Oceanic Transform Faults are major plate boundaries representing the most seismogenic part of the mid ocean ridge system. Nonetheless, their structure and deformation mechanisms at depth are largely unknown due to rare exposures of deep sections. Here we study the mineral fabric of deformed mantle peridotites - ultramafic mylonites - collected from the transpressive Atobá ridge, along the northern fault of the St. Paul transform system in the Equatorial Atlantic Ocean. We show that, at pressure and temperature conditions of the lower oceanic lithosphere, the dominant deformation mechanism is fluid-assisted dissolution-precipitation creep. Grain size reduction during deformation is enhanced by dissolution of coarser pyroxene grains in presence of fluid and contextual precipitation of small interstitial ones, leading to strain localization at lower stresses than dislocation creep. This mechanism potentially represents the dominant weakening factor in the oceanic lithosphere and a main driver for the onset and maintenance of oceanic transform faults. Deformed mantle rocks exhumed by oceanic transform faults recorded fluid-assisted ductile deformation at high temperatures and pressures, corresponding to the root of the fault. This deformation mechanism controls deep faulting at plate boundaries. M > 7), globally releasing 10 to 20 times more seismic energy than that released at the spreading sections of the MOR2. The downward extent of seismicity along the OTFs is an indirect proxy for the thermal regime constraining the depth of the brittle lithosphere3. The Brittle-Ductile Transition (BDT) in the oceanic lithosphere is assumed to correspond to the 600 °C isotherm under normal stress conditions4. Yet, recent petrological and seismic studies on peridotite mylonites from OTFs showed that brittle deformation can locally extend to temperatures as high as 1000 °C at peak stress conditions8.Oceanic Transform Faults (OTFs) are strike-slip boundaries sub-parallel to the plate spreading direction that offset Mid Ocean Ridges (MORs). Their cumulate length represents more than 40% of the global MOR system. Strike-slip motion along these faults generates large earthquakes  and high temperature (>750 °C) generates mylonites and ultramylonites, i.e. strongly foliated, very finely grained metamorphic rocks14. This process often marks a transition in the creep mechanism from dislocation to diffusion, grain size sensitive creep15. Fluid circulation may drive grain size reduction, as reported in deformed rocks experiencing melt-rock reactions18, fluid addition20, or metamorphic equilibration to new P-T conditions22. In all these studies, fluid-assisted grain size reduction leads to very fine-grained well mixed polymineralic assemblages.Grain size reduction during mylonitization results in significant decrease of rock viscosity, enabling permanent weakening at large scale and strain localization at depthsIn this work we present new petrological and microstructural data of peridotite mylonites and ultramylonites from the Atobá Ridge, in the St. Paul Transform System Fig. . We show24. Four active OTFs oriented E-W dissect the MAR axis in three short intra-transform ridge segments25  to >90% in the ultramylonites, the rest being porphyroclasts . Atobá ridge mylonites and ultramylonites are interpreted as deriving from a peridotitic protolith that experienced high temperature/high pressure ductile deformation then exhumed by transpressive thrusting along a positive flower structure31. Hence, the structure of the Atobá ridge represents a tectonic window, giving a unique opportunity to access deep-seated rocks from the roots of the fracture zone.The St. Paul Transform System (TS) is one of the major structures of the global oceanic ridge system, located at 1°N in the Equatorial Atlantic, and offsetting the Mid-Atlantic Ridge (MAR) by about 630 kms25 Fig. . The norets Fig. . The isle26 Fig.  are mostHereafter, we characterize the microfabric of fresh mylonites  and polymineralic domains with porphyroclastic to porphyroblastic textures Fig. . These t cm thickInternal misorientations also differ in olivine porphyroclasts from the two domains. Olivine porphyroclasts from olivine-rich domains have tightly spaced subgrain boundaries forming orthogonal subgrain cells Figs. D and 4A,The most extremely sheared rocks, the ultramylonites, also show interlayering of olivine-rich (>95% olivine) and polymineralic domains Fig. . The finJ textural and M misorientation indexes  to the CPO of a single crystal .Mineral textures in mylonites and ultramylonites have been investigated by EBSD. The Crystallographic Preferred Orientation (CPO) of olivine and orthopyroxene are presented by orientation maps, stereographic projections and density contours of the distribution Figs. . The stres Figs. . Both inOl axis underline the lineation  correlated to olivine CPOs, with [001]Opx axis subparallel to the lineation and to [100]Ol, and [100]Opx at high-angle to [100]Ol [001]32 , characterized by [001]Opx maxima perpendicular to the lineation and to [100]Ol axis, and [100]Opx axis at small angle to [100]Ol axis  and weaker point concentrations or girdles on [010] and [001] axes at high angle to the shear direction Fig. . These pion Fig. . Olivine]Ol Fig. . This sl]32 Fig. . In all xis Fig. . In all Ol Figs. .31 . The BA-index quantifies the CPO symmetry according to [100] and [010] intensities, which is relevant for olivine as its preferred direction is [100] Fig. . This in31 Figs. D and 6C.M values: 0.25–0.46) to moderate in polymineralic domains  and BA values  orthorhombic symmetries in neoblasts , do not completely randomize and are even stronger than olivine CPOs from the same polymineralic domains  gives average yield stress estimates of 87–93 MPa . Both thermometers have an estimated uncertainty of ±20 °C35. The widespread presence of spinel and the absence of plagioclase in the deformed mineral assemblage suggest that the deformation must have occurred at spinel facies conditions. We then calculate the TCa-in-Opx at nominal pressures of 0.6 GPa (minimum pressure) that represents the spinel to plagioclase boundary transition for depleted lherzolite36. Minimum deformation temperatures calculated in orthopyroxene porphyroclasts average 804 °C and 852 °C using TCa-in-Opx33 and TAl-Cr-in-Opx34, respectively (Table TCa-in-Opx) and 735 °C (TAl-Cr-in-Opx) (Table TCa-in-Opx) and 867 °C (TAl-Cr-in-Opx) for the orthopyroxene porphyroclasts, and temperatures of 768 °C (TCa-in-Opx) and 740 °C (TAl-Cr-in-Opx) for orthopyroxene neoblasts  Table , suggestts Table . These tDeformed peridotites from the Atobá ridge record strong viscoplastic deformation of primary minerals , resulting in extreme grain size reduction. The deformation mechanisms are discussed below.37 with dry to low water content within olivine crystals39. In olivine-rich domains, the presence of an orthogonal network of subgrain boundaries in olivine porphyroclasts [100] and (001)[100] slip systems Fig. . These sts Figs. , the simts Figs. , the occts Figs.  and the ts Figs.  point toins Fig. . This diins Fig. , the decins Fig.  and of tins Fig. , suggest19. Yet, the cusp-shapes of olivine grains , following the relation: Opx I + Cpx I + Ol I + fluid → Opx II + Amp + Ol II ± Cpx II. Accordingly, the composition of the orthopyroxene neoblasts shifts to lower Al and Ca contents compared to the porphyroclast composition  in St. Paul mylonites crystallized after seawater-derived fluids and not from magma-driven metasomatism reported elsewhere in the St. Paul region25.In both mylonites and ultramylonites, the synkinematic presence of fluid in the system is attested by the nucleation of high temperature (>800 °C)ts Figs. , and comts Figs. . Such ters Table . In addi44. This process, in turn favors fluid-assisted reactions and formation of an interconnected network45, leading to olivine weakening, modification of the olivine dominant slip system and mantle viscosity reduction50. The introduction of small quantities of hydrous fluid/melt at depths would also enhance creep rates50, favouring grain size reduction and progressive transition to grain size sensitive creep, and further strain localization48. Fluids are therefore key to localize strain at depths and can potentially induce a major rheological weakening of the lithospheric mantle50. We hence propose that the diffuse grain size reduction observed in St. Paul mylonites results from a combination of deformation mechanisms depending on the initial mineralogy. Dislocation creep is dominant in olivine-rich domains, while fluid-assisted dissolution-precipitation creep is the main mechanism in polymineralic domains  in the peridotite protolith are enhanced during localization of deformation; they can promote micro-scale rheological contrasts, such as between orthopyroxene and olivine, leading to local stress concentrations and strain focusing, thus promoting local grain size reduction by dislocation-accommodated mechanismsins Fig. . These d51, and more rarely the orthopyroxene CPO mimicking that of the olivine51, have been reported in other transform domains along the MOR system. However, all these observations have not previously been interpreted as pertaining to fluid-assisted dissolution-precipitation creep, which could represent the leading mechanism during localization of deformation at OTFs. Yet, recent petrological studies of various OTFs mylonites, such as those from the Shaka and Garret Fracture Zones12, support this assumption by showing positive feedback loop in the brittle-ductile transition zone between seismic-driven fluid infiltration, weakening, grain size reduction, shear and fluids.The petrographic microstructural characters described here, i.e. the extensive grain size reduction, the interlayered olivine-rich and polymineralic domains, the occurrence of syn-deformational high-temperature hydrous phases41, oceanic crust metamorphosed at HT conditions53, mantle and ophiolites43. Recently, oceanic detachment faults in magma-starved contexts also evidenced limited fluid-assisted deformation at the root zone of the detachment54. We therefore suggest that fluid-assisted dissolution-precipitation creep could be a major rheological process controlling strain localization and faulting at plate boundaries.The importance of high temperature fluid-assisted dissolution-precipitation creep as an effective deformation process in the upper mantle is already known for subcontinental crustFoliation and lineation of selected samples are based on the shape preferred orientation (SPO) of pyroxene and spinel grains; thin sections were cut perpendicular to the foliation and parallel to lineation, when visible (XZ plane). All thin sections were polished using a Vibromet Buehler with a colloidal silica solution during 1H15 to improve the surface of the thin sections for an optimal microstructural and microprobe analysis.2) at IFREMER .Scanning electron microscopy (SEM) imaging and analysis were done at 20 kV with a FEI Quanta 200 SEM coupled with an OXFORD X-MAXN Silicon Drift Detector  of olivine, pyroxenes, spinel, and amphibole was measured in 8 selected samples using a JEOL JSM-7100F TTLS LV at the Centre de Microcaractérisation Raimond Castaing located in Toulouse, France. EBSD maps were recorded with 20 kV acceleration voltage and 15 mm working distance. For each thin section, high-resolution CPO maps were obtained for polymineralic and olivine-rich domains, with a grid step from 0.2 to 8 μm, depending on the observed minimum grain size (Supplementary Table http://mtex-toolbox.github.io/)56. The orientation distribution functions (ODFs) were calculated using a “de la Vallée Poussin” kernel function with a half-width of 15°. All CPOs are presented as one crystallographic orientation per grain in pole figures  and Misorientation relative to the Mean Orientation of the grain (Mis2Mean) maps. Except for one sample (COL-DR01-18), small grains are well defined, and their grain size and shape distributions were quantified using MTEX )−(G100/(G100 + P100)))59. In the case of olivine, the values varies between 0 and 1, where BA values < 0.35 represent an axial-[010] fabric, BA values between 0.35 and 0.65 an orthorhombic fabric and BA > 0.65 corresponds to an [100]-axial fabric.MTEX was also used to calculate fabric strengths using the TCa-in-Opx34 and TAl-Cr-in-Opx35. Both thermometers have an estimated uncertainty of ±20 °C given by the authors. For equilibrium temperatures estimated with TCa-in-Opx, we chose the pressure of 0.6 GPa, which is the minimum pressure at which the spinel to plagioclase transition occurs36. As the recrystallized assemblage does not contain any plagioclase, the deformation must have occurred at depths corresponding to pressures >0.6 GPa. The equilibrium temperatures calculated with the above geothermometers therefore correspond to the minimum deformation at which grain size reduction occurs in St. Paul mylonites and ultramylonites.In situ major element concentrations of pyroxenes and amphiboles were measured using a JEOL JXA-8200 electron microprobe at Dipartimento di Scienze della Terra, Università degli Studi di Milano . The accelerating voltage was fixed at 15 kV and beam current at 15 nA. The spot size was 1 μm. Counting time was 30 s on the peak and 10 s on the backgrounds. A set of natural standards was used for calibration. Amphibole and pyroxenes compositions are reported in Supplementary Tables Composition maps Fig.  were acqSupplementary InformationDescription of Additional Supplementary FilesSupplementary Dataset


Escherichia coli has been extensively studied, including its response to oxygen availability. The ArcA/B two-component system (TCS) is the key regulator for the transition between these two environmental conditions and has been thoroughly characterized using genetic and biochemical approaches. Still, to date, limited structural data is available. The breakthrough provided by AlphaFold2 in 2021 has brought a reliable tool to the scientific community for assessing the structural features of complex proteins. In this report, we analyzed the structural aspects of the ArcA/B TCS using AlphaFold2 models. The models are consistent with the experimentally determined structures of ArcB kinase. The predicted structure of the dimeric form of ArcB is consistent with the extensive genetic and biochemical data available regarding mechanistic signal perception and regulation. The predicted interaction of the dimeric form of ArcB with its cognate response regulator (ArcA) is also consistent with both the forward and reverse phosphotransfer mechanisms. The ArcB model was used to detect putative binding cavities to anaerobic metabolites, encouraging testing of these predictions experimentally. Finally, the highly accurate models of other ArcB homologs suggest that different experimental approaches are needed to determine signal perception in kinases lacking the PAS domain. Overall, ArcB is a kinase with features that need further testing, especially in determining its crystal structure under different conditions.The complex metabolism of  Escherichia coli is a versatile organism that quickly adapts to changing environments, especially transitioning from aerobic to microaerophilic or anaerobic conditions. Its ability to use a variety of electron donors and acceptors is essential for its survival  are a widely conserved signaling mechanism in bacteria that allow them to sense and respond to environmental changes. Phosphorylation is the most prevalent protein modification . More thSalmonella enterica is highly conserved in Gram-negative bacteria and plays a crucial role in virulence by regulating gene expression necessary for survival within host cells  has been identified as the key regulator mechanism of the kinase activity  analysis identified two putative regulatory cavities for binding anaerobic metabolites, which have been shown to regulate kinase activity. The data from these models have also been correlated with extensive biochemical and genetic evidence of the phosphotransfer reactions. Additionally, the structural features of different homologs of ArcB suggest that the signal detection mechanism in kinases lacking the PAS domain is more complex than previously thought and requires further experimental analysis.The AlphaFold2 models in this study have provided valuable insights into the features of the ArcB sensor kinase. Protein models enable researchers to develop new hypotheses that can be confirmed by site-directed mutagenesis experiments, in this case, to further study ArcB The overall strategy to analyze the predicted structure of the ArcB/A TCS and the tools used for each task in this work are summarized in https://alphafold.ebi.ac.uk/) or generated in the collaborative resource on line (https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb)  as PDB files, and the PDB ID numbers are indicated on the figure legends.Protein models were either downloaded from the AlphaFold database hosted at EBI (2.ipynb) , without2.ipynb) . All the2.ipynb) . Chimerath PyMol . ReferenArcB protein and ligand binding prediction was conducted with CB-Dock , a cavitE. coli   , and litin (Hpt) .292). The primary receiver domain (or D1) is located between residues 520 to 640, where the conserved Asp residue is situated (576). The phosphotransfer domain (Hpt) is in residues 640 to 778, with a conserved histidine residue (717) located within this region (The ArcB kinase is comprised of a short cytoplasmic domain (residues 1 to 22), two transmembrane helices (TM) (residues 22 to 41 for TM helix 1 and 58 to 77 for TM helix 2), a short periplasmic domain between the two transmembrane domains (residues 42 to 57), and a PAS domain (residues 177–267), where the two cysteine residues are located (180 and 241). The catalytic domain is located between residues 290 to 520 (primary transmitter domain or H1), where the ATP binding box is located (spanning residues 399 to 473) and the primary transmitter residue , which is in the second TM helix . In 180 residue is identical in both predicted structures. The resulting null phenotype found by Previously, 180 pairs in the kinase dimer, except functional analysis using a hybrid kinase with the membrane domain of the Tar chemoreceptor showing that the kinase has a rotational on/off mechanism , with the two cysteine residues pointing apart. The structural model suggests that at least the rotation must bring up to 2–4 Å apart each monomer to form the disulfide bond between the two Cys180. Once assembled, this will bring the two catalytic domains apart and stimulate the phosphatase activity  and Tyr170 (magenta) are highlighted in the dimeric structure of ArcB, indicating that the Tyr170 residue delimits the orientation of the disulfide bond formation and the stability since its located towards the Cys180 residue on each ArcB monomer. Phe177 may have a lesser role since its orientation is farther apart from the cysteine residue  performing mutagenesis targeting the 78–778 region of the kinase that demonstrates quinone-mediated regulation in vitro. Directing mutations to the PAS domain may uncover further evidence favoring or discarding this analysis and ultimately showing the regulation exerted by these anaerobic metabolites.With the above data, then the binding of anaerobic metabolites was explored. Caution must be taken since the binding of quinone molecules was predicted to bind in the catalytic and PAS domains. In the case of anaerobic metabolites, one possibility is that the metabolite D-lactate is sensed in the catalytic domain since it has been shown to enhance the kinase activity both  in vivo . CB-Dock in vivo . However in vivo . In the When the same analysis is conducted with L-lactate and other anaerobic metabolites  and S5, As shown in The calculation of the overall characteristics of each metabolite is a useful approach to determining why the binding is probable in the molecules tested. In 180 allosterically, like the experimental evidence shown for D-lactate  towards D1. It is more flexible in the 290–778 kinase fragment . Using the model presented in fragment . In Fig.576 residue resides and meets the opposite on each monomer. The Hpt domain can move towards this residue of the opposing subunit due to a flexible connector, consistent with the biochemical findings of 180 facing in the opposite direction, consistent with the active form of the kinase. One remaining question is whether ArcA, once bound, the phosphotransfer reaction occurs directly and the kinase is already locked in the conformation needed to be in close proximity to ArcA  vs. D1-H2   .via the transfer from Asp54 of ArcA to the conserved histidine (His717) in the Hpt domain and then releasing the phosphate group from D1 domain , which includes the PAS domain. As proposed by Pasteurella (Uniprot A0A126QD99), which is worth studying further to assess the mechanism of signal sensing.The available protein models in the AlphaFold database and the comparison between the types of ArcB kinases can uncover mechanistic insights into signal sensing. E. coli is absent in type 2 kinases. Genetic data indicate that both Mannheimia (Uniprot A0A378NAL7) and Haemophilus (Uniprot A0A2S9SIC6) ArcB homologs can provide toluidine blue resistance and redox regulation in vivo in an arcB null mutant in E. coli respectively , the linker region of D1 (Cys470), and the Hpt domain (Cys554). On the other hand, the ArcB protein from Haemophilus influenzae has five cysteine residues located in different domains of the protein. Specifically, there is one in the first transmembrane domain , one in the ATP-binding box and H1 domain (Cys268), one in the D1 domain (Cys472), and two in the Hpt domain (Cys574 and Cys596). ArcB proteins from different bacterial species have variations in their amino acid sequences, including the number and location of cysteine residues. For example, the ArcB protein from E. coli to different environmental cues. Despite all the knowledge gained from this model organism, several aspects of its physiology and molecular response to the environment need to be further scrutinized. One example of regulatory systems that still encase aspects that need further study is TCSs, such as their evolution, structure, specialization, and signaling mechanisms. In this report, we provide insight into the structural features of ArcB sensor kinase that confirms being studied in detail by genetic and biochemical approaches in numerous studies. Also, here we present evidence that predictive models can help advance our knowledge of the role of kinase regions involved in its regulation waiting for experimental structure determination. The predictive models also open the avenue for protein engineering applications such as developing biosensors  with ArcB and other kinases, limiting their diffusion in the membrane plane. The same detergent-resistant domains have been shown to directly affect the kinase activity, as reported for the Staphylococcus aureus SaeRS TCS  to generate specificity , thus esin vivo CheY can interact with the ArcB kinase (as shown in Interestingly, these mutations occurred on the histidine kinase of one system and on the response regulator of the other, resulting in the evolution of specificity rather than changes in phosphotransfer speed between them. Based on the data presented in The modeling of ArcA in interaction with ArcB in this study shows that either the dimeric or tetrameric forms of ArcA, only one monomer binds to the Hpt domain. According to available data on the multimerization of ArcA, the complex formation between phosphorylated and unphosphorylated ArcA is 1:1 . Therefo180 has been experimentally determined; this residue is needed to form a disulfide bridge that usually is in the range of ~2.4–4.0 Å to form (170 suggests that the electrons in the aromatic π-system avoid the lone sulfur pair of electrons, modifying the plane at which the disulfide is located (shown here in 170 is followed by either Phe169 or Tyr169 (170, suggesting that the presence of aromatic residues hampers the formation of Cys180-Cys241 disulfide bonds and orients both subunits to each other. Also, the location of Phe177 may play a role in the redox state of Cys180 due to the orientation towards this residue. The experimental assessment of the two aromatic amino acids near Cys180 may shed light on the fine-tuning of the regulation of ArcB.The mechanistic role of Cys to form . Also, t to form . Here, tr Tyr169 ; both reThe two types of ArcB kinases found to date suggest that in type 2 kinases, the sensing of the signal may require the interaction with another protein rather than by disulfide bond formation or the involvement of other cysteine residues not located in the linker between the transmembrane domains and the catalytic domains. Further research on organisms bearing type 2 kinases will illuminate this subject.Overall, in this work, we addressed the following. First, AlphaFold2 models are accurate in two regions that have been experimentally determined for the sensor kinase. Second, the multimer algorithm of AlphaFold2 provided a model consistent with the available experimental evidence on the phosphorelay mechanism of ArcB. Third, CB-Dock analysis suggests that two cavities can be involved in the binding of anaerobic metabolites. The binding depends on the ligands’ cavity size and physicochemical properties. Finally, there is still much work to be done regarding the experimental determination of the structure of both type 1 and 2 ArcB kinases, at least the cytosolic portion of the kinase, to assess the oxidation state and the environment surrounding the cysteine residues.ArcB/A TCS has been the center for many studies regarding its function and role in cell physiology. Numerous research has been conducted to date addressing the fundamental aspects of signal perception and target genes. In this work, we provide insight on the structural features of the ArcB sensor kinase and ArcA response regulator that are consistent with the available experimental evidence. Protein models used in this work are compatible with the current biochemical and genetic data. However, in this work, we provide a hypothesis that D-lactate and perhaps other anaerobic metabolites may bind to the PAS and catalytic domains, suggesting that experimental approaches to elucidate which two binding-sites are the true regulatory pocket of ArcB. Although models are providing insight into protein function and characterization, crystallographic data is urgently needed to clarify further the mechanistic details of ArcB anaerobic metabolites binding and further characterize the phosphotransfer mechanism to ArcA response regulator.10.7717/peerj.16309/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.16309/supp-2Supplemental Information 2Click here for additional data file.


M) according to the equation 0.048·exp (0.073·M) (R2 = 0.97). The LA of the flours decreased up to 90% under the studied conditions. MW treatment also significantly reduced (up to 20%) the FFA level in the flours. The rheological study confirmed the presence of significant modifications induced by the treatment, as a lateral effect of the flour stabilization process.In recent years, many efforts are being made to produce tef-based food for its nutritive and health-promoting advantages. Tef grain is always whole milled because of its tiny grain size and whole flours contain bran  where major non-starch lipids could be deposited along with the lipid-degrading enzymes: lipase and lipoxygenase. As lipoxygenase shows little activity in low moisture, the inactivation of lipase is the common objective for most heat treatments to extend the shelf life of flours. In this study, tef flour lipase inactivation kinetics via hydrothermal treatments assisted using microwaves (MW) were studied. The effects of tef flour moisture level  and MW treatment time  on flour lipase activity (LA) and free fatty acid (FFA) content were evaluated. The effects of MW treatment on flour pasting characteristics and the rheological properties of gels prepared from the treated flours were also explored. The inactivation process followed a first-order kinetic response and the apparent rate constant of thermal inactivation increased exponentially with the moisture content of the flour ( In recent years, the market for gluten-free baked products has grown quickly worldwide, more than expected, as a result of the increased incidence of celiac disease . The comGrains can be stored for a long duration without much change in quality as long as their kernels are intact and stored under the recommended conditions. However, during and after the milling of these grains, the lipids undergo many changes due to the action of lipolytic enzymes, which are mainly located in the aleurone, subaleurone, and oil-rich germ regions, which affect the sensory quality of the products . The milWhole flours contain bran  where major non-starch lipids could be deposited along with the lipid-degrading enzymes: lipase and lipoxygenase . As lipoIn other heat treatments of flour, hydration has been identified as a critical parameter for successful enzyme inactivation ,23,24. TOn the other hand, the heat treatment of cereal grains and their flours at varying moisture level causes significant changes in their morphology and physicochemical properties. MW treatment is being applied to flours and starches of gluten-free cereals to modify their properties and expand their range of applications ,26,27. STherefore, the aim of this work was to establish the inactivation kinetics of lipase in tef flour by means of MW radiation and how it depends on flour moisture content. Flour hydration was varied between 12% and 25%, while MW treatment time was varied between 1 and 8 min. The side effects of flour MW treatment on flour’s important properties, such as the free fatty acid content (FFA), the flour pasting characteristics, and rheological properties of gels prepared from the treated flours, were also tested.® containers ranged between 60 and 121 °C, showing an increasing trend with increasing flour moisture levels and treatment time, as shown in p > 0.05) reduced the LA from nearly 10% to 94% of the LA in the native flour, depending on the treatment time and the moisture level at which the flours were conditioned. For all moisture levels, MW treatment time had a highly significant effect (p < 0.01) on the LA in the resulting flour, and the inactivation level achieved increased with increasing treatment time. The influence of flour moisture level was also important, where the LA reduction increased with increasing flour moisture level. However, the effect of treatment time was more vivid than the flour moisture content , t = treatment time (min), A0 = lipase activity at t = 0 min. The constant k is the apparent constant of lipase inactivation (min−1), which is dependent on flour moisture level as described in Equation (2):M = flour moisture content (g H2O/100 g flour), K0 represents the rate of LA inactivation when the flour moisture content is 0% (min−1), and b is a constant that quantifies the influence of moisture level in the flours on the lipase inactivation rate. This leads to the general equation (Equation (3)) that helps to estimate the effect of both variables, M and t:The dependence of lipase inactivation on flour moisture level and MW treatment time was estimated by determining the kinetics of LA reduction by fitting the results to a first-order kinetic model (Equation (1)) as described in Pérez-Quirce et al. :(1)A=A0·A0 constant after fitting the data set for this specific system was A0 = 0.62 ± 0.01 U/g, while the values of k0 and b were found to be 0.048 ± 0.007 min−1 and 0.073 ± 0.007, respectively. The adjusted correlation coefficient (R2) obtained for the regression undertaken was 0.97 and the standard error of the estimate was 0.027 U/g. This underlines how successfully the model explains the changes in the LA of the flours depending on the flour moisture level (12–25%) and the length of the MW treatment time (1–8 min). The estimated value of A0 by the model is very close to the measured initial LA in the control sample, which was the tef flour before the MW treatment  effect. As can be seen in Free fatty acid (FFA) content in the MW-treated tef flour samples and the control are shown in p < 0.005). In general, flours treated with long MW treatments showed a higher pasting temperature, while those subjected to short treatments did not show significant differences and this is in agreement with earlier studies [The MW treatment on the tef flour had a visible effect on the pasting property parameters  and the  studies ,31. The  studies . AccordiThe remaining pasting parameters that are related to paste viscosity varied significantly showing an initial increase followed by a decreasing trend; the same trend was reported by Kamble et al. . Peak vip > 0.05) increased τmax, while further treatment led to immediate deterioration. A similar trend was reported for buckwheat by Vicente et al. [p < 0.05) increase was scored at the 6 min treatment time level, while it suddenly dropped with the 8 min treatment.The results of the rheological properties of the tef flour gels obtained after MW treatment at a 25% flour moisture content as affected by the treatment time are depicted in e et al. , which u1′) were greater than their viscous moduli (G1″), making (tan δ)1 < 1. The MW treatment of less than 6 min altered the elastic (G1′) and viscous (G1″) moduli of the resulting gels only moderately. However, the values of the viscoelastic moduli scored by the gels made from the 8 min treated tef flour were significantly lower than the control and the rest of the treatment time levels. This fact was also supported by the significantly higher (tan δ)1 value scored by the 8 min-treated flour indicating the less elastic behavior of its gel [a exponent was obtained for the control gel and continuously increased with the increasing treatment time (except in the sample treated for only 1 min) showing an increased effect of frequency on the elastic modulus of gels made from the treated flour. This might indicate the deterioration of the structural stability of the gels with the increasing treatment time [b exponent started to increase significantly as of the 2 min treatment, indicating the increasing dependency of the G1″ of the gels on the frequency. The c exponent, which results from the (b-a) difference, showed the opposite tendency, which decreased significantly after 4 min of treatment This underlined the dependency of the ratio G1″/G1′ of the gels on the frequency, and the ratio also decreased with the increasing treatment time.The dynamic moduli values obtained from the frequency sweep data revealed all the gels obtained from flours had solid-like behavior because their elastic moduli  was whole floured using a laboratory mill  fitted with a 0.5 mm screen size. The flours were immediately packed in impermeable plastic bags and stored at 4 °C until further analysis and MW treatment.® container and treated  in a MW oven  for 1, 2, 4, 6, and 8 min in cycles of 10 s of radiation and 50 s of rest. During the treatment, the Teflon® containers were continuously rotated (60–70 rpm) to distribute radiation evenly. The temperature in the containers was controlled using Testoterm® temperature strips . The treated flours were dried at 35 °C in an incubation chamber  until their MC reached 12%, and then sieved to <500 µm.Adjustment of the moisture contents (MC) of the tef flour was conducted by adding distilled water to reach 15, 20, and 25% from an initial value of 12% on a wet basis. The flours were continuously mixed while adding the water and then stored for 24 h at 4  ±  2 °C with polyethylene bags for moisture equilibration. A total of 75 g of tef flour of each MC level was weighted and placed in a 1000 mL hermetically sealed cylindrical Teflong for 5 min were pooled and evaporated using a rotary vacuum evaporator, and the residue was dissolved again in 4 mL of isooctane. The other test tube (sample) was incubated for 4 h at 40 °C. Following the incubation, lipid extraction was performed as described for the blank, and both extracts were used for the lipase assay. An aliquot of 0.75 mL isooctane extract was mixed with 0.5 mL of 3% (v/v) pyridine in 5% (w/v) aqueous cupric acetate. The mixture was shaken for 1 min, centrifuged for 1 min, and the absorbance of the supernatant was read at 715 nm. The extract absorbance was compared with the absorbance of the oleic acid standard solutions (1–10 mM) prepared in isooctane. The lipase activity was expressed as units per g RB (U/g), where 1 U was defined as the micromoles of fatty acid liberated per h, according to Equation (4):v = the volume of olive oil added (mL), SA = the absorbance of the sample after incubation at 715 nm, BA = the absorbance of blank at 715 nm, −1cm−1, t = incubation time (h), l = path length (1 cm), and s = sample weight (g).The extent of lipase inactivation was determined by measuring the lipase activity in the treated flour samples according to Rose et al. , followiFree fatty acid values were determined using the rapid titrimetric procedure for small grains described in the AACC method 02-02 . The resFlour pasting properties were determined using a Rapid Visco Analyser . The flour (3.5 g) (at 14% basis) was dispersed in distilled water (25 mL) and the slurry was subjected to RVA analysis under programmed heating and cooling conditions. A temperature of 50 °C was applied for 1 min, followed by heating to 95 °C at a rate of 6 °C/min, holding at 95 °C for 5 min, cooling to 50 °C at a rate of 6 °C/min, and holding at 50 °C for 2 min. The paddle speed was set at 160 rpm. The pasting property parameters recorded were peak viscosity (PV), trough viscosity (TV), breakdown viscosity (BV = PV − TV), setback viscosity (SV = FV − TV), final viscosity (FV), and pasting temperature (PT). All samples were analyzed in duplicate.max) applicable within LVR was estimated by locating the stress level at which G′ decreases above 10%, which coincided with the sudden increase in tan δ [1′, G1″, and (tan δ)1 represent the elastic and viscous moduli and the loss tangent at 1 Hz, respectively, obtained by fitting the power law to the frequency sweep data ranging from 1 to 10 Hz. The values a, b, and c are the exponents of the corresponding potential equations (power-law model) and quantify the dependence of the dynamic moduli on the oscillation frequency. All tests were completed in duplicate.The rheological tests were performed on the control (untreated tef flour) and the MW-treated flour conditioned to a 25% moisture level because the more visible change was anticipated at this flour moisture level. Fresh flour gels obtained from the pasting property analysis described in in tan δ . Frequenin tan δ . The coep < 0.05) between samples.Statistical analysis was conducted using Statgraphics Centurion 18 . The least significant difference (LSD) analysis of variance (ANOVA) was used to assess the significant differences (2 = 0.97). The apparent rate constant of thermal inactivation increased exponentially with the moisture content of the flour and treatment time. However, despite the effectiveness of a longer treatment time and higher moisture level, caution should be taken due to the fact that severe treatments, above 6 min of MW radiation, may lead to deleterious effects on the flour by generating higher FFA in the flours and lowering the strength and consistency of the gels obtained from the flours.The result of the MW treatment of tef flour can be considered a promising alternative for reducing lipase activity in tef flour. The lipase inactivation process followed a first-order kinetic response (R


As a promising distributed learning paradigm, federated learning (FL) faces the challenge of communication–computation bottlenecks in practical deployments. In this work, we mainly focus on the pruning, quantization, and coding of FL. By adopting a layer-wise operation, we propose an explicit and universal scheme: FedLP-Q (federated learning with layer-wise pruning-quantization). Pruning strategies for homogeneity/heterogeneity scenarios, the stochastic quantization rule, and the corresponding coding scheme were developed. Both theoretical and experimental evaluations suggest that FedLP-Q improves the system efficiency of communication and computation with controllable performance degradation. The key novelty of FedLP-Q is that it serves as a joint pruning-quantization FL framework with layer-wise processing and can easily be applied in practical FL systems. With the advent of the big data era, data privacy and system efficiency have attracted great attention, which also brings unprecedented challenges to the field of artificial intelligence (AI). As the first attempt to solve the problem of data silos, federated learning (FL) has now emerged as an important and promising branch of distributed machine learning . WithoutThe employment of AI in communication networks is already considered as a core feature of 6G systems . In partThe development of this distributed learning paradigm faces multiple challenges. In FL scenarios, clients have greater control over distributed data and local devices. Thus, there exists significant heterogeneity in terms of both data and devices. The former causes a non-iid data distribution, which hinders the convergence and the performance of the global model . The hetIn order to handle these issues related to system efficiency, two basic solutions have been considered: model pruning and parameter quantization. These were first introduced in centralized machine learning, aiming to accelerate the training process and compress the deep learning models . These tThe pruning strategies of FL have been broadly studied in the literature. Caldas et al. proposed the first federated pruning framework in , where tAs for quantization, it is commonly treated as an independent procedure from pruning. QSGD, a classic quantization scheme proposed in , has bee•https://github.com/Zhuzzq/FedLP/tree/main/FedLP-Q), a joint pruning-quantization FL framework employing the layer-wise approach. FedLP-Q provides a simple but efficient paradigm to mitigate the communication loads and computation complexity in FL systems;We propose FedLP-Q  in . Layer-wb and the layer 2-norm N follows l, the layer 2-norm will firstly be encoded into 32 bits (float). The quantization setting b (integer) will be attached using Elias omega coding. Then, for each gradient, the index of interval b. Then, for each gradient, its interval index will be reconstructed according to Elias omega decoding, while the sign will be decoded by the last bit.Given the quantization bit  coding  . The corAlgorithm 1 Encoder for layer lInput:   1: encode the header▹for do   2:    3:    encode the interval index▹    4:    encode the gradient sign▹ end for   5: Output: encoded message After layer-wise pruning-quantization, the layer gradients can be represented by a 4-element tuple Algorithm 2 Decoder for layer lInput: received message Initialization:    1: decode the gradient norm▹   2:    3: decode the quantization bit▹   4: while do   5:    6:     decode the interval index▹    7:        8:     decode the gradient sign▹    9:     end while  10: Output: decoded elements Algorithm 3 FedLP-QInitialization: local models, pruning configures, etc.fordo   1: for participator client k in parallel do   2:     client side▹    3:         Update    4:         Calculate the accumulated gradients    5:         Conduct layer-wise pruning:    6:         Quantize the local gradients:    7:         Carry out Algorithm 1 to encode the quantized gradients;    8:         Upload local message to parameter server; end for   9:       10:     Carry out Algorithm 2 to decode the received messages; server side▹   11:     Aggregate each layer θ¯tl by ;   12:     Download the global model: client side▹ end for  13: Output: global model: Finally, we formulate the procedures of FedLP-Q as in Algorithm 3. The main changes from the original FL include (1) layer-wise pruning (for both the homogeneity and heterogeneity case ) for the local gradients is conducted in Line 5; (2) stochastic quantization and the corresponding encoding/decoding are performed in Lines 6 and 7; and (3) Line 9 introduces the layer-wise aggregation for the proposed pruning-quantization schemes.Then, we theoretically analyzed the convergence and coding performance of FedLP-Q.Since FedLP-Q modifies the aggregation process of the original FL, the main impacts on convergence come from layer gradient pruning and quantization. Assuming that local training is independent from the pruning operations, the following proposition indicates the convergence result of FedLP-Q.Proposition 1.Consider a fairness case where Therein,denote the aggregated gradients in the original FL and FedLP-Q, respectively.Proof. For simplicity, rewrite the client index in ation in , the expantizers ,25. ThenRemark 1. shows thn e.g., ,24) and  and Proof. N using Elias omega coding satisfies l-th layer as shown in b takes The proposition is an intuitive result of the proposed quantization coding scheme. As discussed above, the code length of an integer shown in . □Remark 2.According to , the oriwhere ients in  and 19)S=2b is tSpecifically, the Elias coding approach for QSGD considers the positional coding of the non-zero gradient to fit the cases with sparse and numerous parameters. On the contrary, in FedLP-Q, the gradients of each layer are handled independently. Therefore, the sparsity and the element amounts are not enough to show the advantages of the Elias coding scheme in QSGD. The experimental results in the next section also indicate that the quantization coding for FedLP-Q can be simple but more efficient. Overall, by employing layer-wise pruning and quantization, FedLP-Q improves the communication–computation efficiency in FL, and also simplifies the code design for model uploading.Furthermore, we discuss the code efficiency. After layer-wise pruning-quantization, the gradients of Definition 1.After the b-bit quantization, the entropy rate of the layer gradients where where l-th layer. In the next section, we will show that the proposed FedLP-Q can achieve the above goals through experiments.Then, the code efficiency can be investigated by comparing the per-gradient code length and the entropy rate. According to Proposition 2, the per-gradient code length can be expressed asymptotically by:In this section, we describe several experiments to evaluate the performance of the FedLP-Q schemes.p for the homogeneous case, abbreviated as FedLP-Q_Homo(p). For the heterogeneous case, we separated the Conv layers into five ordered sequences with LC from 1 to 5. These five sub-models were assigned to clients according to LC distributions. FedLP-Q_Hetero(l) means that the model with l LC was assigned with the highest probability and the parameter ’u’ represents the uniform assignment.We carried out the experiments on an image classification FL task on CIFAR-10 with 100 clients and a 0.1 participation rate. Specifically, the parameter server randomly selected 10 clients for aggregation in every global round. The number of the local training epoch was fixed at 5. We adopted the global model as a CNN-based neural network with six Conv layers and two FC layers. Batch normalization and maxpooling were also conducted following each Conv layer. As for the two proposed schemes, we adopted the consistent LPR b = 10) led to nearly the same test accuracy curves as that of FedAvg, but significantly reduced the communication traffic. Small quantization bits and the non-iid data settings led to a lower accuracy and the instability of the global model. Therefore, there exists redundancy in the transmitted gradients and the quantization representation. By setting different FedLP-Q configures, FL systems can reduce the requirement of the communication rate and computation capability, which enables more devices to participate in the FL tasks.We evaluated the performance of the proposed FedLP-Q schemes under both iid and Dirichlet non-iid data (The detailed numerical results for the test accuracy and the communication–computation efficiency are listed in We next evaluated the efficiency of the coding scheme for FedLP-Q. Finally, we investigated the gap between the per-gradient length of the proposed schemes and the entropy rate of the gradients.  through  based onAll the above results suggest that there exists a significant redundancy in the interaction of FL systems. Layer-wise pruning-quantization and the proposed coding design can be applied without damaging the model performance. Therefore, it can be concluded that FedLP-Q is an efficient scheme that improves the system efficiency and facilitates the deployment of FL applications by mitigating the straggler effects.In this article, we proposed FedLP-Q, an efficient pruning-quantization approach for FL by adopting layer-wise operations. The corresponding pruning strategies, stochastic quantization, and coding scheme were formulated. Both the theoretical and experimental analyses indicated that FedLP-Q achieves a better communication–computation efficiency with controllable performance loss. This may be vital for the deployment of FL applications in mobile scenarios.This work also opens up several potential research directions for future work, including exploring the robustness and security of layer-wise pruning-quantization, combinations with other FL structures, quantization/coding designs based on layer-gradient entropy estimation, and more practical modeling for the communication of FL clients.


Antisense oligonucleotide-based (ASO) therapeutics have emerged as a promising strategy for the treatment of human disorders. Charge-neutral PMOs have promising biological and pharmacological properties for antisense applications. Despite their great potential, the efficient delivery of these therapeutic agents to target cells remains a major obstacle to their widespread use. Cellular uptake of naked PMO is poor. Cell-penetrating peptides (CPPs) appear as a possibility to increase the cellular uptake and intracellular delivery of oligonucleotide-based drugs. Among these, the DG9 peptide has been identified as a versatile CPP with remarkable potential for enhancing the delivery of ASO-based therapeutics due to its unique structural features. Notably, in the context of phosphorodiamidate morpholino oligomers (PMOs), DG9 has shown promise in enhancing delivery while maintaining a favorable toxicity profile. A few studies have highlighted the potential of DG9-conjugated PMOs in DMD (Duchenne Muscular Dystrophy) and SMA , displaying significant exon skipping/inclusion and functional improvements in animal models. The article provides an overview of a detailed understanding of the challenges that ASOs face prior to reaching their targets and continued advances in methods to improve their delivery to target sites and cellular uptake, focusing on DG9, which aims to harness ASOs’ full potential in precision medicine. The advancement of antisense oligonucleotides (ASOs) has brought about a profound change in the field of genetic therapeutics, offering a promising avenue for addressing a diverse array of diseases on a molecular level. ASOs are short synthetic nucleic acid analogs that offer a revolutionary means to modulate gene expression by precisely interacting with RNA transcripts. The history of ASO can be traced back to the pioneering work of Zamecnik and Stephenson in early 1970, who first proposed the concept of using synthetic oligonucleotides to regulate eukaryotic gene expression in cultured cells through sequence-specific hybridization with RNA . Later, ASOs have been successfully employed in treating a wide range of diseases, including Duchenne Muscular Dystrophy (DMD), spinal muscular atrophy (SMA), amyotrophic lateral sclerosis (ALS), and many more. This success led to the regulatory approval of 10 ASO-based drugs  and manyCell-penetrating peptides (CPPs) or peptide transduction domains (PTDs) are one of the many approaches that have been developed to improve the delivery of oligonucleotides. CPPs are small peptides with the ability to transport cargos, including ASOs, across cellular barriers and hereby offer the potential to improve ASOs’ cellular uptake and intracellular distribution, enhancing therapeutic outcomes and reducing the required dosage . The iniParticularly in the context of phosphorodiamidate morpholino oligomers (PMOs), R6G, PiP , and DG9 have captured interest among the CPPs for their potential to improve ASO-mediated therapy. PMOs have shown effectiveness in treating genetic diseases, but their poor cellular absorption continues to be a major drawback. Due to its high efficacy and low toxicity, DG9 has become a promising CPP for improving the intracellular transport of PMOs since it holds the prospect of improved therapeutic advantages . This reWith their ability to precisely target disease-causing genes at the RNA level, antisense oligonucleotides (ASOs) have become an important tool in the development of therapeutics. ASOs show promise in the treatment of a wide range of illnesses, such as cancer, viral infections, genetic disorders, and neurological problems. Currently, 15 oligonucleotide therapeutics have received approval from the Food and Drug Administration , the European Medicines Agency (EMA), and/or the Japanese Ministry of Health, Labour, and Welfare, and most of them have received approval in the past 4 years  Table 1Table 1. Antisense oligonucleotides (ASOs) are synthetic, single-stranded nucleic acid molecules targeted for mRNA, generally comprised of ~18–30 nucleotides with a variety of chemical structures . ASOs foRNase H-mediated degradation: When DNA-based oligonucleotides, also known as gapmers, bind to their respective mRNA sequences, they can recruit endogenous RNase H enzymes. RNase H recognizes the RNA–DNA duplex and catalyzes the degradation of RNA, leading to the reduction in the target RNA and gene silencing a 14]. T. T14]. TSteric hindrance: Apart from RNase H-mediated breakdown, ASOs can interfere with RNA–RNA or RNA–Protein interaction by blocking certain regions within the target transcript. This results in the prevention of translation rather than the lowering of transcript levels . The besThe effectiveness of antisense oligonucleotides (ASOs) as therapeutic agents depends significantly on cellular uptake and intracellular distribution. To have the desired effects, ASOs must efficiently penetrate cells and locate their target locations. After intravenous, subcutaneous, or direct administration, ASOs reach the bloodstream, where they can be broken down by nucleases . Once thAlthough ASOs have great potential as therapeutic agents, their efficient delivery faces several difficulties. These difficulties are associated with the physiochemical characteristics of ASO molecules, such as their large size, molecular weight , and negative charge, which hinders passive diffusion across the cell membrane. ASOs predominantly rely on endocytosis for cellular uptake, which might be ineffective and lead to entrapment in endosomes or lysosomes, leading to lysosomal degradation. So, once inside the cell, ASO must escape endosomal entrapment to gain access to the target region in the cytoplasm or nucleus . Apart fDue to these challenges, to date, most of the approved oligonucleotide treatments are delivered either locally  or to the liver. The eye is chosen as a target for ASO delivery  due to its accessibility, anatomical considerations, and immune-privileged status . AlthougAntisense oligonucleotides were initially employed as synthesized, unaltered DNA, which turned out to be extremely vulnerable to exonuclease and endonuclease degradation  Figure . ChemicaBackbone modifications involve changing the repeating sugar-phosphate units that make up the phosphodiester backbone of ASOs. Typical changes to the backbone include the incorporation of phosphorothioate (PS) linkages, in which one of the non-bridging oxygen atoms of the inter-nucleotide phosphate group is replaced with sulfur  Figure c. PhosphOligonucleotides are frequently modified at the 2′ position of the ribose sugar to provide resistance to enzymatic degradation, improve stability in plasma, and increase tissue half-lives. 2′-O-methyl (2′-OMe), 2′-O-methoxyethyl (2′-MOE), and 2′-Fluoro (2′-F) are among the most used 2′ substituents b 46]. 2. 246]. 2In comparison to unmodified phosphorothioates, 2′-O-methyl (2OMe) and 2′-O-methoxy-ethyl (MOE) have been shown to increase hybridization affinity to their target RNA and decrease sequence-independent toxicity arising from the PS backbone ,48,49,50Locked nucleic acids (LNA) are a type of 2′-modification in which the 4′-carbon is linked to the 2′-hydroxyl group and have also been utilized in steric block ASOs, such as miRNA inhibitors b. LNAs oOther sugar modifications that are less frequently used are tricyclo-DNA (tc-DNA) and S-constrained-ethyl (cEt) b. For tcASOs’ characteristics can also be enhanced by the introduction of nucleobase modifications in ASO to achieve optimized Watson–Crick base-pairing and thereby control the melting temperature of the ASOs  Figure d. AlteraApart from the modifications mentioned above, other chemistries have also been explored for the improvement of ASOs’ drug-like properties, such as PNA (Peptide Nucleic Acid) and PMO (Phosphorodiamidate Morpholino Oligomer) e. PNA isAnother strategy that has been explored is the use of PMO synthetic backbone modification, in which the five-membered ribose heterocycle is replaced by a six-membered morpholine ring structure with phosphorodiamidate linkages . SimilarDmd using 2′-OMePS ASO chemistry was documented in a work by Lu et al. [mdx mice by the intramuscular administration of leashed PMOs . Even two weeks after the injection, the skipped transcript was observed, which was not the case with 2′-OMePS ASOs [To date, four PMO drugs have been approved by the FDA: eteplirsen, golodirsen, viltolarsen, and casimersen  for Duchenne Muscular Dystrophy ,74 Tabl. The firu et al. . In anotePS ASOs . An addiePS ASOs .In addition to the DMD, the neutrally charged PMO chemistry was also being studied by several groups for the treatment of central nervous system (CNS) disease. For Spinal Muscular Atrophy, a neurodegenerative disease, peripheral administration of Nusinersen, an MOE-based drug, has already been proven to rescue the SMA phenotype ,79 Tabl. HoweverHowever, the main pharmacokinetic shortcomings associated with PMO are low efficacy, which is related to its rapid clearance from the bloodstream, poor uptake in tissues like the skeletal muscle, and endosomal entrapment ,83. As PWhile chemical modifications are required to protect ASOs from exonucleases and prolong their stability, the next challenge is ASO passage across biological barriers. These barriers include the vascular endothelial barrier, cell membranes, and intracellular compartments. Additionally, achieving specific cell/tissue targeting and a reduction in clearance from circulation is essential . ImproviCell-penetrating peptides  are short (fewer than 30 amino acids) cationic, amphipathic, or hydrophobic peptides that translocate small drugs/cargo across cell membranes and biological barriers ,102. CPPCPPs can be classified into cationic CPPs, amphipathic CPPs, and hydrophobic CPPs based on their physicochemical characteristics . CationiAs mentioned earlier, PMOs show low efficacy as therapeutic agents due to their poor cellular uptake, less permeability of membrane barriers, rapid clearance from the systemic circulation, inability to cross blood–brain barriers, and the requirement of repetitive administration and/or a high dosage of the drug for executing its function. Apart from that, due to the hydrophobicity of the plasma membrane and the neutral charge in PMO, only small portions of internalized PMOs can escape endosomes and reach their intended target . A promiDmd gene, resulting in the loss of dystrophin, the structural muscle protein [A promising utilization of CPP is their ability to directly conjugate with neutrally charged PMO and PNA using several methods, including maleimide linkage, disulfide linkage, click chemistry, or amide linkage. It enhances the pharmacokinetic properties of PMO and PNA. Among various therapeutic purposes, this approach has been extensively explored, mostly for Duchenne Muscular Dystrophy (DMD). This affects approximately 1 in 3500 newborn boys and is caused by out-of-frame deletions in the  protein . Lack of protein . The mRN protein ,74. Etep protein . For gol protein . The cas protein . Whether protein . Therefomdx mouse model of DMD in a variety of doses, time intervals, and delivery methods. It was observed that a single intravenous administration can cause high dystrophin exon skipping in skeletal muscle, the diaphragm, and for the first time in the heart [mdx mice confirmed approximately 50% of wild-type dystrophin levels along with restoration in cardiac function [Conjugation of CPPs with PMO is one such approach to improving PMO delivery. This strategy was first demonstrated with an arginine-rich peptide (RXR)4, which was administered to the he heart . Anotherhe heart . In anotfunction ,117 and function . Fusion function . Interesfunction . Additiofunction . Anotherfunction . R6G pepfunction ,124. Whefunction .mdx mice [mdx animal model [mdx mice who had previously undergone a forced exercise regimen to cause changes resembling the DMD cardiac phenotype [mdx mouse has also been found to induce an effective exon 23 skipping and a noticeable increase in dystrophin rescue [Recent research has led to the development of several peptide series known as “Pip’s” , which are generated from the parent peptide penetratin ,126 and mdx mice . To incrmdx mice . In a stal model . In anothenotype . Addition rescue . Anothern rescue .CPP-PMO has also been used as a therapeutic approach for myotonic dystrophy type I, where a CTG expansion in the DMPK gene’s 3′ untranslated region causes a pathogenic transcript that interacts with RNA-binding proteins like muscleblind-like 1 (MBNL1) to cause widespread aberrant splicing abnormalities. Systemic administration of B-PMO targeting this repeat element causes the blocking of Mbnl1 sequestration, resulting in normal nuclear distribution and subsequent correction of abnormal RNA splicing, including for the chloride channel 1 gene, which is a primary contributor to myotonia .One of the biggest challenges of nucleic acid therapy is crossing the blood–brain barrier to reach the central nervous system (CNS) after systemic delivery. CPPs have been identified as promising medicines in the treatment of central nervous system (CNS) diseases due to their demonstrated transmembrane transporting ability. It is assumed that small-size cationic or amphipathic CPPs may exhibit greater affinity for negatively charged endothelial cells on the blood–brain barrier ,133. CPPCPP-PMO strategies have also been developed for the treatment of other neurological diseases like Huntington’s disease (HD) and Amyotrophic Lateral Sclerosis (ALS) , as wellAlthough CPPs hold promise in facilitating the transport of biologically active cargo across cell membranes, including the notorious blood–brain barrier and other challenging barriers within the body, they also pose a number of difficulties and issues that require careful study. The primary obstacle to completing clinical trials for PPMO-based medications right now is their toxicity and immunogenicity. Toxicity can be variable depending on several factors, including species, treatment duration, frequency of systemic administration, dosage, exons skipped, and the cationic nature of the peptide . AdditioDG9 is a cell-penetrating peptide derived from the protein transduction domain (PTD) of the human Hph-1 transcription factor, which facilitates the cell membrane penetration of its protein cargos in the lungs . Two of A separate study reported that a conjugate of PMO and a unique peptide, derived from a human T cell and a near dimer of the PTD, are at least 10- to 100-fold more efficient than the prior peptides at delivering the PMO into bacteria and ultimately causing bacterial death. The only difference between the DG9 and the peptide used in the study is that only L forms of amino acid residues were used in the peptide . Kim et mdx mice can increase skipping efficiency by a factor of 2.2 to 12.3-fold and 14.4-fold compared to the unconjugated PMO. This resulted in a dystrophin restoration amount of 3% and 2.5% of wild-type levels in skeletal muscles and the heart, respectively. Skeletal muscles produced 2.8 to 3.9% more dystrophin and had an exon 51 skipping level of 55 to 71% after receiving repeated injections of DG9-PMO once each week for three weeks. Most notably, hDMDdel52; mdx mice treated repeatedly with DG9-PMO showed a considerable improvement in forelimb and total limb grip strength, indicating the improvement of the muscle function of the treated mice. Additionally, the tibialis anterior DG9-PMO intramuscular injection was successful and demonstrated dystrophin restoration, suggesting the possibility of DG9-PMO for DMD therapy. There was no significant toxicity observed after the injection of DG9-PMO [The FDA has approved exon skipping as a promising therapy for DMD, which utilizes phosphorodiamidate morpholino oligomer (PMO) to target and modulate gene expression. Yokota’s research group has identified DG9 peptide conjugation as a powerful way of enhancing the exon-skipping efficacy of PMO in vivo ,144,150.Another study by Yokota’s research group tested the effectiveness of DG9 peptide in an SMA mouse model . In thatDespite their huge potential, the poor biodistribution of nucleic acid-based medications has prevented them from being used clinically. As a result, cell-penetrating peptides have been thought of as a potential tactic to enhance passage across biological barriers and intracellular delivery, as covered in great detail in this chapter. However, there are a number of concerns that need to be resolved before CPPs are used in clinics, such as in vivo stability, immunogenicity, cellular toxicity, lack of selective intracellular uptake, and inability to escape from endosomes. Despite extensive research, the underlying mechanisms regulating the extravasation and cellular transport of these CPP-drug conjugates or complexes remain poorly understood. Undoubtedly, a deeper comprehension of these PPMOs’ limitations, pharmacodynamics, and mode of action will help create new CPP generations that are better able to target various tissues  and deliver their payload within the appropriate cellular compartment, giving patients hope for an improvement in their quality of life. The integration of DG9 into ASO-mediated therapy holds the potential to enhance cellular uptake and biodistribution of PMO, opening the door to more effective and precise treatments for a wide range of disorders. However, further research and development are necessary to fully realize the potential and long-term safety considerations. While DG9 has demonstrated proficiency in facilitating ASO transport into the cytoplasm, the precise underlying mechanism remains a topic requiring deeper exploration. This quest for mechanistic understanding holds promise as a compelling avenue for future investigative pursuits. With continued scientific inquiry, answers may emerge, potentially solidifying CPP-conjugated ASO-mediated therapy as a valuable asset within the arsenal of gene therapy strategies.


AlphaFold, but also makes the case for the continuing need for experimental structural biology.This editorial acknowledges the transformative impact of new machine-learning methods, such as the use of  AlphaFold protein structure-prediction program  are generally better than et al., 2021AlphaFold models, including those submitted to CASP14, indeed have large errors in the relative orientations of domains.It is also important to remember that multi-domain targets in CASP are subdivided into ‘evaluation units’ when predictors fail to predict their relative orientations  matrix presents clear warnings when relative domain orientations are uncertain, and the predicted value of the local distance difference test or LDDT , while Structural Biology , Structural Biology Communications  and IUCrJ. For new structures, authors should bear in mind that the use of predicted structures as molecular-replacement models has already become standard practice, and papers describing the resulting structures must make new contributions to our biological understanding.Papers describing macromolecular structure-prediction algorithms and their use are welcome in the relevant IUCr journals: de novo’ model building, but it has not made X-ray crystallography or NMR spectroscopy redundant (Kühlbrandt, 2014AlphaFold has spawned does not mean that experimental techniques have suddenly become meaningless. As other commentators have said (Perrakis & Sixma, 2021The field of structural biology and the nature of its challenges has, for the second time this century, undergone a major change. The resolution revolution in cryo-EM has allowed previously intractable systems to be studied at resolutions permitting ‘


Unsupervised domain adaptation (UDA) aims to adapt a model learned from the source domain to the target domain. Thus, the model can obtain transferable knowledge even in target domain that does not have ground truth in this way. In medical image segmentation scenarios, there exist diverse data distributions caused by intensity in homogeneities and shape variabilities. But multi source data may not be freely accessible, especially medical images with patient identity information.To tackle this issue, we propose a new multi-source and source-free (MSSF) application scenario and a novel domain adaptation framework where in the training stage, we only get access to the well-trained source domain segmentation models without source data. First, we propose a new dual consistency constraint which uses domain-intra and domain-inter consistency to filter those predictions agreed by each individual domain expert and all domain experts. It can serve as a high-quality pseudo label generation method and produce correct supervised signals for target domain supervised learning. Next, we design a progressive entropy loss minimization method to minimize the class-inter distance of features, which is beneficial to enhance domain-intra and domain-inter consistency in turn.Extensive experiments are performed for retinal vessel segmentation under MSSF condition and our approach produces impressive performance. The sensitivity metric of our approach is highest and it surpasses other methods with a large margin.It is the first attempt to conduct researches on the retinal vessel segmentation task under multi-source and source-free scenarios. In medical applications, such adaptation method can avoid the privacy issue. Furthermore, how to balance the high sensitivity and high accuracy need to be further considered. Retinal diseases such as glaucoma and diabetic retinopathy often lead to blindness  and the availability of large-scale datasets. CNNs have outstanding ability to provide powerful and meaningful feature representations for medical image segmentation. Guo et al.  proposedRecently, there has been extensive research about unsupervised domain adaptation (UDA) in the medical image segmentation field. On the one hand, some studies consider making maximum use of multiple source datasets to adapt a model from the source domain to the target domain  that include original unsupervised domain adaptation, source-free unsupervised domain adaptation (SFUDA), and multi-source unsupervised domain adaptation (MSUDA). Under the unsupervised domain adaptation scene, the goal of the model is to learn how to obtain more transferable features for the source domain and the target domain. It can be achieved by emphasizing the features of specific channels with less discrepancy between the first-order and second-order statistics of the source domain and target domain , sensitivity (Sen), specificity (Spe), which are defined as below:where TP and FP denote the number of foreground vessel pixels that are correctly segmented and the number of background pixels that are wrongly classified, respectively. TN represents the number of background pixels that are correctly segmented, and FN denotes the number of foreground vessel pixels that are wrongly classified as background class. Moreover, we also calculate the AUC metric (the area under the ROC curve) that is depended on the recall and precision and is more appropriate to measure performance under an unbalanced circumstance.Because of the presence of distribution shift, the model trained on the source domain tends to be frustrated when facing target sample. In order to deal with such problem, we introduce the inter-domain consistency constraint, which can select those reliable samples to improve the adaptation process.i in the two augmented images,For each target image, there are two different augmented images as the input of the source model, that is, a weak-augmented image and a strong-augmented image. The weak augmentation operations include intensity normalization, random rotation, and random flip. The strong augmentation operations include random gray scale adjustment and random color jitter besides the operations in weak augmentation. Therefore, we get two different prediction results for each pixel Sk denotes the source model trained on the kth source domain. The superscript s denotes the strong augmentation and w denotes the weak augmentation. Due to the existence of domain shift between the source domain and the target domain in the early stage of model training, the two prediction results often have certain differences while the differences represent unreliable samples that do harm to the adaptation. Therefore, we introduce the inter-domain consistency constraint to discover credible samples. If the two prediction results at the same pixel position share the same category, this pixel sample is credible, and it can participate in the domain adaptation. On the contrary, this pixel sample is unreliable and should be discarded. Thus, we can obtain a consistency mask indicating the dependability of each pixel,where ith pixel in strong-augmented image and weak-augmented image, and ith pixel is selected to adapt the kth source model.where For the approach of pseudo label generation, we proposed a dynamic threshold mechanism with weak-augmented image for each source model given byAfter ranking the predicted score results in ascending order, the top γ percentage probability value is taken as the dynamic factor. Dynamic factor fits in with the adaptation process that can adjust according to the training epoch,where a and b are the upper and lower bounds of the interval. With the increase of training rounds, the number of credible vessels samples in prediction also increases gradually, and the probability distribution of the prediction results gradually inclines to the high probability area. Thereafter, the dynamic threshold is supposed to be reduced. After the dynamic threshold is obtained, the final pseudo label can be obtained byIt should be noted that the generation method of pseudo label can be both applied to the prediction of weak-augmentation image and strong-augmentation image. The pseudo label of strong-augmentation image S1 and S2 as special cases, we can get the pseudo label imposing intra-domain consistency constraint for target sample,Considering that the inter-domain consistency constraint only focuses on a single source domain, it can only improve the feature compactness of the model in a single source domain. In order to make full use of the information in multiple source domains, we propose that the intra-domain consistency constraint learns the crucial knowledge and gets rid of the domain shift. Given two source domains and corresponding source models ti will be given to the pixel i only when the pseudo labels of S1 and S2 are consistent; otherwise, it will be assigned invalid category 2. The final output pseudo label only depends on the pseudo label of a weak-augmentation image instead of a strong-augmentation image.The valid pseudo label Those highly reliable pixels obtained via inter-domain consistency constraint can maintain prediction-invariance on different source domain models for each target domain image. This prediction-invariance character can alleviate the domain shift between multiple source domains and target domains to a certain extent and improve generalization between multiple source domains and target domains. Therefore, for the dual consistency constraints, the intra-domain consistency constraint of a single source domain can reduce the intra-class distance and the feature space, while the intra-domain consistency constraint of multiple source domains can utilize multiple source domain models to ease the domain shift problem.Losscon is defined asThereafter, we introduce a consistency loss to utilize the advantages of both the inter-domain consistency constraint and intra-domain consistency constraint via filtering out samples that do not meet both consistency constraints. For all source domain models, consistency loss where CE denotes cross entropy loss,Dual consistency constraints can identify valuable pixels for model training from both intra-domain and inter-domain perspectives to deal with the unlabeled data. However, this strategy is not completely satisfied. The pseudo label generated by dynamic threshold mechanism cannot entirely substitute for the real ground truth, which causes the intra-class feature to be not discriminative enough. Therefore, in order to further reduce the distance of the intra-class features, we propose progressive entropy loss.Entropy minimization is familiar in semi-supervised learning and unsupervised domain adaptation, which essentially supervises model with the help of high probability regions during the training phase. On the other hand, entropy minimization can also be seen as a clustering method to compress the distance within each class, making the features extracted from the model more compact  The source domains are the DRIVE and CHASEDB1 datasets, and the target domain is the IOSTAR dataset. (2) The source domains are the DRIVE and IOSTAR datasets, and the target domain is CHASEDB1. (3) The source domains are the CHASEDB1 and IOSTAR datasets, and the target domain is DRIVE. We evaluate all methods via four common metrics for segmentation task including AUC, accuracy (Acc), specificity (Spe), and sensitivity (Sen). The AUC represents the overall performance which is more appropriate to judge whether an algorithm is robust or not under an unbalanced circumstance. The higher the value of Acc, the higher the correct recognition rate of the algorithm. The Spe and Sen metrics indicate the recognition capacity of background class and vessel class, respectively.We perform the ablation experiments to validate our proposed modules are effective or not on the DRIVE dataset. Comprehensive results are summarized in Inter-domain consistency constraint can filter out pixels with inconsistent categories in the predicted results under different augmentation operations, improving the stability and consistency of the model. Such constraint can explore more valuable vessel samples than background samples. Therefore, it has a strengthening effect on the learning of vessel regions, and the sensitivity of the method-a is improved compared to the baseline.When there is only the inter-domain consistency constraint module, the knowledge of each source domain model is mixed, which is not beneficial to the learning of knowledge in the target domain. Accordingly, when introducing the intra-domain consistency constraint module, more effective vessel pixels are identified during the model training for supervised learning, making full use of the inherent knowledge of multiple source domains. Therefore, the AUC and sensitivity metrics of method-b are increased compared with method-a, especially the increase in sensitivity. However, the specificity decreases from 0.9819 to 0.9741 due to such constraint, because it filters some samples of background class when the model is able to identify more vessels.On the one hand, unsupervised progressive entropy loss enhances the compactness of intra-class features. The high probability regions obtained through supervised learning with pseudo labels guide the model to extract more discriminative features for background and vessel classes. On the other hand, because the generation of pseudo label is based on the dynamic threshold mechanism, it gradually strengthens the recognition capability of vessels during the adaptation process. Therefore, compared with the other experiment group, method-c has a significant improvement in sensitivity, with the highest AUC and sensitivity. Although the accuracy and specificity of the final model have slightly decreased, it has brought about significant improvements in sensitivity, which is more practical for medical image segmentation and can detect more foreground objects to assist in medical diagnosis.We also provide the visualization result of our proposed method in different ablation experiment groups in We perform experiments on DRIVE, CHASEDB1 and IOSTAR three datasets, where we choose two datasets as the source domain and the remaining one as the target domain. We compare our proposed method with three methods with three different multi-source and source-free single condition settings in Compared to the original unsupervised domain adaptation method such as AdaptSegNet , and minor(s)' legal guardian/next of kin, for the publication of any potentially identifiable images or data included in this article.BC: conceptualization, methodology, software, investigation, formal analysis, and writing-original draft. LM: data curation, methodology, resources, software, supervision, writing-original draft. YS: visualization and investigation. All authors contributed to the article and approved the submitted version.