understanding of chemical ice nucleators is essentialto enable banking and distribution of increasingly complex cell-basedmodels. The easy storage of these models will increase their uptakein fundamental and applied studies, including to reduce the need forin vivo  experimentation.In conclusion, wehave demonstrated that soluble polysaccharide-basedice nucleators can reduce intracellular ice formation during DMSO-mediatedcryopreservation. This reduction in intracellular ice allowed theroutine cryopreservation and recovery of intact spheroids. Comparedto DMSO alone, increases of 2–5-fold in cell recovery wereobserved. Triggered extracellular ice nucleation was shown to benefitscenarios with extensive cell–cell contacts (monolayers/spheroids)more than those without (suspension), supporting the hypothesis thatintracellular ice can propagate between adjacent cells, and hencethe need to prevent it. The active ice nucleation approach reducedthe number of membrane damaged cells, and appeared to reduce overallreactive oxygen species, contributing to the exceptional post-thawrecovery results. The solubility and sterile nature of this chemicalnucleator will enable its widespread use in 2D and 3D cryopreservationprotocols, and the simplicity of addition to established cryopreservationsolutions makes this easy to deploy compared to insoluble nucleators.While this study clearly demonstrates the efficacy of PWW as a nucleatorfor cryopreservation of cell-based models, it has recently been shownthat warmer nucleation temperatures than those achieved using PWWwill likely yield further improvements to cryopreservation outcomes,motivating further research into biocompatible ice nucleators.


Competition theory is founded on the premise that individuals benefit from harming their competitors, which helps them secure resources and prevent inhibition by neighbours. When multiple individuals compete, however, competition has complex indirect effects that reverberate through competitive neighbourhoods. The consequences of such ‘diffuse’ competition are poorly understood. For example, competitive effects may dilute as they propagate through a neighbourhood, weakening benefits of neighbour suppression. Another possibility is that competitive effects may rebound on strong competitors, as their inhibitory effects on their neighbours benefit other competitors in the community. Diffuse competition is unintuitive in part because we lack a clear conceptual framework for understanding how individual interactions manifest in communities of multiple competitors. Here, I use mathematical and agent-based models to illustrate that diffuse interactions—as opposed to direct pairwise interactions—are probably the dominant mode of interaction among multiple competitors. Consequently, competitive effects may regularly rebound, incurring fitness costs under certain conditions, especially when kin–kin interactions are common. These models provide a powerful framework for investigating competitive ability and its evolution and produce clear predictions in ecologically realistic scenarios. Yet, natural examples of cooperation (directly benefitting another organism) and even altruism  abound, with major impacts on ecology and evolution –3. To gia). The Hawk-Dove Game is another game that is commonly applied to ecological systems—and it would seem to be relevant where competitive aggression is being explored—but the Prisoner's Dilemma is close mathematically to the models developed here and Hawk-Dove Game is not, so it will not be discussed further.The evolution of cooperation has been a focus of evolutionary game theory. At the heart of game theory is the ‘pay-off matrix’, which describes the outcome of each combination of strategies used by interacting agents. One well-known example is the Prisoner's Dilemma, in which two agents may cooperate or defect (i.e. forgo cooperation and instead adopt an antagonistic strategy). If both cooperate, both benefit . The greatest pay-out comes to an agent who defects while their partner cooperates . Conversely, the least pay-out comes to the cooperator in this condition . When both agents choose to defect, they receive little pay-out , but this is greater than the pay-out given to a sucker a. The HaIn the Prisoner's Dilemma the most reliable strategy is to defect because it has the best pay-off no matter which strategy the partner uses. Defection, in the Prisoner's Dilemma, is described as an ‘evolutionarily stable strategy’ (ESS) because it is an ‘evolutionary attractor’ (it tends to evolve from a given starting point) and is ‘invasion-resistant’ . EvoAt first glance, interference competition would seem to be a poor candidate for the evolution of cooperation because it might not be obvious why any individual should abstain from harming its competitors, especially if there are no direct costs for doing so and there is a guarantee that competitors are also causing harm. This view of competition centres on a phenomenon I term the ‘initiator effect’, which is the benefit to an individual who pre-emptively suppresses competitors. The initiator effect improves resource availability by weakening or killing competitors and ensures that those competitors cannot exert direct interference effects of their own. However, assortativity of kin, caused by processes such as dispersal limitation or socialization, can help cooperation evolve in social dilemmas because harming others is risky if they might be relatives . FurtherDiffuse competition has many facets—as explained below—but it can be conceptually thought of as an ‘averaging’ or ‘blending’ of competitive interactions that occur when multiple individuals compete simultaneously in a large arena. Although we tend to view competition in a pairwise lens, a major—or perhaps dominant—form of competition is diffuse. Diffuse competition (DC) has three aspects : Type I:In this paper I develop a series of models to evaluate the possibility of cooperation, in the form of conflict de-escalation, evolving among competitors in the absence of any other ecological trade-offs. Specifically, I investigate how kin assortativity and diffuse competition affect evolution of competitive de-escalation. In ecological terms, defection might involve suppression of competitors through conflict, allelopathy or resource pre-emption, and cooperation—in the form of de-escalation—might involve abstaining from those activities. Furthermore, I allow agents to influence competitive outcomes through variation in their own competitive tolerance. I test predictions of mathematical models with agent-based simulations. My goals with this study are (i) to investigate how kin assortativity and diffuse competition interact to affect evolution of competitive de-escalation, (ii) to explore under what conditions and how often de-escalation might be favoured, (iii) to evaluate how the density and identity of competitors influence the number and nature of ESSs, and (iv) to evaluate equilibrium systems for invasion by other strategies. Ultimately, I aim to demonstrate whether, when and how often cooperation in the form of conflict de-escalation might be an evolutionarily stable competitive strategy even when there is no direct cost of competitive aggression and no other mitigating ecological factors or trade-offs.A note on terminology: throughout this manuscript I am defining competition as any negative interaction between two individuals, but for simplicity it may be best to think in terms of interference competition, meaning direct suppression  of one competitor by another. Resource competition is more complicated because it is an indirect interaction mediated by a resource, which will not be modelled here, although many of the same principles may apply. Also, because competition in these models always has negative effects, cooperation involves conflict de-escalation but never a total loss of competition. As I will show below, even de-escalation is a form of altruism because it comes at the expense of individual survival probability (if not the genotype's inclusive fitness). Fitness is the lifetime reproductive success of an organism, although in the models presented here, variation in fitness is a consequence only of variation in survival, which in turn affects reproductive success.. 2b).Imagine a scenario in which competitors can kill one another. In game theoretic terms, this is an extreme version of the Prisoner's Dilemma in which defectors kill their partners and two defectors kill one another. A defector whose neighbour cooperates enjoys its maximum fitness, W, because it experiences no competition from its killed neighbour. Throughout the manuscript, I will define ‘cooperators’ and ‘defectors’ in game-theoretic terms (referring to whether an agent chooses how much to harm a partner), although this differs slightly from the ecological definition of cooperation, meaning that an organism acts directly for the benefit of another . The payfigure 1b), which makes no allowances for continued interaction among agents. If the game is not repeated, then two cooperators are eternally locked into cooperation and C = 0 for the reward case, in which case there is no benefit to defection! If the game is a repeated game, then mutual cooperators and defectors alike might face competition in the future, in which case C needs to be defined for the defector-versus-cooperator case as well as for the mutual cooperation case. To accommodate this, one can introduce a term ‘B’ which describes the proportionate degree of competition felt by defectors as compared with cooperators. I call this the ‘initiator effect’. The initiator effect describes the indirect advantage enjoyed by individuals who suppress their competitors and experience reduced competition in turn. This is one form of diffuse interaction [Expressing competition as above implies that there is a possibility of further interaction with the same or other agents. This is called a ‘repeated game’, and it creates a problem for the simple competition model b, which eraction ,15. When0 and A1, which are the kin-competition terms for competition felt by cooperators and defectors respectively, in which defectors are competitive escalators with strong suppression of competitors, and cooperators are de-escalators with weak suppression of competitors. If kin assortativity is positive, A1 > 1 > A0, meaning that defectors experience stronger competition than cooperators. This is intuitive; clustered, related defectors will kill one another off, and clustered cooperating relatives will benefit from avoiding interactions with other genotypes which are likely to be stronger suppressors (c). In this more general model, it becomes clear that the evolution of cooperation will depend on the balance between initiator effects and kin-assortativity effects. Cooperation will be favoured when the combined initiator effects and kin-assortativity effects acting on defectors exceed the kin-assortativity effects acting on cooperators,Initiator effects would seem to prohibit evolutionary de-escalation, by giving suppressors an advantage over de-escalators under all conditions. However, initiator effects could be counteracted if individuals have increased odds of encountering—and harming—relatives. Such kin assortativity generally promotes cooperation in social dilemmas because it increases the risk of defectors harming relatives . I reprepressors c. In thi. 3i surviving competition is a function of its own tolerance ability, iP. The reduction in survival due to competition is term ‘C’, discussed above. We follow Atwater et al. [Although an individual may attempt to defect (i.e. by killing a competitor), it is not guaranteed to succeed. It is widely recognized in plant ecology, for example, that competitive ability is the product of two functional traits: the ability to suppress competitors, and the ability to tolerate competition. Thus, the probability of an individual of genotype T^i 0–1; , the aver et al.  in definiP are intrinsic traits of a genotype, but i is influenced by the competitive ability of interaction partners and must be calculated. Let us assume that, for any given individual, n neighbouring or otherwise interacting competitors  multiplied by the total relative population density, ρ , variables DC, per ,16),3.2sk genotypes (because other variables are properties of the system and not unique to any given genotype), acting as a uniform field that equally suppresses all genotypes in the population. This formulation represents Type III DC, in which competitive effects are regularized across a population by statistical averaging [As defined here, veraging . This isveraging . Analogoinfinity , with vaijφ, which generalizes equation (3.1) toi,jφ is an odds ratio comparing odds of genotype i encountering genotype j versus any other genotype. Assuming that dispersal limitation affects the odds ratio of a genotype encountering itself, iiφ, but does not bias odds ratios against any other genotypes, iφo, it follows thatEquation (3.2) assumes that all genotypes experience the same competitive environment, but that may not be the case in nature. For example, kin assortativity, caused by dispersal limitation, clustering of family groups, or kin avoidance, might lead to predictable variation in the competitive environment of kin groups  ,14. I iniiφ increases above 1, iS becomes an increasingly important component of iS and As i harms, kills or weakens a competitor, the more it will reduce the suppression it experiences  reduces to equation (2.1) for β = 1, in which case killed competitors are immediately replaced by equivalent competitors. For simplicity, I have chosen to model β as an independent parameter, but more realistically β might co-vary with α and ρ and could possibly be expressed as a function of those. Equation (3.5) contributes Type II DC to the model [n.Term B, which ranges from 0 to 1, describes how neighbour suppression reciprocally ameliorates the competition experienced by an individual in the future—i.e. the initiator effect . The variable he model ,15, and Kin-assortativity effect: Initiator effect: Mean-field effect: We can conceptualize suppression as a force affecting the survival probability of each individual in the community. The suppression experienced by the whole community could therefore be mapped as a field of force, conceptually similar to a magnetic field. If the force is entirely uniform . Genotypes started at equal densities. To simulate passage of time, I estimated the survival probability iD to iiφ = 0, 1, 10, 100, 1000 or 10 000, with ρ = 1 and α = 1. I also ran SFMs with ‘tuned’ values of β and αρ based on their estimated values in the ABMs . For most models I used a default maximum survival probability P = 0.8. Separately, I tested effects of intra-population variation in P by running models with iP equalling each of 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9 and 0.95. These models were run with the same values of β, ii, ρφ and α as above. These values were chosen to represent a comprehensive range of conditions.I simulated SFMs using genotypes that varied in every possible combination of 4, 13 or 101 values of . 5T) and 13 levels of suppression (S) abilities, each ranging from 0.2 to 0.8. All 169 genotypes were characterized by maximum survival probability P, maximum annual fecundity F, and age A in time steps, which were used to calculate age-dependent effects on T, S and F. Of note, unlike the mathematical SFMs, in the cellular ABMs terms A (kin assortment) and B (initiator effects), and their associated variables α, β, ρ and iiφ were not modelled explicitly. These could be measured, but took on emergent, dynamic values. Thus, ABMs served the dual purpose of (i) testing predictions of the SFMs in a stochastic, spatially explicit simulated community and (ii) showing what portions of the available parameter space simulated populations tended to occupy. ABMs do not simulate motile organisms and SFMs are not tested in that setting here. Details can be found in the electronic supplementary material.Model outcomes can be affected by randomness and spatial arrangement of agents ,25, part. 6iT (equation (3.6)) in the SFMs and ABM sub-models. By contrast, suppression, iS, had much more complex and situational fitness effects, as described below.Tolerance was beneficial under all conditions figures  and 3. T. 6.1S were exactly zero in the ABMs, or extremely close to zero, I ran post hoc models in which S varied, but not T, replicated 1000 times over 500 time steps. With all age effects enabled, suppression increased by approximately 1.1%, in 55.4% of simulations  when T was 0.8, and by 7.4–11.6% in 68.7–87.2% of simulations (p < 0.0001) when T was 0.2. It was difficult to directly measure initiator effects, which varied dynamically, but β ranged roughly from 0.74 to 1.00. Parameters iiφ, β, α and ρ varied depending upon the model conditions and identity of surviving genotypes . Plugging in tuned estimates of β, ρ and α into SFMs essentially matched the outcomes of the ABMs.In SFMs with neutral kin assortativity, meaning that genotype distributions were totally random, and with no initiator effects, meaning that individuals could not protect themselves from competition by killing neighbours, suppression had no effect on fitness  and cooperation was adaptively neutral. At maximum initiator effects, meaning that individuals that suppressed their neighbours experienced a complete, lifelong release from competition, suppression had a slight fitness benefit in the SFMs , weakly favouring defection. In 50 replicate ABMs with random dispersal there was no detectable benefit of neighbour suppression, despite initiator effects . To detePositive kin assortativity. When kin assortativity was positive, e.g. due to dispersal limitation or family aggregation, suppression was generally selected against . However, selective benefits of neighbour suppression were short-lived, as they were tied to kin density . As with the neutral-association models, ABMs reflected the SFMs. Age effects did not alter this basic pattern . figures  and 3. WEach genotype had a threshold density beyond which the fitness effect of suppression changed signs was high . If weak. 6.2When kin assortativity was negative, e.g. due to over-dispersion, suppression conferred a fitness advantage under all conditions, although this advantage was weak relative to the advantages of tolerance. SFMs with no initiator effects and complete over-dispersal represent the best-case scenario for the evolution of neighbour suppression, in which case suppression was nonetheless orders-of-magnitude slower to evolve than tolerance . ABMs behaved similarly to SFMs  was impossible if those genotypes were introduced at low density. However, cooperators could invade if introduced at a density above their critical density, in which case the population would experience a state change and cooperators would come to dominate via positive-feedback mechanisms . Because the critical density was quite low, it is conceivable that stochastic processes in small populations could temporarily elevate cooperators above this density, causing change in the ESS, although this process was not specifically tested here. Conversely, if the population was in a super-threshold state with abundant cooperators, defectors could invade, but new cooperators could not. In this case, invading defectors would increase until reaching their threshold abundance. Seemingly, therefore, serial invasion by multiple suppressors could ‘poison’ the system by steadily weakening cooperators, but this was prevented by competition among defectors . In systems that initiated at a super-threshold state, cooperation dominated a stable mixed-strategy community with defectors stably persisting at low abundance.. 6.4Tolerance always provided a fitness benefit. Fitness effects of neighbour suppression, however, varied in direction and magnitude according to the value of every variable in the model. Cooperation was an ESS whenever there was positive kin assortativity and cooperator densities were high enough or initiator effects were low enough that cooperators were above their threshold densities. Otherwise, defection would be a meta-stable ESS when kin assortativity was positive, a stable ESS when kin assortativity was negative, or a stable ESS when kin assortativity was neutral and initiator effects were present . Thus, throughout most of the parameter space explored in this study, cooperation was an ESS, albeit dependent upon both current and initial conditions.iiφ), number of interactors (n), age effects (α), population density (ρ), neighbour replacement rate (β), suppression ability (iS) and mean population tolerance , and much less in the multiplayer case . The exceptions were systems with very high kin assortativity, in which de-escalation evolved at a rate up to approximately 33% the rate of evolution of increased tolerance. Thus, regardless of whether selection favoured competitive escalation or de-escalation, it tended to act slowly relative to selection favouring increased competitive tolerance.The importance of tolerance versus suppression as determinants of fitness  depended strongly on the number of interaction partners, petition ,30,31, bIn the SDMs used in this study, competition occurred in a two-dimensional community of sessile organisms, at high density (i.e. similar to competition among plants). Kin assortativity was manipulated by varying the mode of dispersal. In the SFMs, competition was not spatially explicit and the models themselves are agnostic to both the mechanism of competition and the ecological process influencing kin assortativity. Thus, while the SDMs most closely represent sessile competitors, the SFMs are generalizable to a wide variety of systems, as governed by a variety of assumptions, including but not limited to: competition results in a loss of survival probability; competitor encounter rates are linearly related to competitor density; a bias towards or against encountering kin affects all kin  equally; there is no kin recognition; competitor replacement has a constant chance of happening; and tolerance of competition is possible and is uncorrelated with suppression ability.. 7cost. Thus, altruism, in the form of competitive de-escalation, may be an evolutionarily stable strategy (ESS) in a wide range of systems, and, crucially, in the complete absence of any costs of competition.Does an individual benefit by harming direct competitors? For decades, we thought that the answer is ‘yes’, because competitor suppression provides greater access to resources and, presumably, a lower likelihood of being competitively inhibited via the ‘initiator effect’ ,32,33. Tφii= 1 and B = 1; electronic supplementary material, figures S1 and S2), competition functioned as a uniform field, affecting all genotypes more or less equally. A useful analogy is an electromagnetic field, which is created by, and also directs the motions of, individual particles within the field. Here, individual competitors contribute to the ‘suppression field’, which applies evenly to all others in the community. These ‘suppression-field’ models suggest that in multiplayer competition diffuse interactions are likely to be far and away the dominant mode of competition. If this is true, we may be thinking of competition in the wrong way.One of the key outcomes of this study is support for the hypothesis that diffuse interactions may dominate multiplayer competition in a wide variety of conditions, dramatically reducing the importance of individual pairwise interactions ,15–17,19not to function this way, and instead cause individual interactions to become important? This is a much simpler, more straightforward way of thinking about multiplayer competition than an upscaled pair-centric paradigm. In these models, two processes disturbed the competition mean field, enabling suppression to evolve as a strategy: (i) kin assortativity and (ii) the initiator effect. Positive kin assortativity—i.e. caused by dispersal limitation or communal behaviour—caused a positive correlation between the suppression caused and the suppression felt by a genotype. Kin assortativity burdened competitors with a risk: by suppressing their neighbours they might inadvertently harm their own relatives. Conversely, weak suppressors benefitted by assorting together; in so doing they protected themselves from the strong competition experienced by other community members. I note that these models incorporate inclusive fitness organically without the need for it to be specified explicitly.It is tempting to view competition as fundamentally a pairwise interaction, and multiplayer competition as an upscaled network of such pairwise interactions. While this is certainly true, using this pair-centric mode of thinking it is difficult to intuit how diffuse interactions manifest or what their role is in evolutionary dynamics of competitor communities. I propose instead that we should take a different perspective and consider using a mean-field, fully diffuse model as our starting point for thinking about the evolution of competitive ability in realistic communities of multiple individuals. In other words, we should begin with the premise that individual interactions do not matter, because they are completely diluted and diffused by Type I and III diffuse effects. The question then becomes, what processes cause a community iS). If stronger suppressors find themselves also experiencing stronger competition, then cooperation will evolve. Where do typical ecological systems fall along this spectrum? Data from the field are needed to answer this question, but the models point towards some intuitable hypotheses . I note that even in the best-case scenario suppression was orders of magnitude slower to evolve than tolerance in models. If the same holds true for ecological systems, then fitness benefits of suppression might be difficult to detect or swamped by other ecological or evolutionary processes.Whether competitive escalation or de-escalation was an ESS depended upon a variety of conditions, such as the strength of age effects, the population density, the mean strength of the suppression field, the mean tolerance ability of neighbours, the size of the competitor neighbourhood , the odds of kin–kin encounter, and the replacement rate of killed competitors. Although these effects are mathematically complex, the ecological interpretations are intuitive . Any conModels suggest that the evolutionary outcome of competition also depends strongly on the relative abundance of suppressor genotypes. This could matter, for example, during species invasions. When a species is at low abundance, suppression is favoured because kin–kin encounters are rare. However, as the species rises in abundance due to competitive exclusion, kin–kin encounters become more common, and the system might eventually change state to one where cooperation is favoured. Thus, our models point to a novel eco-evolutionary hypothesis for the evolution of competitiveness in invasive species: based purely on competition, the suppression-field model predicts that competitive aggression will be favoured in the early stages of invasion but will give way to cooperation once the species has established.Mean-field models are finding expanding use in disciplines concerned with complex systems with large numbers of agents, such as economics, social science, physics and neurobiology . Recent The field models developed here point towards a new way of conceptualizing multiplayer competition as a predominantly diffuse field that is occasionally perturbed by individual-based interactions. From that perspective, a question emerges: what processes cause individual-based interactions to become important? I tested kin assortativity and competitor replacement lag as two candidate processes, but many others certainly exist. For example, my models did not account for kin recognition, quorum sensing or learning, all of which might influence the evolution of cooperation. Furthermore, the direction of selection on cooperation versus defection was complex and contingent on many processes, such as priority effects, age effects and genotype density. Other, unmodelled processes are also likely to be important, such as higher trophic interactions, ecological trade-offs, modes of inheritance, motion of competitors, etc. Much more work is needed both to generalize these findings and to guide the development of ecological experiments to test the hypotheses generated by these models.


Preferential pruning of inhibitory synapses bymicroglia contributes to alteration of the balance between excitatory and inhibitory synapses in the hippocampus intemporal lobe epilepsy by Jianchen Fan et al., https://doi.org/10.1111/cns.14224.The cover image is based on the Original Article


Plants have evolved to synthesize specialized (or secondary) metabolites. In contrast to primary metabolites such as nucleic acids and amino acids, which are directly connected with growth, these specialized metabolites help the plant to interact and survive within their environment . They coVolatile terpenes are often produced in flowers and are major contributors to floral scent . Floral Plant Physiology, Freesia genus, from the Iridaceae family, is widely cultivated as an ornamental cut flower, mainly due to its attractive fragrance. Cultivated varieties belong to Freesia x hybrida, likely a hybrid between Freesia corymbosa and Freesia leichtlinii  and -farnesyl diphosphate. Different substrates led to different sets of products, ranging from 1 to up to 14 different terpenoids (Thus, the authors explored the consequences of allelic variation in the amino acid sequences of TPSs by performing a deeper biochemical analysis. Heterologous expression of TPSs in rpenoids .Freesia species had completely lost the flower scent. In other plant genera, such as Capsella and Petunia, some wild species have acquired loss-of-function mutations in genes from the benzenoid biosynthetic pathway, resulting in odorless flowers (Interestingly, none of the investigated wild  flowers . These eFreesia species in 3 TPSs affected their enzymatic functionality, altering both the product composition and quantity. The authors used site-directed mutagenesis in combination with metabolic profiling to pinpoint the causative amino acids that impact product specificity of the terpenoid synthases. Notably, changing a single amino acid was sometimes enough to alter volatile emission, which demonstrates the fascinating versatility of TPSs.Allelic variation from wild This work highlights the incredible diversity, promiscuity, and product plasticity of TPSs that allow plants to succeed in a constantly changing environment. Showing that both gene expression and sequence allelic variation in TPSs contribute to differences in the volatile profiles paves the way to incorporate multiple strategies to breeding pipelines—for example, modulating gene expression to achieve a certain amount of a specific volatile, introgressing wild allelic variants into commercial varieties to incorporate targeted compounds, or even designing synthetic TPSs with the desired properties.


Functional human IgG anti-IgE from one out of six AD donors and rabbit IgG anti-IgE induced the release of preformed  and de novo synthesized mediators (PGD2 and LTC4) from HCMCs. Human IgG anti-IgE was more potent than rabbit IgG anti-IgE in inducing proinflammatory mediators from HCMCs. Human monoclonal IgE was a competitive antagonist of both human and rabbit IgG anti-IgE. Although functional anti-IgE autoantibodies rarely occur in patients with AD, when present, they can powerfully activate the release of proinflammatory and vasoactive mediators from HCMCs.Mast cells are multifunctional immune cells with complex roles in tissue homeostasis and disease. Cardiac mast cells (HCMCs) are strategically located within the human myocardium, in atherosclerotic plaques, in proximity to nerves, and in the aortic valve. HCMCs express the high-affinity receptor (FcεRI) for IgE and can be activated by anti-IgE and anti-FcεRI. Autoantibodies to IgE and/or FcεRI have been found in the serum of patients with a variety of immune disorders. We have compared the effects of different preparations of IgG anti-IgE obtained from patients with atopic dermatitis (AD) with rabbit IgG anti-IgE on the release of preformed (histamine and tryptase) and lipid mediators [prostaglandin D Mast cells are immune cells present in most connective tissues where they play multifunctional roles in tissue homeostasis and disease , 2. Mast2 (PGD2), cysteinyl leukotriene C4 (LTC4)]  –43. Mast (LTC4)] . In fact (LTC4)] –47 and t (LTC4)] –49.Recent epidemiological studies have reported an increased risk of coronary artery disease and/or heart failure in patients with IgE-mediated allergic disorders –52. MoreSeveral investigators have reported the presence of autoantibodies targeting either IgE –60, and 4, PGD2) involved in several cardiovascular and metabolic disorders , Hanks’ balanced salt solution, fetal calf serum (FCS) , pronase, deoxyribonuclease I , Percoll , and CD117 MicroBead kit , HClOlla, CA) . Rabbit dextrose .This study was approved by the Ethics Committee of the University of Naples Federico II, School of Medicine (Protocol N. 198/18), and informed consent was obtained from participants prior to the collection of blood specimens according to recommendations from the Declaration of Helsinki. Peripheral blood was obtained from six AD patients (aged 5–17 years) with similar clinical pictures  . Blood sSerum from six AD patients and comparable high levels of IgG antibodies to anti-IgE were passed through the immunosorbent Sepharose column coated with purified IgE (ADZ). Immunosorbent-bound IgG with anti-IgE activity were collected. IgE content was less than 0.05 U/ml .IgE myeloma protein was purified from a myeloma patient (ADZ) as previously described –82. No IThis study was approved by the Ethics Committee of the University of Naples Federico II, School of Medicine (Protocol N. 7/19). Heart tissue was obtained from patients undergoing heart transplantation as previously described , 74. The4 mast cells per tube) resuspended in P2CG buffer were placed in 12 × 75 mm polyethylene tubes. Then, 0.2 ml of each prewarmed releasing stimulus was added and incubation was continued at 37° C for 45 min  induced by functional human anti-IgE (4.2 × 10–2 ± 5 × 10–3 μg/ml) was lower than HRSENS caused by rabbit anti-IgE (4.6 × 10–1 ± 5 × 10–2 μg/ml) (p < 0.005)  concentration-dependently induced tryptase release from HCMCs. In the same experiments, higher concentrations (3 × 10–2 to 3 μg/ml) of rabbit IgG anti-IgE also caused tryptase secretion. By contrast, five preparations of non-functional IgG anti-IgE did not induce the release of tryptase. The maximal tryptase release induced by human anti-IgE from HCMCs was similar to that caused by rabbit IgG anti-IgE (Table 50 caused by human anti-IgE (5.8 × 10–2 ± 1.2 × 10–2 μg/ml) was significantly lower than of rabbit IgG anti-IgE (3 × 10–1 ± 3.3 × 10–2 μg/ml) (p < 0.0001)  are derived from arachidonic acid (AA) through the 5-lipoxygenase (5-LO) pathway [2 (cPLA2) cleaves phospholipids at the outer nuclear membrane to generate free AA. 5-LO then oxidizes AA in the presence of 5-LO activating protein to generate leukotriene A4 (LTA4), which is subsequently converted to LTC4 by LTC4 synthase [4 and through the cyclo-oxygenase to form PGD2 [–2 to 1 μg/ml) and rabbit anti-IgE (3 × 10–2 to 3 μg/ml) on the de novo synthesis of LTC4 and PGD2 from HCMCs. Figure 4 from HCMCs. The maximal LTC4 release induced by human anti-IgE was similar to that caused by rabbit anti-IgE  compared to rabbit anti-IgE (4.7 × 10–1 ± 3.6 × 10–2 μg/ml) (p < 0.0002)  from HCMCs. Figure –2 to 10 μg/ml) of human polyclonal IgG failed to induce the release of proinflammatory and vasoactive mediators from HCMCs. Similar results were obtained when human polyclonal IgG purified from AD patients were incubated with HCMCs (data not shown).In these experiments, we evaluated the effects of increasing concentrations of human polyclonal IgG purified from five healthy donors on the release of preformed  and de novo synthesized mediators LTC, PGD2 frr = 0.79; p < 0.0001). These results suggest that these cells are a source of both mediators in the supernatants of anti-IgE-activated HCMCs. Similarly, there was a positive correlation between the release of histamine and LTC4     Fig. B, histam01) Fig. C, and LT01) Fig. D. Compar01) Fig. .Fig. 2Ef–1 and 1 μg/ml) of human polyclonal IgG did not modify the activating capacity of either human or rabbit anti-IgE to induce histamine release from mast cells (data not shown).The previous results are compatible with the hypothesis that human and rabbit anti-IgE induce the release of mediators through the interactions with IgE-bound to FcεRI on HCMCs. To support this hypothesis, we examined whether human monoclonal IgE purified from a myeloma patient (ADZ)  interferMast cells are strategically located in different sections of murine , 6, 94 aSerum IgE levels are elevated in patients with myocardial infarction , 102, coAutoantibodies to IgE and/or FcεRI can occur in patients with different inflammatory disorders such as CSU , 105–107Although the role of naturally occurring autoantibodies to IgE and/or FcεRI in inflammatory disorders is still a fascinating and unsettled issue , severalOur results provide preliminary information on the prevalence of anti-IgE autoantibodies in AD patients. In this study, only one preparation of human IgG anti-IgE out of six patients with AD triggered the release of mediators from mast cells isolated from human cardiac tissue. The apparent low frequency of functional anti-IgE autoantibodies might explain the controversial results on the presence of functional autoantibodies in different types of AD patients , 62, 67.Our results also provide some insight into the potency of naturally occurring IgG autoantibodies anti-IgE. Although the HCMC reactivity to human IgG anti-IgE was similar to that of rabbit IgG anti-IgE, the potency of functional human anti-IgE was consistently higher than that of rabbit anti-IgE in inducing the release of preformed and de novo synthesized mediators from HCMCs.4 is detrimental for atherosclerosis and myocardial infarction [2 is also detrimental for the cardiovascular and respiratory systems [Our results also demonstrate that when the human anti-IgE is functionally active, it can work as a complete secretagogue inducing the release of a wide spectrum of proinflammatory, vasoactive, and immunomodulatory mediators from HCMCs. For instance, histamine exerts profound cardiovascular effects in humans , 71 and farction  and dilafarction . PGD2 is systems , 76.The presence of spontaneously occurring autoantibodies to IgE/FcεRI has been described by several investigators since the mid-80s, but the relevance of these observations continues to generate some controversies in the field , 109. WeIncreasing concentrations of human monoclonal IgE concentration-dependently shifted to the right the activating properties of both human and rabbit anti-IgE. These results are compatible with the hypothesis that soluble monoclonal IgE was a competitive antagonist of both human and rabbit antibodies to IgE. The specificity of this observation was supported by the finding that human polyclonal IgG did not interfere with the capacity of either human and rabbit anti-IgE to induce histamine secretion from HCMCs.4, PGD2) induced by human and rabbit anti-IgE, suggest that HCMCs are the targets of these antibodies.Our study has several limitations that should be pointed out. The experiments were performed using primary mast cells isolated from myocardial tissue obtained from patients undergoing heart transplantation. These mast cells might have biochemical characteristics different from those of cells obtained from healthy donors. In the past we addressed this issue by comparing the release of mediators from mast cells isolated from failing hearts and from subjects who died without cardiovascular diseases . In thisOur results might have translational relevance in several cardiovascular disorders. Cardiac mast cells seem to play a role in experimental myocarditis , 120, myIn conclusion, although functional autoantibodies to IgE rarely occur in patients with AD, when these antibodies are present, they are able to trigger the release of proinflammatory and vasoactive mediators from HCMCs.