The molecular weight of polyimide was determined using the method of light scattering and the study of the intrinsic viscosity of polyamic acid solutions. To obtain CFRPs, the uniform distribution of polyimide powder on continuous carbon fibers via electrostatic spraying and further hot calendering and pressing were applied. The study of the structure of the obtained carbon plastics via scanning electron microscopy has shown that the growth of the molecular weight of polyimide prevents the impregnation of carbon fiber with the introduced polyimide. Moreover, an increase in the molecular weight of polyimide leads to a rise in glass transition and thermal decomposition temperatures up to 590 °C, while the degree of crystallinity of CFRP falls. Nonetheless, raising the molecular weight from 22,000 to 70,000 g/mol of a binder polymer improves the interlayer fracture toughness G At present, much attention is paid to receiving new composite materials based on thermoplastics reinforced with continuous fibers. Relative to the traditional composite materials based on thermosetting binders, composite materials based on thermoplastic polymers have the following advantages: firstly, thermoplastics have high impact strength, good resistance to certain chemicals, and low moisture absorption compared to thermosets; secondly, the short cycle times, made possible with thermoplastics, make them a good potential alternative due to the significant reduction in manufacturing costs compared to thermoset binders ,2. An adThe use of high-tech thermoplastics in combination with continuous carbon fibers is of particular interest in the development of fibrous composite materials. In addition to the traditional advantages of thermoplastic polymers , they usually have improved mechanical properties such as high level of impact strength, strength, stiffness, and fatigue resistance ,4. MoreoTo date, polysulfones , polyethIn the development of CFRPs based on high-tech thermoplastics, an important characteristic of the polymer is its molecular weight. Thermoplastics used qua binders of the carbon fibers have a disadvantage associated with lower impregnation of reinforcing fibers compared to thermosetting binders. The reason is the high melt viscosity of the thermoplastic resin. On the one hand, the molecular weight will affect the viscosity of the thermoplastic binder for the composite material and, on the other hand, affect the mechanical properties of CFRP. The authors of  studied 1,3-bisbenzene (dianhydride R) with Tm ~164 °C was obtained from TechKhimProm LLC, Yaroslavl, Russia. 4,4′-bis(4″-aminophenoxy)biphenyl) (diamine BAPB) with Tm ~198–199 °C was obtained from VWR International. Phthalic anhydride (PA) was used as a chain limiter for the polycondensation reaction, and Tm ~131–134 °C was obtained from Sigma-Aldrich Co. LLC, St. Louis, MO, USA. Acetic anhydride, benzene, and triethylamine were supplied by Sigma-Aldrich Co. LLC.To obtain CFRPs, continuous carbon fibers of the ELUR P-0.08 type (Russia) were used as a reinforcing filler in the form of a tape 200 mm wide with a linear density of 15 g.The synthesis was carried out using the method of chemical imidization in accordance with the protocol described in . In the Based on the synthesized polyimide powders, unidirectional carbon plastics were obtained via the formation of a prepreg using a technological and environmentally friendly approach based on the uniform distribution of polyimide powder on a carbon tape using electrostatic spraying followed by their impregnation in heated calenders and hot pressing. The peculiarity of this impregnation method is that due to the electrostatic spraying of the powder over the surface of the reinforcing fibers, the distance over which the polymer melt must spread is reduced. In this regard, it is possible to obtain a prepreg with good impregnation even when using high-viscosity thermoplastic binders ,21,22. The process to obtain samples of CFRPs based on polyimide R-BAPB consisted of several operations, similar to those described in . The resulting synthesis of the polyimide powder with different molecular weights was crushed and sifted through a 125 µm sieve. As a result, the powder particles were formed with a particle size not exceeding 125 microns. Blanks, 30 × 15 cm in size, were cut from a carbon tape for the subsequent introduction of polyimide powder into them by an electrostatic sprayer. A carbon tape coated with polyimide powder was placed between the sheets of the polyimide film and rolled through the calenders heated up to 360 °C. In the process of heat treatment, the fibers are moistened by molten polyimide, which spreads and penetrates into the interfiber space under the action of capillary forces. The prepregs obtained as a result of rolling through the calenders were cut into blanks with dimensions of 15 × 3.2 cm. The blanks were stacked and packed in 36 layers in a mold with 0° orientation between the layers. From one edge in the middle (between the 18th and 19th layers), a polyimide film 3 cm long and 3.2 cm wide was laid to obtain a crack in this place, which was necessary for the subsequent testing of CFRP for interlayer fracture toughness. Then, the prepregs were pressed at a pressure of 1 MPa. Pressing was carried out according to the following regime: heating from room temperature up to 360 °C at a heating rate of 5 °C/min; exposure at the temperature of 360 °C for 10 min; cooling from 360 °C to 280 °C; exposure at the temperature of 280 °C for 1 h; cooling from 280 °C to room temperature under pressure. For all CFRPs, the fiber content varied within 64–70 vol.%. To study the bending strength, plates with plates ~1 mm thick and ~4 mm wide were cut from the obtained carbon plastics.The molecular weight of the synthesized polyimides was calculated on the basis of the study of the PAA prepolymer since the R-BAPB polyimide is an insoluble polymer.Rh-D of macromolecules are determined using static (SLS) and dynamic light scattering methods  = Kηfficient f = KfMba and b of the KMHS equations are sensitive indicators of the macromolecular shape. a = 0.83 ± 0.04 and b = 0.66 ± 0.06 are typical for flexible chain polymer in thermodynamic solvent. Exponents A0 are also evidence of the increased flexibility of the PAAs chains. This characteristic is determined by the experimental values of molar mass M, intrinsic viscosity [η], and translation diffusion coefficient D0 by the formula [The values of the hydrodynamic invariant  formula .(7)A0=ηA0 values hydrodynamic invariant for PAAs samples do not change with increasing MM, and the average value A0 = (3.1 ± 0.2)∙10−10 erg·K−1·mol−1/3 are in good agreement with the experimental and theoretical values of A0 = 3.2∙10−10 erg·K−1·mol−1/3 for linear flexible-chain polymers [A0 is constant in wide intervals of polymer molar mass and depends on molecular conformational and architecture. In particular, for flexible chain polymers and rigid chain polymers, the average experimental values of hydrodynamic invariant differ by almost 20%: A0 = 3.2∙10−10 erg·K−1·mol−1/3 [−10 erg·K−1·mol−1/3, respectively. Low values of A0 ≤ 2.8, i.e., lower than the theoretically predicted value for a hard sphere, are typical for polymers with complex architecture, such as molecular brushes [It is clearly seen that the polymers . Note th·mol−1/3  and 3.8∙ brushes , as well brushes  and star brushes . A of the statistical segment. As mentioned above, the samples were studied in a thermodynamically good solvent. Accordingly, to determine the value of the Kuhn segment length, one should use theories that take into account volume effects, namely, theories of Stockmayer-Fixman [∞ = 2.84 × 1023 mol−1 is Flory parameter, and P∞ = 5.11 is the hydrodynamic parameter. The parameter B depends on the thermodynamic quality of the solvent; for a θ–solvent, B = 0. Therefore, using an extrapolation procedure to zero molar mass, the unperturbed dimensions of polymer chains can be found.The measure of equilibrium stiffness is the length r-Fixman  for intr-Bywater  for tranM0.5 and f/M0.5 on wM1/2 obtained for the polymer understudy. Both dependencies can be approximated by straight lines, and the Kuhn segment length A was derived from their intercepts using Equations (8) and (9). Using viscometry data yields a Kuhn segment length Aη = (2.2 ± 0.3) nm, and the analysis of the translational friction data yields Af = (2.0 ± 0.2) nm. The obtained values of Aη and Af confirm the above assumption that PAAs are a flexible-chain polymer. These values are in agreement with the magnitudes of A reported for polyimides of different chemical structures with swing joint groups or flexible spacers. In particular, Kuhn segment lengths for aromatic polyimides were found  to be 1.9–3.8 nm [9–3.8 nm , 2.5–4.29–3.8 nm , 1.6–4.49–3.8 nm , 2–3 nm 9–3.8 nm , and 1–29–3.8 nm . The hig9–3.8 nm . An important characteristic in the production of carbon-fiber-reinforced plastics based on thermoplastic binders is the value of the molten polymer’s viscosity. At a high viscosity of the melt, the impregnation of the fibers deteriorates, and, therefore, it is no longer possible to obtain defect-free carbon plastics. In this regard, the viscosity of the polyimide melt of different molecular weights was studied .Angular frequency viscosity studies of R-BAPB melts as a function of molecular weight showed that as the molecular weight increases, an elevation in viscosity is observed, especially at low shear rates. The composition with a low molecular weight PI 0.93 is characterized by an almost Newtonian melt flow. As the molecular weight rises, the dependence of viscosity on frequency begins to appear (starting from sample PI 0.95). When moving to higher molecular weight R-BAPB homologs , the overall level of system viscosity increases, as well as the appearance of a strong effect of changes in melt viscosity on angular frequency. Thus, at the frequency of 100 rad/s, the R-BAPB melt viscosity for PI 0.97 is ≈1000 Pa*s. Lowering the angular frequency to 10 rad/s leads to rocketing melt viscosity up to ≈3000 Pa*s see . Thus, tThe analysis of the obtained DSC and TGA data for the carbon plastics with different molecular weights of polyimide is represented in Based on the obtained TGA data, it can be concluded that all the studied polyimide carbon plastics are stable up to the temperature of ≈570 °C, after which the processes of thermal destruction begin to become more active. With an increase in molecular weight, there is a slight enchantment in the thermal degradation temperature τ5 (the temperature at which a loss of 5% of the polymer mass occurs), especially when moving to the CFRP sample with the highest molecular weight.It is known that the mechanical properties of composite materials are affected by the quality of impregnation of reinforcing fibers with a binder matrix, which was studied by some researchers . ImpregnThe results of studies of the temperature dependences of the storage moduli E′ and losses E″ of the CFRPs are shown in After considering the temperature dependence of the storage modulus (E′) for all carbon plastics of the R-BAPB homologous series, except polyimide with the ratio of the matrix component equal to 0.99, it can be stated the two-stage mechanism for reducing the modulus with increasing temperature is most probably observed, which is associated with the presence of the crystalline phase in the polymer a. For thThe results of the study of the mechanical properties of CFRPs are presented in 2. As the molecular weight increases, the interlayer viscosity rises significantly to 720 J/g and 830 J/g for PI 0.97 and PI 0.98, respectively. An increase in interlayer fracture toughness is due to a decrease in the brittleness of the polyimide matrix in the higher molecular weight samples. For the CFRP based on high molecular weight polyimide PI 0.99, the highest value of interlaminar fracture toughness of 1800 J/m2 is measured. Such a high value of interlayer fracture toughness for the sample of carbon fiber plastic based on PI 0.99 is probably caused by a decrease in brittleness not only because of an increase in the macromolecule length but also due to the absence of the crystalline phase. CFRPs obtained on the basis of the low molecular weight homologs for the samples based on PI 0.93 and PI 0.95 have rather low values of interlaminar fracture toughness in the range of 350–370 J/m−1. Analysis of molar mass dependences of intrinsic viscosity and hydrodynamic radius showed that the synthesized linear polymers PAAs are typical flexible-chain polymers. The Kuhn segment length is A = 2.2 nm.The samples of linear PAAs were investigated via static and dynamic light scattering and viscometry. The solutions in DMAA were molecularly dispersed. Molar masses of prepared samples were in the range from 22,000 to 70,000 g·mol2 is observed. However, the strength and bending modulus somewhat decrease with increasing molecular weight owing to the deterioration of the impregnation of the carbon fibers with the binder because of the high viscosity of the polyimide melt. Based on the PAA of various molar masses, polyimide powders were obtained via chemical cyclization CFRPs were formed from the obtained R-BAPB polyimide powders; for this, a method based on the uniform distribution of polyimide powder on the carbon fibers via electrostatic spraying followed by their impregnation in heated calenders and hot pressing was used. With an increase in the molecular weight, a regular increase in the glass transition and melting temperatures and a decrease in the degree of crystallinity are observed. The lowest degree of crystallinity (1%) is found in the CFRP sample with the highest polyimide molecular weight PI 0.99. CFRPs based on the polyimide sample PI 0.99 have the highest thermal stability, close to 590 °C. Due to the presence of the crystalline phase, the CFRPs based on PI 0.93, PI 0.95, PI 0.97, and PI 0.98 can withstand loads up to temperatures of ≈300 °C. In the case of the CFRP PI 0.99, the operating temperature is limited by its glass temperature. With an increase in the molecular weight of polyimide, an increase in the interlayer fracture toughness of the carbon fiber plastics up to 1800 J/m


The incorporation of polycarboxylate ether superplasticizer (PCE)-type polymers and silica fume (SF) in high-performance concretes (HPC) leads to remarkable rheological and mechanical improvements. In the fresh state, PCEs are adsorbed on cement particles and dispersants, promoting the workability of the concrete. Silica fume enables very well-compacted concrete to be obtained, which is characterized by high mechanical parameters in its hardened state. Some PCEs are incompatible with silica fume, which can result in slump loss and poor rheological behavior. The main objective of this research is to study the influence of three types of PCEs, which all have different molecular architectures, on the rheological and mechanical behavior of high-performance concretes containing 10% SF as a partial replacement of cement. The results show that the carboxylic density of PCE has an influence on its compatibility with SF. According to the World Cement Association (WCA), the cement industry is responsible for 5–6% of global carbon dioxide emissions . This as3Al(OH)6 12H2O]2(SO4)3 2H2O [PCEs are currently the most effective at reducing the water content (up to 40%) and improving the workability and fluidity of concrete compared to other types of superplasticizers. PCEs are copolymers and have a comb structure ,9. The m4)3 2H2O . This oc4)3 2H2O ,19,20,214)3 2H2O ,23,24,25The adsorption and dispersing capacity of PCEs in cement-based materials depends on their chemical structure as well as their molecular weight, carboxylic groups, density, and length of the side chain ,27,28. STo the best of our knowledge, the effectiveness of PCEs in high-performance concrete (HPC) containing silica fume (SF) as a partial replacement of cement has not been fully investigated. There are several knowledge gaps regarding this matter, especially concerning the interaction of SF and PCE (with different molecular weights and carboxyl densities) in HPC. To fill this research gap, the authors of this paper conducted studies of the interaction between PCE and silica fume in cement slurries as well as in high-performance concretes (HPC) that have a low w/b ratio. Three PCEs with different molecular compositions were used to study their impact on the properties of concrete (HPC). Therefore, the fluidity and mechanical properties of HPC were evaluated. The impact of the PCE on the shear stresses, plastic viscosity, and zeta potential of cement slurries (containing 10% silica fume as a cement substitute) was also measured.Three types of superplasticizers produced in Algeria were used: Polyflow SR 3600—designated as PCE1, Polyflow SR 5400—designated as PCE2, and Medaflow 30—designated as PCE3. The three polymers are new-generation, non-chlorinated, and based on modified polycarboxylic ether. The molecular weight  and the polydispersity indices (PDI) of the superplasticizers were measured using multi-detector steric exclusion chromatography (SEC) in France . The analytical conditions are presented in 2/kg. The chemical composition of the PC and SF is presented in The Portland cement (PC) that was used in the experimental study was CEM I 52.5 R. It is commercially produced by the Lafarge Company, whose manufacturing site is located in the city of Msila (in the eastern region of Algeria). The microsilica (SF) sample was obtained from Granitex, Algeria. Its marketing name is Medaplast HP, and its specific surface is 23,000 mThe properties of the aggregates are summarized in + ions, which come from carboxyl groups and NaOH, react with each other in a stoichiometric ratio. Therefore, the more NaOH needed to titrate the sample, the more carboxyl groups it contains. The content of carboxyl groups can then be calculated as follows: C(COO–) = content of carboxyl groups (mmol/100 g);CNaOH = molar concentration of NaOH (mmol/L);VNaOH = volume of the NaOH solution used (L); and m = weight of the sample.The acid-base titration method was used to measure the content of carboxyl groups in each polymer sample . The meaThe recipe of the concrete was determined on the basis of the Dreux–Gorisse method while taking into account the ratio of its components , as shown in The slump test was used to evaluate the fluidity of the concrete mix in the case of all the formed HPCs. The dosage of superplasticizer (PC/b) was 1.75%, and it was determined using the VT 550 viscometer. The initial fluidity of the HPC was measured 5 min after mixing all the components. The concrete mix was then placed in a container, which was covered with a plastic film in order to prevent any water evaporation. The second slump test measurement was made after 1 h of concrete maturation. In each case, the concrete mix was stirred for one minute before taking the measurements.2/g) as a partial replacement of cement, the water-to-binder ratio (w/b) was assumed to be 0.35. In this case, for an amount of water equal to 35 g, the mix contains 90 g of cement and 10 g of SF, as well as an added superplasticizer (PCE) in an amount of 0–2.5 wt% of the total cement-based binder. The test was performed at a temperature of 20 ± 1 °C. The rheology of the cement paste was evaluated using a VT 550 viscometer, which is a rotational viscometer. Using this viscometer, the following parameters of cement pastes can be measured: shear stresses (τ) and viscosities (µ), which are obtained as a function of shear rates  were determined using a Malvern ZETASIZER 2000 in materials, processes and environment research unit; Algeria. In a standard experiment, 1 cm2) were prepared according to the EN 206-1 protocol. These specimens were kept for 24 h in molds. After demolding, all the specimens were stored in tap water at 20 ± 1 °C for 3, 7, and 28 days.In order to determine the compressive strength, six cylindrical concrete specimens  and the fluidity assessed after 1 h (slump retention) are shown in  of PCEs . It meanPCE1, with the lowest carboxylic density and molecular weight, exhibited a poor slump and poor slump retention due to the prior adsorption of the PCE on the silica fume surfaces ,29. The PCE2 (with the highest molecular weight) had a bad initial slump and the highest slump retention. The amount of absorbed PCE on the surface of the cement paste increased with a decrease in molecular weight. Therefore, the PCE with a high molecular weight significantly delayed the hydration of the cement. This is due to the fact that the main chain of PCEs adsorbs on different cement particles simultaneously, making them agglomerated and, in turn, hindering the hydration process .The saturation point is the value of dosage beyond which the superplasticizer has no effect on the rheological properties of the cement paste. It was determined using the viscometer VT 550. Each of the polymers  had the same saturation point value, i.e., 1.75, at the tested water-to-binder ratio (w/b). 2+). PCE3 had a high carboxyl content (1.95 mmol/g) and a moderate molecular weight, whereas its polydispersity index  presented a high absolute value of zeta potential in the cement paste (9.7735 mV). This is due to its having the best adsorption capacity with regard to cement particles.The zeta potentials of the cement pastes prepared with the addition of the PCEs (with different carboxylic densities and molecular weights) are shown in The compressive strengths at 3, 7, and 28 days of HPC without superplasticizers are, respectively, 40.2, 45.1, and 50.9 MPa. They are lower than the compressive strength of the HPC with PCEs . TherefoThe aim of this work is to study the compatibility of a polymer with silica fume in HPC. The results show that the chemical structure  of the polymer influences this compatibility. The best chemical structure is a polymer with a moderate molecular weight and high carboxyl density. The PCE with a moderate molecular weight and the highest content of carboxylic groups has the best dispersion , the lowest viscosity, and the highest compressive strength of hardened HPC. Therefore, it can be considered to be compatible with SF. Such compatibility was not observed in the case of the PCE with the low carboxyl density, which was due to the fact that this PCE was adsorbed on the SF and reduced the ability of the cement particles to disperse. Therefore, the workability of the HPC was also reduced.There is a clear relationship between the zeta potential and rheological properties of cement paste and the anionic charge density and molecular weight of PCEs. A good correlation was also found between zeta potential, carboxylic density, and compressive strength.The results from the retention slump tests show that a PCE with a high molecular weight can be applied to precast concrete, whose consistency has to be retained over a longer period of time (until this concrete is delivered to a construction site).The molecular weight of polycarboxylate superplasticizer has a great impact on the properties of cementitious systems. Therefore, PCE3 can be seen to be a perfect polymer in which all the macromolecules have the same length and the same molar mass (PDI close to 1). This, in turn, makes it the most efficient admixture, which was proved by the results of the research conducted.


Otud6a−/− mice produced more type I interferon after virus infection compared to cells from WT mice. −Otud6a−/− mice also exhibited increased resistance to lethal HSV-1 and VSV infections, as well as LPS attacks due to decreased inflammatory responses. Mechanistically, mass spectrometry results revealed that UBC13 was an OTUD6A-interacting protein, and the interaction was significantly enhanced after HSV-1 stimulation. Taken together, our findings suggest that OTUD6A plays a crucial role in the innate immune response and may serve as a potential therapeutic target for infectious disease.OTUD6A is a deubiquitinase that plays crucial roles in various human diseases. However, the precise regulatory mechanism of OTUD6A remains unclear. In this study, we found that OTUD6A significantly inhibited the production of type I interferon. Consistently, peritoneal macrophages and bone marrow-derived macrophages from  The ubiquitin–proteasome pathway (UPP) is the most broadly effective mechanism for modulating a broad range of cellular physiology, including antigen presentation ,2, homeoUbiquitination is involved in regulating signal transduction in antiviral immune responses by precisely activating downstream proteins through pattern recognition receptor signaling, which initiates the innate immune response . UbiquitOTUD1 functions as a metastasis suppressive factor by repressing the metastasis of breast cancer through the deubiquitination of SMAD7 . AdditioOTUD6A is a member of the OTU domain family DUBs. Previous studies have shown that OTUD6A regulates cell proliferation and tumorigenesis. For instance, OTUD6A deubiquitinates and stabilizes Drp1 to regulate mitochondrial morphology in colon cancer, and it plays a critical role in promoting tumor cell resistance to chemoradiotherapy by deubiquitinating and stabilizing TopBP1 ,36. OTUDIn this study, we used a library of DUB expression plasmids to screen for DUBs that specifically regulate classical natural immune signaling pathways and found that overexpression of OTUD6A suppressed type I interferon expression. It was found that OTUD6A interacts with UBC13 and not only participates in the regulation of innate immunity but also regulates the inflammatory response. In conclusion, our findings suggest that OTUD6A plays a dual role in regulating innate immunity and inflammatory responses and could provide a potential therapeutic target for related immune diseases.2 atmosphere. HEK293T cells, Hela cells, and iBMDM cells were maintained in our laboratory. For the isolation of mouse primary peritoneal macrophages (PMs), cells were collected from the lavage of the peritoneal cavity from mice that were pre-stimulated with thioglycollate (TG) for 3 days. For BMDMs, bone marrow cells were isolated from femurs and tibiae of 8-to-12-week-old Otud6a−/− mice and WT mice. Cells were cultured with 20 ng/mL recombinant murine GM-CSF  in a 10 cm dish for 7 days before experiments.HEK293T cells, Hela cells, iBMDM cells, primary bone-marrow-derived macrophage (BMDM) cells, and peritoneal macrophage (PM) cells were cultured in DMEM medium supplemented with 10% FBS  and 100 U/mL penicillin and 100 mg/mL streptomycin at 37 °C under 5% COPolyethylenimine (PEI)  and jetPRIME  were used for transfection. DSS (Cat# D8906) was purchased from Sigma-Aldrich. Listeria monocytogenes were provided by the laboratory of Zengfan Jiang, Peking University. The antibodies used in this study were as follows: Anti-OTUD6A ; Anti-NF-κB P65 ; Anti-p-p65 (Ser536) (93H1) ; Anti-IκBα ; Anti-p-IκBα ; Goat anti-mouse IgG, HRP conjugate ; and Goat anti-rabbit IgG, HRP conjugate .The plasmids used in this study were prepared using standard molecular biology techniques, and the coding sequences were completely verified. Ifnβ promoter luciferase reporter plasmids (IFN-β–luc) and mammalian expression plasmids for human STING, MAVS, TBK1, IRF3, TRIF, and RIG-1 were previously described . All tru293T cells were seeded onto 24-well plates and transfected with the luciferase reporter plasmid, along with the specified expression plasmids or an empty vector (VEC). After 24 h, luciferase activity was measured using the Dual-Luciferase Reporter Assay System  following the manufacturers’ instructions.−ΔΔCt) using GraphPad Prism Version 8.0 . The data are presented as relative abundance normalized to GAPDH mRNA levels.Total RNA from cells, blood, and tissues was extracted using Trizol reagent  and then reverse transcribed into cDNA using HiScript II Q RT SuperMix . Quantitative Real-time PCR was performed using the ChamQ Universal SYBR qPCR Master Mix  on a 7500 Fast real-time PCR machine . The qRT-PCR Data were analyzed using the Livak method . Subsequently, the raw reads in a Fastq format were processed using in-house PERL scripts. Clean reads were obtained by removing reads with adapters, reads with more than 5% unknown bases, and low-quality reads . Additionally, the Q20 and Q30 scores, as well as the GC content of the clean data, were calculated . The original data of the RNA-seq were uploaded to the GEO DataSets.The whole RNA of cells with specific treatment was purified using RNeasy Mini Kit (QIAGEN NO. 74104). The transcriptome library for sequencing was generated using VAHTSTM mRNA-seq v2 Library Prep Kit for Illumina  according to the manufacturer’s recommendations. Following library preparation, the libraries were sequenced on the Illumina Hiseq X Ten platform using (2 × 150 bp) paired-end module. The raw images were transformed into raw reads by base calling using CASAVA software (7 plaque-forming unit (PFU) virus) or VSV (3 × 108 PFU per mouse). At the indicated times after infection, the mice were sacrificed, and blood samples were collected. The expression of cell cytokine and viral genes were analyzed using RT-qPCR.Wild-type C57BL/6 mice were obtained from the Department of Laboratory Animal Science of Peking University Health Science Center, Beijing. The mice were bred and housed under specific pathogen-free conditions. For all in vivo experiments, age- and sex-matched mice were used unless otherwise specified. Six-week-old mice were infected with HSV-1 virus . The cells were then transfected with a total of 10 μg of Flag-OTUD6A expression plasmids or an empty plasmid. After 24 h of transfection, the cells were lysed in lysis buffer , 150 mM NaCl, 12.5 mM β-glycerolphosphate, 1.5 mM MgCl2, 2 mM EGTA, 10 mM NaF, 1 mM Na3VO4, 2 mM DTT) supplemented with protease inhibitors. The lysates were centrifuged, and the supernatant was incubated with anti-Flag antibodies at 4 °C overnight. The following day, prewashed protein A/G beads  were added to the lysates and incubated at 4 °C for 4 h. The beads were then washed four times with cold PBS and eluted with SDS sample buffer containing DTT by boiling for 10 min in preparation for western blotting.HEK293 cells and Hela cells were seeded on 10 cm dishes . The p values were as follows: ns p > 0.05, * p < 0.05, ** p < 0.01, *** p < 0.001, and **** p < 0.0001.Differences between experimental and control groups were evaluated using Student Ifnβ. Luciferase reporter assays demonstrated that among the OTUDs tested, OTUD6A exhibited the most significant inhibition of Ifnβ expression  compared to wild-type mice (Otud6a−/− mice with vesicular stomatitis virus (VSV) and observed that Otud6a−/− mice had a higher survival rate and lower viral RNA levels  was a specific interacting protein of OTUD6A c. UBC13 The balance between immunity and inflammation responses is dynamic under steady-state conditions, but can quickly shift in response to pathogen invasion ,47. The Ifnβ expression induced by RIG-I/MAVS, cGAS/STING, TBK1/IRF3, or TRIF overexpression. These results indicate that OTUD6A had a broad inhibitory effect on the production pathway of Ifnβ. It is worth noting that OTUB1 and OTUB2 negatively regulate the expression of Ifnβ induced by the VISA-mediated pathway, but not the TBK1-mediated pathway [Ifnβ expression during VSV, SeV, or HSV-1 infection was also observed upon overexpression of OTUD6A. In vitro experiments demonstrated that OTUD6A-deficient BMDMs and PMCs expressed more interferons when infected with SeV compared to WT. Furthermore, in vivo experiments revealed that Otud6a−/− mice expressed more interferons when infected with VSV or HSV-1 and were more resistant to death than WT mice. NGS analysis showed that Otud6a−/− iBMDM expressed more interferons when infected with HSV-1 compared to WT.Our findings demonstrate that overexpression of OTUD6A led to the downregulation of  pathway . The dowOtud6a−/− BMDMs and PMCs showed that Otud6a−/− mice were more resistant to death induced by LPS or DSS compared to WT. These findings are consistent with the work of Xin Liu et al. [Il1β levels and inflammation [Otud6a−/− mice also confirmed that OTUD6A promoted the induction of proinflammatory cytokines.In vitro LPS treatment in u et al. , which sammation . In vivoT. gondii infection by increasing the stability of UBC13. Conversely, OTUD1 physically interacted with receptor-interacting serine/threonine-protein kinase 1 (RIPK1) and selectively cleaved K63-linked polyubiquitin chains from RIPK1 to inhibit the recruitment of NF-κB essential modulator (NEMO), thereby inhibiting the inflammatory response [Otud6a−/− PMC exhibited a reduced level of phosphorylated p65 but increased levels of phosphorylated and total IκBα, indicating that OTUD6A promotes the degradation process of pIκBα.To elucidate the mechanism by which OTUD6A participates in the immune response, we employed immunoprecipitation (IP) and mass spectrometry (MS) to enrich and identify potential interacting proteins of OTUD6A. Our results revealed that OTUD6A could interact with UBC13, and further verification experiments demonstrated that this interaction was partly dependent on the N-terminal of OTUD6A. UBC13 is a well-known E2 ubiquitin-conjugating enzyme and immune receptor, which has an important role in the process of tagging target proteins with lysine 63-linked polyubiquitin chains that are essential for the activation of NF-κB ,53. ReceIn summary, our findings demonstrated that OTUD6A plays a critical role in regulating both innate immunity and inflammatory response. Our data revealed that OTUD6A can both inhibit innate immunity and promote inflammatory response, highlighting its importance in accelerating the cellular response to deadly virus or bacterial infections. These results provided new insights for the treatment of infectious and inflammatory diseases, emphasizing the potential therapeutic value of targeting OTUD6A to modulate immune and inflammatory responses.


Background: Alzheimer’s disease (AD) and Korsakoff’s syndrome (KS) are two major neurocognitive disorders characterized by amnesia but AD is degenerative while KS is not. The objective is to compare regional volume deficits within the Papez circuit in AD and KS, considering AD progression. Methods: 18 KS patients, 40 AD patients (20 with Moderate AD (MAD) matched on global cognitive deficits with KS patients and 20 with Severe AD (SAD)), and 70 healthy controls underwent structural MRI. Volumes of the hippocampi, thalami, cingulate gyri, mammillary bodies (MB) and mammillothalamic tracts (MTT) were extracted. Results: For the cingulate gyri, and anterior thalamic nuclei, all patient groups were affected compared to controls but did not differ between each other. Smaller volumes were observed in all patient groups compared to controls in the mediodorsal thalamic nuclei and MB, but these regions were more severely damaged in KS than AD. MTT volumes were damaged in KS only. Hippocampi were affected in all patient groups but more severely in the SAD than in the KS and MAD. Conclusions: There are commonalities in the pattern of volume deficits in KS and AD within the Papez circuit with the anterior thalamic nuclei, cingulate cortex and hippocampus (in MAD only) being damaged to the same extent. The specificity of KS relies on the alteration of the MTT and the severity of the MB shrinkage. Further comparative studies including other imaging modalities and a neuropsychological assessment are required.  Amnesia refers to severe episodic memory deficits that interfere with independent daily living. While amnesic cases have been described more than one century ago and have largely contributed to the understanding of memory function and substrates , pathophWe focused the present study on Alzheimer’s disease (AD) and Korsakoff’s syndrome (KS), which are two major neurocognitive disorders  resultinThe regions crucial to both pathologies are actually part of a single brain circuit: the Papez circuit. There is increasing support in the literature to consider amnesia as the result of an impairment of a brain network responsible for memory, rather than to a particular region ,11,12. TThe goal of the present study is thus to compare structural brain damage in these two amnesia which are hypothesized to result from different pathophysiological mechanisms primarily involving either the hippocampus (in AD), the thalamus (in KS), or the Papez circuit in general. The present investigation therefore focuses on evaluating the volumes of regions belonging to the Papez circuit and considered as hallmarks to each pathology, taking the potential effect of AD evolution into account.Forty patients with AD (20 with Moderate AD (MAD) and 20 with Severe AD (SAD)), 18 patients with KS, and 70 healthy controls (HC) were enrolled in this study. All groups were matched for gender and education but not for age . All parInclusion for all participants: All participants spoke French as their native language. They did not present with previous neurological, psychiatric problems or history of severe brain injury . No participants presented with contraindications for an MRI scan . For patients, clinical neuroimaging examinations ruled out other etiologies that could explain memory impairments.Inclusion criteria specific to KS: KS patients were recruited as inpatients at Caen University Hospital (n = 9) and from a nursing home . They met the criteria for alcohol-induced major neurocognitive disorder, amnestic-confabulatory type, persistent (DSM-5 [t (DSM-5 ). All pat (DSM-5 ) score ≥Inclusion criteria specific to AD: AD patients were recruited from the local memory clinic  and in partnership with care facilities in the region of Normandy, France. All patients fulfilled the standard criteria for AD diagnosis reported by the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) [S-ADRDA) . Each paInclusion criteria specific to HC: All HC had preserved performance on the MMSE for their age and education level. None consumed more than 3 standard drinks (2 for women) per day on a regular basis, as recommended by the World Health Organization.As expected, all patient groups had lower Total MMSE score than HC, with KS and MAD presenting similar global cognitive deterioration and SAD exhibiting the most severe scores. On the Recall subtest of the MMSE, the three patient groups had significantly lower results than HC but performed similarly. For the Learning subtest, only the SAD group was impaired while for the Orientation and Attention subtests, KS and MAD performed lower than HC but better than SAD. On the Language subtest, the two AD groups performed lower than HC, with SAD presenting poorer performance than MAD. KS had preserved language results. On the Praxis subtest, only the MAD group had preserved results . The SAD2; matrix, 256 × 256). Imaging data were preprocessed and analyzed using the SPM12 toolbox (www.fil.ion.ucl.ac.uk/spm (accessed on 25 April 2023)). The data were normalized to the Montreal Neurological Institute (MNI) template and segmented into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). Normalized unmodulated images from controls were averaged with a threshold at 0.5 to create a binary gray matter mask for statistical analyses. For each participant, the total intracranial volume (TIV) was calculated based on the sum of the individual volumes of GM, WM, and CSF.A high-resolution T1-weighted anatomical image was acquired for each participant on a Philips Achieva 3T scanner  using a 3D fast-field echo sequence . The GLM showed that, for all 8 regions, there was a significant group effect on the regional volumes  = 47.3; Entire thalami: F = 38.3; Anterior thalamic nuclei: F = 38.28; Mediodorsal thalamic nuclei: F = 30.44; Anterior cingulate gyrus: F = 27.0; Posterior cingulate gyrus: F = 50.6; Mammillary bodies: F = 46.6; MTT F = 9.78; p < 0.001 in all cases). Shrinkage tended to be more severe in the KS group compared to both the MAD (p = 0.051) and SAD (p = 0.052) groups, which did not differ from each other (p = 1). For the mediodorsal thalamic nuclei and mammillary bodies, we found a similar pattern of significant results: smaller volumes in the KS group compared to both MAD (p = 0.008 and p = 0.02 respectively) and SAD (p = 0.005 and p = 0.027 respectively) groups.Further post-hoc analyses showed that the volumes of the entire thalami were significantly smaller in all patient groups compared to HC . The anterior thalamic nuclei, as well as the anterior and posterior cingulate gyri, were affected to the same extent in the three patient groups compared to HC . Shrinkage was significantly more severe in the SAD group compared to both KS (p < 0.001) and MAD (p = 0.024) groups, which did not differ between each other. The volume of the hippocampi was significantly smaller in all patient groups compared to HC (p < 0.001), MAD (p = 0.012) and SAD (p = 0.002), which did not differ significantly from each other. Finally, the volume of the MTT was lowest in the KS group and significantly different from HC  and the anterior thalamic nuclei were damaged to the same extent in AD and KS amnesia. Regarding the cingulate cortex, abnormalities have previously been observed in PET studies measuring cerebral glucose metabolism in patients with severe episodic memory deficits. Hypometabolism of the posterior cingulate cortex is classically found at an early stage of AD  and the The hippocampus is considered the predominant brain region responsible for episodic memory function  and remaThalamic abnormalities are considered a cardinal feature of KS . As expeKS and AD thus share, within the Papez circuit, a pattern of regional brain shrinkage, which could reflect the involvement of some common pathways leading to amnesia, irrespective of the etiology. The direct comparison of thalamic abnormalities in AD and KS provides new evidence that confirms the contribution of the thalamus to amnesia, above KS, and its key role in episodic memory . In patiContrary to what we observed for the anterior thalamic nuclei, the shrinkage of the mediodorsal nuclei was more severe in KS than in the AD groups. Given the putative role of this thalamic nuclei in cognition , this fiMammillary bodies were affected in the three patient groups but more severely in KS than in AD. Shrinkage of the mammillary bodies is consistently described in KS ,40,41,42The mammillothalamic tracts (MTT) were observed as damaged in the KS group only, suggesting that its alterations could be specific to this pathology. The MTT effectively connects the mammillary bodies, which itself receives input from the hippocampus, to the anterior thalamic nuclei . LesionsThis study finds its originality in its cohorts that are difficult to recruit and carefully select. The main limitation is the absence of a common and refined neuropsychological assessment. This absence is related to the fact that two patient groups come from different research programs conducted in the same imaging center, and thus sharing structural imaging modalities, but that included different cognitive evaluations. The common cognitive evaluation is limited to the MMSE, which makes it impossible to infer reliable and fine brain-behavior relationships. Our study provides the basis for developing a heuristic model that must be incremented by adding, in future studies, other imaging modalities, with a more detailed cognitive battery, to examine whether a similar pattern of hippocampal or anterior thalamic shrinkage observed in AD and KS is associated with similar or different profiles of alterations, including but not limited to, episodic memory components, structural and functional connectivity, metabolism, amyloid and tau deposition. A next step could also be to compare AD- versus alcohol-related minor neurocognitive disorders  across both cross-sectional and longitudinal studies to better understand the cascade of events governing the pathophysiological mechanisms that can potentially lead to AD and KS.Our findings provide new direct evidence regarding the specificities and commonalities in the pattern of volume deficits within the Papez circuit in AD and KS, two major neurocognitive disorders leading to amnesia. Our study fits into the broader consideration of the substrates of amnesia and challenges the relevance of considering amnesia in reference to classic descriptions as being the result of critical damage to one non-overlapping region rather than the aftermath of a cascade of pathophysiological events within the Papez circuit. These data reinforce the relevance of examining not only the hippocampus or the thalamus, but a network of connected brain regions as the substrate of memory functioning . From a


This dataset includes data on the embodied human appropriation of net primary production (eHANPP) associated with products derived from agriculture and forestry. The human appropriation of net primary production (HANPP) is an indicator of changes in the yearly availability of biomass energy from photosynthesis that remains available in terrestrial ecosystems after harvest, under current land use, compared to the net primary production of the potential natural vegetation. HANPP is an indicator of land-use intensity that is relevant for biodiversity and biogeochemical cycles. The eHANPP indicator allocates HANPP to products and allows tracing trade flows from origin (the country where production takes place) to consumption (the country where products are consumed), thereby underpinning research into the telecouplings in global land use. The datasets described in this article trace eHANPP associated with the bilateral trade flows between 222 countries. It covers 161 primary crops, 13 primary animal products and 4 primary forestry products, as well as the end uses of these products for the years 1986 to 2013. Specifications Table1•The dataset on embodied Human Appropriation of Net Primary Production (eHANPP) reveals pressures on biodiversity and ecosystems, including their carbon balance, embodied in global supply chains of agricultural and forestry products.•The data reveal trade flows between 222 countries, at the product level for 161 primary crops, 13 primary animal products and 4 primary forestry products, as well as the end uses of these products, for the years 1986 to 2013.•The dataset is useful for consumption or flow-based accounts of the pressure on ecosystems, or to study effects of international trade on ecosystems and biodiversity, such as the displacement of ecological pressure to other countries, the global reallocation of agricultural production, etc.•These data can be interesting for researchers, NGOs, consultancies or policy makers working on global supply chains and anthropogenic pressures on ecosystems.•The data can be used to visualize the evolution of pressure on ecosystems linked to specific supply chains, to determine which commodities and trade flows are causing most pressure on ecosystems. It can also be used to compare the pressure on ecosystems linked to different end uses as animal products, vegetal human food, or materials and energy.•Embodied HANPP Data are also of growing interest to quantify the overshoot of nations over planetary boundaries. See ,42For semantic explanations of the HANPP and eHANPP indicators, refer to .File 1 [embodied_HANPP_all_uses_no_ap_trade_cl_gl_resid_by_feed_products.csv]producing the animal product.Contains HANPP embodied in the bilateral trade of primary crop products (excluding the residues used as feed), roughage and residues used as feed, by end uses , between 1986 and 2013. The HANPP embodied in bilateral trade flows of animal feed corresponds to the total trade flow from the country producing the feed to the country File 2 [embodied_HANPP_all_uses_incl_ap_all_cl_gl_resid_by_animal_products.csv]consuming the animal product.Contains HANPP embodied in the bilateral trade of primary crop products, roughage and residues used as feed, combined to animal products, by end uses , between 1986 and 2013. Unlike in File 1, the HANPP embodied in bilateral trade flows of animal products corresponds to the total trade flow from the feed producing country to the country finally File 3 [trilateral_embodied_HANPP_cl_gl_resid_avg_2011_2013.csv]Contains HANPP embodied in the trilateral trade  of feed crops, roughage and residues used as feed combined to animal products. Because of the size of the data, we only display the average data over the period 2011-2013 . Each row represents the HANPP embodied in one combination of feed and animal product trade between a country producing feed, a country producing the animal product, and a country consuming the animal product .FILE_NAME4 [embodied_HANPP_forest_IRW_and_WF.csv]Contains HANPP embodied in the bilateral trade of 4 primary forestry products, between 1997 and 2013.3FAOSTAT Data were accessed using the fenixservices API on July 2021 (code from https://github.com/martinbruckner/fabio_v1). Required FAOSTAT Data are described in Countries that changed their border between 1986 and 2013 were the USSR, Belgium-Luxembourg, Yugoslavia, Serbia-Montenegro, Czechoslovakia, Ethiopia-Eritrea, Sudan. When factors were missing either for the subsequent countries, the factor of the former country was applied to the divided countries. When indices  were missing for the former aggregated country, the index from the largest of the divided countries was taken. Spatially explicit data were shared according to country areas.Calculation steps are summarized in 3.1The product level HANPP calculation is similar in most steps to previous HANPP assessments ,1,7, but3.1.1harv on cropland were similar to Kastner et al. [The calculations of HANPPr et al.  and Haber et al. , except We first converted crop production from fresh weight to dry matter using crop specific water content factors.We calculated used by-products (out of which some are used as feed) and unused residues with specific multipliers (harvest indices) for each world region , differeSome annual crops, called Total above-ground harv on cropland at the product level.The total above and below-ground of ∼0.87 . This yiWe calculated pre-harvest losses according to each country's degree of industrialization . For perharv and the NPPeco is the NPPact, at the product level.The sum of the HANPP3.1.2harv on grassland was calculated using the grazing gap method, i.e. by calculating grazed biomass as the residual feed requirement after subtracting market feed and crop residues used as feed The HANPPDaily feed  demand for milk cows and beef cattle were taken from the linear functions between milk (beef) yield and feed intake, from Krausmann et al. Because the distinction between meat cattle and milk cattle is not straightforward, the feed demand from cows and cattle were set to the maximum between the two values (milk and meat).Feed demand  from other ruminants and grazing animals as horses  was calculated with a demand per head factor . For sheFeed demand for monogastrics was calculated via world region specific feed demand per unit product factors .TotalfeeWe converted market feed from the commodity balances into dry matter, and allocated it in priority to monogastrics, as they cannot eat large quantities of roughage. Where market feed alone was not sufficient to cover the feed requirements of monogastrics, we assumed that the rest would have come from kitchen residues and other unformal sources, to which we do not allocate a HANPP rucksack.Market feed for ruminants was defined as the difference between total market feed (in dry matter) and the market feed allocated to monogastrics. For the grazing gap method, market feed for ruminants was multiplied by 1.5 to convert it into hay equivalents.Used crop residues were taken from the cropland calculation, and multiplied by a country specific factor of residues used as feed for cereals and other crops. Residues used as feed were downscaled when these exceeded the remaining feed requirement of ruminants.harv on grassland, was calculated as the feed requirement of ruminants, neither covered by market feed nor by crop residues. Note that because the FAO does not report roughage from fodder crops (green maize etc.), the latter are embedded in the HANPPharv on grassland.The total feed from grazing, i.e. HANPPPrevious calculation steps were similar to previous studies. For our purposes grazing has to be allocated to the various grazing animals. We hence calculated the shares of market feed, residues and roughage (grazing including roughage fodder crops) in the total feed demand of all ruminants.Where These shares are then multiplied to each ruminant's total feed demand. In other words, in each country, cattle, goats and sheep would be allocated the same share of market feed, crop residues and roughage.Where To retrieve the market feed in market feed equivalent, the market feed in hay equivalents must be divided by 1.5.The shares of each market feed product within the total market feed were as well attributed to all animals. Therefore, within their respective market feed, pigs get the same share of soy cake or brans as do cattle.Where 3.1.3We calculated the difference between the national cropland area under arable crops, and the total harvested area over all arable crops in each country.In countries and years where The multiple cropping factor is hence multiplied to the respective harvested area of each arable crop to obtain the physically cropped area.In countries and years where Unlike previous HANPP assessments, we had to distribute this fallow to each arable crop, and opted for distributing it according to their respective physically cropped area.3.1.4We first isolated the production of the four primary products, namely: industrial roundwood, coniferous; industrial roundwood, non-coniferous; Wood fuel, coniferous; Wood fuel, non-coniferous. Following previous HANPP assessments, wood volumes were converted in tonnes of dm biomass using the following wood density factors. We added the bark of the tree using bark factors, accounted for non-removed fellings with recovery rates, and for belowground NPP with a shoot over total ratio. For each of the primary products:3.1.5pot at half degree spatial resolution is provided yearly by the LPJmL dynamic global vegetation model . The mof et al. ), coverif et al. ), iii) sf et al. ).pot was attributed to countries and land cover types based on spatially explicit land cover data from Kastner et al., [pot data were then smoothed over the entire period, using a loess method, and set to 0 where the result was negative.The NPP et al., . Countrypot has to be allocated to products and animals. NPPpot on cropland was proportionally allocated to crop products based on their respective physically cropped area, including the fallow land. For permanent crops, the physical cropped area is the harvested area, and fallow land is null. The crop specific NPPpot was adjusted based on the location of the cultivars, with factors from [pot values.For the product level HANPP factors, NPPors from , which wors from . The totpot proportionally to the physically cropped area.In countries where factors from Kastner et al.  were mispot on fallow was isolated by multiplying crop and country specific NPPpot per hectare values, to the corresponding fallow area.NPPpot on grassland was first distributed to minor grazing animals  proportionally to their respective share in the total HANPPharv on grassland in each country.NPPpot, to consider that certain animals (often cattle) graze on more productive land than others (often sheep and goats). We calculated HANPPharv per head, and combined it to the dasymetric (DA) Gridded Livestock of the World (GLW) spatially explicit dataset from the FAO , for the year 2010, to globally map the HANPPharv on grassland for each animal for all years.For cattle and buffaloes, goats, horses, and sheep , we proceeded to a further adjustment of the NPPWithin each pixel:Where harv maps of all animals, we gridded the share of each animal's HANPPharv in total HANPPharv on grassland. We combined the gridded shares of HANPPharv to the gridded NPPpot from grassland of major grazing animals, and multiplied the NPPpot of grassland by each animal's share of HANPPharv from grazing within each pixel, to obtain the animal specific NPPpot.Summing the HANPPFor countries small countries that were not in the raster, we kept the proportionally allocated values .harv of wood products exceeded the NPPpot in forests. We assumed this is because wood products, especially wood fuels, may be harvested on other land cover types than forests, for example bush lands and savannahs. We hence adjusted the HANPPharv of wood products, to isolate the HANPPharv in forests only. We first removed the excessive HANPPharv from the HANPPharv of wood fuel (wf). In countries where this was still not sufficient, i.e. where the HANPPharv of industrial roundwood (ir) alone was above the NPPpot of forests, we removed the remaining excess from the HANPPharv of industrial roundwood.In some countries, the HANPPWhereharv of wood fuel.Is the share of coniferous and non-coniferous wood in the total HANPPharv of industrial roundwood.Where pot in forests proportionally to the HANPPharv in forest of the four primary wood products.We can now allocate the NPP3.1.6luc on fallow was calculated by multiplying the NPPpot on fallow with factors from [act on fallow is hence the difference between the NPPpot on fallow and the HANPPluc on fallow. The total NPPact on cropland is the sum of the NPPact cropland and the NPPact on fallow. HANPPluc on cropland is the difference between NPPpot on cropland and total NPPact on cropland. The crop specific HANPP on cropland is the sum of the crop specific HANPPharv and HANPPluc on cropland.HANPPors from . NPPact luc per NPPpot on grassland were taken from [pot values. The animal specific HANPP on grassland is the sum of the animal specific HANPPharv and HANPPluc on grassland. NPPact on grassland is the NPPpot minus HANPPluc on grassland. NPPeco on grassland is the NPPact minus HANPPharv on grassland. Where the NPPeco on grassland was below 5% of the NPPact, we adjusted the NPPact upwards (and therefore the HANPPluc downwards) as to have a NPPeco of minimum 5% of NPPact, while keeping the HANPPharv constant. This can be thought of as increasing grassland productivity through fertilization, as it is done in several intensive grazing systems .Country specific average HANPPken from , and mulFor all animals, countries and years where luc to crop residues used as feed. Therefore, the only HANPP allocated to crop residues used as feed was the mass of these residues (HANPPharv). This comes from the assumption that the land use change occurred primarily for the main product of the corresponding crop (for example wheat), and not for its by-product (straw).We did not allocate any HANPPluc is set to 0. NPPact is hence equal to NPPpot and NPPeco equals NPPpot minus HANPPharv,forest (i.e. excluding the HANPPharv exceeding the NPPpot in forest), all at the primary product level.For forestry products, the HANPP3.1.7harv and the HANPP on cropland, and dividing the respective value by the crop production quantity (in dry matter).For cropland, we calculated the factors per tonne of crop by first removing the residues used as feed from the HANPPFor grassland and crop residues used as feed, the allocation to animal products requires one further step, for grazing animals, namely the attribution to milk or meat . For grazing animals, we allocate HANPP values according to the number of heads reported for the production of milk or meat respectively.Where For horses, asses, mules, and camels, we did not allocate the HANPP to the reported milk and meat, as we assumed that these animals are mainly kept for services and leisure, and we do not know how many animals are grown exclusively for products.We finally calculate NPP and HANPP values per tonne of product, by dividing by the production quantities of the respective animal products.pot in forest, which are saved separately.For wood products, factors per tonne of product were calculated by dividing NPP and HANPP values by the production of wood in dry matter. Note that these values do not include the HANPP exceed the NPPhttps://ijsadihsadoaisjd.shinyapps.io/shiny_app_hanpp_all_lu_types/This finishes the HANPP calculation at the product level. Product level HANPP data can be visualized interactively and downloaded under 3.23.2.1The calculation of the trilateral trade data relies on the same procedure as the one described in . In this283 traded vegetal products were converted to 159 primary crops, based on their dry matter content. Similarly, bilateral trade matrices were built for 66 traded animal products, converted to 13 primary animal products.Where Special treatment was given to sugar crops, often reported as “sugar refined”, which have to be split into sugar beet and sugar cane. We hence calculated the available supply of sugar beet and cane in each country, by correcting a first time for reexports, as described in , solely We could then correct for reexports for all primary crops as described on . The difDespite the reported milk and meat products for Asses, Camels, Horses, Mules, we decided to neglect the production and international trade of these products, given that the large share of these animals is not used for their products. For examples, horses are likely rather used for horse riding than for horse meat. We hence considered that all the footprint of these animals was domestic.For wood products, we followed the methodology of . This me3.2.2In order to calculate the matrix of livestock product trade in feed crop equivalents Animal products used as feed  were neglected from this step. Consequently, any animal product used as feed was dealt with as if it would have been finally consumed in the country where it was used to feed other animals, hence underestimating the international footprint of animal products. Hereafter, we call 3.3We linked the trade data of crops to their final uses , adjusting for seed use and losses, based on the commodity balances (CBS) from the FAO. Again, the first step was to allocate all products from the commodity balances to primary crops according to apparent consumption, using the method described earlier (for the feed products). Sugar refined was again replaced by sugar cane and sugar beet.For example, if 10 tonnes of brans are used as feed in a country, and that country apparently consumes 30 tonnes of wheat and 70 tonnes of maize, and that apart from brans 60 tonnes of maize are used for animal feed, then Trade matrices are to be adjusted to seed use, as the consumption of a crop in a given country should include the quantity of seeds globally used to produce that crop, but exclude the amount of that crop which this country is using for its own seeds. We adjusted trade matrices for seed use, by adding seeds to the production (and exports), and eventually removing them from the consumption (and imports). Ideally, one should add seeds from the previous year to the production, and remove seeds from that year from the consumption (as these are to be used the following year). However, for calculation ease  we decided to assume that all seeds were used the same year. The adjustment for seeds does hence not affect the global quantity of crops, but solely the trade patterns.Call Call Then we calculated the trade matrix adjusted for seeds:We allocated losses to feed, food and others . We calculated a loss adjustment factor in each country for each crop, and applied this factor to the quantity of feed, food and other uses.We eventually calculated the share (including losses) of the final end uses feed, food and other uses.By multiplying the values of  We calculated We omitted the differences in final uses of animal products (e.g. leather).3.4Then We calculated 3.5harv, HANPPluc or physically cropped area) embodied in the final use of crops for food and other uses, as well as for animal products production and final consumption, by multiplying the HANPP factors calculated in step 1.7, to the corresponding trade matrices.For eHANPP from cropland and forestry products, we calculated the HANPP  and roughage (grass and fodder crops). The HANPP embodied in the production of animal products was hence set to the HANPP calculated in step 1. As explained earlier, we as well set the HANPP embodied in the “final consumption” of asses, camels, horses and mules equal to their HANPP in that given country, reflecting the idea that most of the feed and grazing of these animals is not meant to produce meat and milk, but rather to provide services  enjoyed domestically.https://doi.org/10.5281/zenodo.8384359Data handling were performed using the R software. The calculation explained above, combining raw data and factors from various sources, makes this dataset unique. All code can be found attached to this article. The final eHANPP data files are publicly available under 4https://doi.org/10.1016/j.scitotenv.2022.158198.See section 4.3. “Limitations” in the related research article  https://the authors have read and follow the ethical requirements for publication in Data in Brief and confirming that the current work does not involve human subjects, animal experiments, or any data collected from social media platforms.Nicolas Roux: Conceptualization, Methodology, Writing – original draft. Lisa Kaufmann: Conceptualization, Methodology, Writing – review & editing, Supervision. Sarah Matej: Conceptualization, Writing – review & editing. Thomas Kastner: Methodology, Writing – review & editing. Alberte Bondeau: Formal analysis, Writing – review & editing. Helmut Haberl: Conceptualization, Writing – review & editing, Supervision, Project administration. Karlheinz Erb: Conceptualization, Writing – review & editing, Supervision.


Predicting evolution is a fundamental problem in biology with practical implications for treating antimicrobial resistance, which is a complex system-level phenomenon. In this perspective article, we explore the limits of predicting antimicrobial resistance evolution, quantitatively define the predictability and repeatability of microevolutionary processes, and speculate on how these quantities vary across temporal, biological, and complexity scales. The opportunities and challenges for predicting antimicrobial resistance in the context of systems biology are also discussed. Based on recent research, we conclude that the evolution of antimicrobial resistance can be predicted using a systems biology approach integrating quantitative models with multiscale data from microbial evolution experiments. The rapevolutionary predictability and evolutionary repeatability mean in the context of AMR evolution and which aspects of resistance can be forecasted for microbes? These questions are challenging given the multiple scales at which AMR occurs, ranging from the rapid emergence of resistance mutations inside of cells7 to the longer-term establishment of complex resistance-conferring dynamics among microbial communities2, along with the fact that AMR is a complex system-level evolutionary process8. Next, can we predict evolutionary paths (trajectories) or are we restricted to evolutionary endpoints (outcomes)? Distinguishing between trajectories and outcomes is essential since evolutionary processes can follow different trajectories to arrive at the same evolutionary outcome or they can take similar trajectories but diverge to yield different evolutionary outcomes9. Last, which variables are important to develop predictive models of microevolution? This is critical for model development as well as the design of microbial evolution experiments, which can generate high-dimensional data  or low-dimensional data .There is a pressing need to develop a predictive theory of AMR evolution to answer numerous outstanding questions. First, what do Throughout this article, we tackle the questions posed above and explore the challenges and opportunities for predicting AMR evolution from a systems biology perspective, which incorporates the complex interactions between different scales and components of evolving microbial systems. We conclude that the evolution of AMR can be predicted  using quantitative systems-based models informed by multiscale data from high-replicate or high-temporal resolution experiments on evolving microbial systems.Box 1 TerminologyAntimicrobial Resistance (AMR): AMR arises when bacteria, fungi, viruses, parasites, or other microbes no longer respond to antimicrobial drugs.Clade: A group of genetically related organisms that evolved from a common ancestor.Clonal Interference: Competition between beneficial mutations in different lineages with fixed genetic backgrounds in asexual populations.Deterministic: Describes a process that evolves along the same trajectory to the same future state from a given initial state.Epistasis: The phenotypic expression of a genetic mutation/gene is dependent on the expression of one or more other mutations/genes.Evolutionary Predictability: A statement about the evolutionary trajectory/future state of a biological system, defined by the existence of a probability distribution .Evolutionary Repeatability: How likely a particular evolutionary event is to repeatedly occur in a biological system, defined by the likelihood, entropy, or other measures of variability .Fitness: The reproductive success of an organism. Growth rates are often used as a proxy for fitness in microbes.Fitness Landscape: A visual representation of the relationship between genotype and fitness.Genetic AMR: Drug resistance that is mediated by genetic mutations or through the acquisition of drug resistance genes.Genetic Drift: The change in gene variant  frequency in a population due to random chance.Macroevolution: Large evolutionary changes such as the emergence of a novel pathogen over long timescales.Microevolution: Change in allele frequency over a short timescale (as compared to macroevolution).Nongenetic AMR: Drug resistance that arises among genetically identical microbes exposed to the same drug, which is not the result of genetic AMR.Stochastic: Describes a process that randomly evolves along one of many possible trajectories to one of many possible future states from a given initial state.2. We propose that evolving biological microbial systems be quantified based on their evolutionary predictability and their evolutionary repeatability. The predictability of an evolutionary process is ultimately a probabilistic statement about a biological system (or ensemble), which can be defined by the existence of a probability distribution  will be easily repeated or realized in an evolution experiment or during patient treatment. An analogy can be drawn to picking cards from a deck of playing cards; while the distribution of outcomes is predictable, picking a specific card is not easily repeatable.Researchers lack a shared language to describe evolutionion Fig. . If a pr1213 and larger selection pressures are known to generate more repeatable evolution15. While AMR repeatability has been observed for some large-effect sequence changes18, it has been more broadly observed for molecular resistance phenotypes20.Evolutionary repeatability is related to the likelihood of occurrence of individual events that constitute a statistical ensemble. Repeatability can be quantified using measures from statistical physics such as entropy Fig. . If an eopy Fig. , a Diracopy Fig. , and othopy Fig.  in prediopy Fig. . AMR evogenetic drift21. This requires that a stochastic framework22 be applied to analyze and make predictions based on data obtained from measurements of biological quantities  at low concentrations during early stages or at late stages (if drug treatment is effective) of microbial evolution experiments and infections; genetic drift also plays a confounding role in this regime9. Recently, stochastic systems-based population models were proposed to guide drug therapies by providing predictions on resistance mutation appearance probabilities and first-appearance times, indicating timescales for substituting or combining antimicrobial drug during patient treatment23. Random genetic drift distorts the impact of selective forces and decreases the ability of models to predict evolution2.There are fundamental and practical limitations to predicting AMR evolution Fig. . Predict24 and genetic drift25. The interplay between nongenetic AMR and genetic AMR is not fully understood26, and such knowledge gaps negatively impact our ability to predict resistance evolution. It has been hypothesized that nongenetic AMR facilitates evolution by allowing a fraction of the cell population to survive and subsequently evolve genetic resistance to antimicrobial treatment27. Systems-based stochastic population dynamics modeling has shed light on the nongenetic-genetic AMR enigma by incorporating resource competition between nongenetically resistant and genetically resistant subpopulations; these models predict that nongenetic resistance facilitates survival but slows down genetic AMR evolution23. Similar models can be inferred from coupled stochastic trajectories of gene expression and cell division28. The architecture of systems of interconnected genes comprising genetic networks modulates the amplitude and timescale of gene expression fluctuations, which enhances acute, reversible, nongenetic drug resistance30 and facilitates prolonged, nonreversible, genetic drug resistance26.Other sources of biological stochasticity also pose a challenge for predicting the evolutionary path to resistance, including gene expression variability among genetically identical cellsEpistasis arises from a non-additive interaction between mutations that affect fitness31. Mutations that arise early in the evolution of AMR affect which mutations are subsequently selected32. Epistatic interactions generate a nonlinear fitness landscape33. This occurs because whether a mutation is beneficial, detrimental, or neutral in terms of selection depends on the genetic, extracellular, and environmental background34. Beneficial mutations may synergistically interact to decrease selective pressure and render evolution slower and more stochastic (since weaker selection is less deterministic), thus reducing repeatability. For instance, beneficial mutations in disjoint clades compete for fixation, whereas mutations in nested clades reinforce one another in clonal interference, a phenomenon that arises in large asexual microbial populations subject to strong selective pressures such as antimicrobial drug therapy35. Clonal interference may enhance predictability by reducing stochastic waiting times for fitter genetic variants and by ensuring that beneficial mutations that fix have a large fitness effect and are driven by natural selection, not by genetic drift or environmental noise1. Clonal interference was found to be pervasive in an experimental model of E. coli in the mammalian intestine, and the targets of natural selection were similar in independently evolving bacterial populations, resulting in similar early-stage phenotypic changes36. Another fundamental challenge in predicting AMR evolution lies in the unknowns, complexities, and redundancies in mapping genotype to phenotype. Our ability to predict AMR evolution on fitness landscapes with considerable epistasis is highly dependent on the precision of the initial conditions data9. Epistasis can make predictive modeling more challenging, as the order of and interactions between mutations must be accounted for as well as the fact that epistasis facilitates the evolution of novel functions37; on the other hand, epistasis can constrain evolutionary trajectories to a given endpoint thus potentially increasing repeatability. Despite these challenges, metabolic fitness landscapes have been utilized to predict antibiotic resistance38. Data limitations further hinder our ability to predict resistance evolution, such as uncertainty in initial conditions for chaotic environmental systems in which microorganisms evolve39, even for evolution driven by deterministic natural selection9.The interactions and competition among mutations, along with data limitations, are other important considerations for predicting AMR evolution. 40. Others have argued that physics can be used to predict evolution on shorter timescales , there exists a characteristic timescale for predicting evolving biological systems . Additionally, patients respond to an infection or vaccination by different immune receptor sequences48. However, even at the genetic scale notable examples exist where microevolution is highly repeatable, such as the establishment of the same mutation due to extensive standing variation49 or strong selective pressure50.Evolutionary predictability and repeatability vary across temporal, biological, and complexity scales Fig. . Evoluti1. On the other hand, AMR evolution is more predictable than general genome evolution due to the finite number of AMR mutations and resistance mechanisms51. Phenotype space  is also more predictable, as different mutations can have similar effects on fitness . However, analyzing low-dimensional traits may be less informative than high-dimension traits, as low-dimensional traits are expected to yield a lower information gain relative to a prior, which can be quantified as the log ratio of predicted probability (q(t)) to prior probability (p(t)): 57, affect the predictability of AMR evolution. As the complexity of ecological interactions increase, adaptive tradeoffs to multiple selective pressures reduce our ability to predict evolution59. Though ecological-evolutionary feedbacks can increase our ability to predict evolution through negative frequency-dependent selection60.An important consideration for predicting AMR evolution is that pathogens evolve within large, highly complex and interconnected ecosystems that are themselves evolving61. However, at present, machine learning models largely function as context-specific “black boxes” that do not elucidate the mechanisms underlying AMR evolution51, though this situation may change depending on progress made in of the field of explainable artificial intelligence62. In contrast, quantitative systems-based models coupled with evolution experiments on pathogens with well-cataloged resistance mutations would serve as powerful tools for elucidating the mechanisms underlying AMR evolution. It remains to be seen if quantitative systems-based modeling will possess the same predictive power as machine learning.Machine learning is not yet prevalent in evolutionary studies, although it shows promise for predicting AMR. For instance, machine learning models have been trained to predict the evolutionary success of complex systems, such as the human influenza virus variants64. This approach may prove valuable in identifying the characteristic time for predicting evolution  and analyze a large collection of endpoint measurements65. Alternatively, researchers could perform in silico or in vitro/in vivo evolution experiments with a small number of replicates and track the evolutionary dynamics at a higher temporal resolution. Ideally, these experiments would measure evolutionary trajectories as well as endpoints at multiple scales . Another promising approach is to use DNA barcoding, which can detect mutations at low frequencies in cellular lineages and estimate their time of occurrence as well as fitness effects70. DNA barcoding is particularly helpful at the beginning of microbial evolution experiments when many mutations are present at low frequencies, in contrast to whole genome sequencing which is beneficial at the end of microbial evolution experiments when few resistance mutants have fixed in the population. Genome editing and deep mutational scanning open the possibility of quantifying the number AMR mutations and their individual fitness effects to predict drug resistance71 and guide drug development72. The systems biology approach of combining different levels of omics technologies with genome-scale metabolic models can provide precision and robustness to AMR predictions8. Finally, systems-based epidemiological models of AMR evolution that include the structure of the host population, interactions between genetic loci, and integration of within- and between-host levels can predict strategies to limit the spread of drug-resistant pathogens73.A promising avenue is to take a systems-based approach to predict AMR evolution based on properties that are not exhibited by individual resistance genes or microbial species, such as the emergent properties that are associated with interacting genes or microbes64, which is related to the entropy of the system74. The predictability of microevolutionary processes such as AMR increases on short timescales41, whereas macroevolution, such as the emergence of a new pathogen, takes place on timescales well beyond the characteristic timescale for predicting evolution. AMR evolution is anticipated to be more predictable but less repeatable at larger biological scales, while at small biological scales it is less predictable but more repeatable. The complexity of biological systems presents practical challenges for making predictions but is also imposes constraints that can increase predictability. In particular, constraints arising from competing infectious agents23 or from drug combinations75 may aid the prediction of resistance evolution.Microbial evolution spans temporal, biological, and complexity scales, which differentially impact our ability to predict AMR. We can quantitatively define evolutionary predictability in terms of our ability to define probability distributions and evolutionary repeatability in terms of likelihood or entropy. Evolutionary predictability and repeatability can also be thought of in terms of information gain relative to a prior76, which incorporates dynamics across multiple scales ranging from the interactions between genes in a genetic drug resistance network30 to species interactions in microbial communities9. The application of machine learning is another approach which promises to play an increasingly important role in drug design, patient diagnostics, and predicting AMR77. Ultimately, to predict AMR evolution we need to overcome two challenges: (1) develop quantitative, multiscale models of complex fast-resistance evolving biological systems and (2) generate high-replicate data that span time, space, and biological organization from systems-based evolution experiments to inform and test predictive models. Overcoming these challenges will enable clinicians to predict AMR evolution, revolutionizing the treatment of patients with drug-resistant infections.Predicting AMR evolution is an interdisciplinary endeavor that will require combining multiple fields including physics, mathematics, computer science, and the biological sciences to achieve breakthroughs. One promising strategy is to develop predictive models of evolution using a systems-based approachFurther information on research design is available in the Reporting Summary