This study introducesthe development of a thermally responsiveshape-morphing fabric using low-melting-point polyamide shape memoryactuators. To facilitate the blending of biomaterials, we report thesynthesis and characterization of a biopolyamide with a relativelylow melting point. Additionally, we present a straightforward andsolvent-free method for the compatibilization of starch particleswith the synthesized biopolyamide, aiming to enhance the sustainabilityof polyamide and customize the actuation temperature. Subsequently,homogeneous dispersion of up to 70 wt % compatibilized starch particlesinto the matrix is achieved. The resulting composites exhibit excellentmechanical properties comparable to those reported for soft and toughmaterials, making them well suited for textile integration. Furthermore,cyclic thermomechanical tests were conducted to evaluate the shapememory and shape recovery of both plain polyamide and composites.The results confirmed their remarkable shape recovery properties.To demonstrate the potential application of biocomposites in textiles,a heat-responsive fabric was created using thermoresponsive shapememory polymer actuators composed of a biocomposite containing 50wt % compatibilized starch. This fabric demonstrates the ability torepeatedly undergo significant heat-induced deformations by openingand closing pores, thereby exposing hidden functionalities throughheat stimulation. This innovative approach provides a convenient pathwayfor designing heat-responsive textiles, adding value to state-of-the-artsmart textiles. Simultaneously, itreduces the reliance of smart textile materials on petroleum-basedraw materials. By exploring this approach, we aim to expand the applicationareas of polyamides and promote sustainable alternatives to the developmentof smart textiles.Polyamides (PAs) have long been recognizedas one of the majorengineering thermoplastics, finding applications in various industriessuch as sports, furniture, textiles, automotive, fishing, and medicaldevices. Their exceptional properties, including excellent chemicalresistance, electrical insulation, robust mechanical and thermal characteristics,as well as ease of processing, have contributed to their widespreaduse.2 into the atmosphereand environmental concerns emerging from their gradual degradationrates and harmful degradation products, along with the continuousdecline in oil reserves, have sparked a growing interest in more environmentallyfriendly ways of synthesizing PAs.9 Accordingly,several partially and fully biobased PAs like PA410, PA510, PA1010,and PA11 have been developed recently from natural sources, e.g.,castor oil, endowing comparable mechanical strength to traditionalpetroleum-based PAs like PA6 or PA66.11 The otherwell-established approach toward developing more sustainable materialsis the incorporation of conventional synthetic polymers, e.g., PAs,with biomass. The production of these biocomposites emits proportionallylower amounts of carbon and greenhouse gases into the atmosphere thanplain petroleum-based plastics; furthermore, replacing conventionalPAs with these biocomposites can reduce the usage of fossil fuel reserves.15 Nevertheless, compounding biomass, i.e., biofillers with PAs, islimited by the relatively low thermal stability of biofillers. Moreover,due to the high polarity and rich intermolecular and intramolecularhydrogen bonds in PAs, high processing temperatures, causing decoloringand thermal degradation of biofillers during blending, are needed.17Conventionally, PAs are synthesized frompetroleum-based monomers;nevertheless, accumulating more CO19 Nevertheless, due to the abundantsurface hydroxyl groups, starch suffers from low compatibility withmost polymers, e.g., PAs.20 Numerous approacheshave been developed to enhance starch compatibility with polymer matrices.The most well-known and common method is employing polyethylene-graftedmaleic anhydride as a compatibilizer.22 The graftingof oligomers/polymers is another efficient strategy to enhance interfacialadhesion between starch and polymer matrix.24 Likewise, disrupting hydrogen bonding between starch molecules viaan esterification reaction between hydroxyl groups of starch and succinicanhydrides has also been of interest.25 Accordingly, alkenyl succinic anhydrides with different chain lengths,from octenyl succinic anhydride to octadecenyl succinic anhydride,could be used for starch treatment. The size of the alkenyl groupand the degree of substitution are important parameters determiningthe level of starch hydrophobic. Octadecenyl succinic anhydride (OSA)is the most popular alkenyl succinic anhydride, which has been usedfor a long time for starch hydrophobization. OSA-treated starch containing3.0 wt % OSA has even been approved by FDA in foods.29 Besides, it has been used as a sustainable template for biomedicalapplications.30 Although there are manyreports on OSA-treated starch as a stabilizer and emulsifier in manyfood systems,32 only a few reports are availableon its melt blending with polymer matrices.33Starch is one of the potential carbohydrate candidates forblendingwith polymers thanks to its abundance, low cost, inherent biodegradability,and renewability.g-starch) through a simple and solvent-freemethod to not only strengthen polymer/particle interfacial adhesionbut also improve starch dispersion in the matrix significantly. Furthermore,to prevent any thermal degradation of starch particles during compounding,for the first time, a novel biobased polyamide with a remarkably lowmelting point of 135 °C was synthesized through a copolymerizationprocess. Thanks to the employed compatibilization methods, variousconcentrations of OSA-g-starch, including a veryhigh concentration of 70 wt %, were easily melt-blended with the synthesizedcopolyamide at 160 °C. To the best of our knowledge, there isno other comprehensive investigation of biopolyamide/starch compositesin the literature. Moreover, to showcase the advantages of the preparedbiocomposites in shape memory textiles, coiled actuators were preparedand integrated into a woven 3D textile structure. The demonstratorfabric was able to switch between closed and open porous structuresas a function of temperature, thus opening the door to the wide varietyof multifunctional textiles, where an added functionality could beexposed and hidden on command.In the current study, OSA was grafted on the surface of starchparticles (OSA-d (99.8atom % D) were obtained from Sigma-Aldrich. Octadecenyl succinic anhydride(mixture of isomers) and 1,12-diaminododecane (≥98%) were preparedfrom TCI, Japan. Chloroform  was purchasedfrom Merck. 1,18-Octadecanedioic acid was bought from Cathay BiotechCompany, China.Starch from potato, 11-aminoundecanoic acid,sodium hypophosphite monohydrate (>99%), trifluoroacetic anhydride, and chloroform-The partially biobasedpolyamide inthis study was synthesized through a copolymerization reaction involvingtwo different polyamides, namely, PA11 and PA1218. The reaction processis illustrated schematically in After the reaction, the reactor was cooled under a nitrogen flow,and the resulting product, a copolymer of PA11 and PA1218 (termedPA11coPA1218), was collected. The synthesized copolyamide was milledusing a Retsch SM 300 Cutting Mill with a 6 mm sieve size (squareholes). For comparison, the parent homopolymers, PA11 and PA1218,were also synthesized by using the same polymerization conditionsas reference materials.g-starchand used for blending with the copolyamide matrix. The establishedreaction between the OSA molecules and starch particles is schematicallydepicted in g-starch in toluene at 90 °C and mixed for 1 h. Afterthat, it was filtered and washed with ethanol (Etax B) to remove anyunreacted OSA residue.The starch wassurface-treated tobe compatible with the biopolyamide matrix. The treatment was carriedout by grafting octadecenyl succinic anhydride (OSA) via a more eco-friendlyapproach, i.e., a solvent-free method. Starch was thoroughly driedin a vacuum oven at 70 °C for 48 h. After that, OSA (10 wt %of the starch mass) was mixed gently with the dried starch, and thenthe mixture was kept in the preheated oven at 100 °C for 24 h.The surface-treated starch was assigned as OSA-g-starch were melt-blended with the copolyamidein a counter-rotating twin-screw extruder  wherethe screw speed was 20 rpm, and the heating zone temperatures wereset to 155 °C (feed), 160 °C (middle), and 160 °C (Die).The output filament was solidified under an air atmosphere and thencut by using a pelletizer. The pellets were then fed to an injectionmolding machine (Engel ES 200/40) to prepare the tensile test specimens.A gentle dosing procedure was used to avoid any damage to the starch;thereby, the four heating zones were set at 155 °C (feed), 160,165, and 160 °C (die) while a screw speed of 30% (counter-pressure1 bar) was used during the dosing step. The injection speed of 150mm s–1 was used during the injection step, followedby after pressure (20 s at 15 bar). The mold temperature was set at40 °C. For other characterizations, the pellets were hot-pressedfor 2 min using a Fontijne Lab Press-TP (The Netherlands) at 160 °Cand then cold-pressed at 20 °C for 10 min. The portions of theOSA-g-starch in the copolyamide matrix were selectedas 10, 30, 50, and 70 wt %, and the samples were coded as PSMS10,PSMS30, PSMS50, and PSMS70, respectively. Biocomposite containing50 wt % native starch (PNS50) was also prepared to compare its propertieswith biocomposites containing surface-treated starch.Differentamounts ofOSA-Plaincopolyamide and PASMS10 and PASMS50 composites were chosen for filamentproduction to create actuators. The materials were introduced intoa twin-screw microcompounder (Xplore Instruments Midi Extruder). Thetemperature was set at 160 °C, and the rotational speed was maintainedat 15 rpm. The extruded material was solidified on a conveyor operatingat a maximum speed of 10 m/min. It should be highlighted that evenat a high starch content of 50 wt %, no filament breaking was observedduring the extrusion process. The resulting filament was wound arounda spindle and utilized for coiled actuator fabrication, as detailedin a subsequent section. The filament diameters for plain copolyamide,PASMS10, and PASMS50 were 190 ± 5, 270 ± 10, and 420 ±20 μm, respectively.Rf) and shape recovery (Rr), as will be demonstrated in the following section, comparedwith the other samples. The actuators were fabricated with self-madeequipment that resembled the fabrication device. First, the filamenttwisted until it started to overtwist. The possible maximum numberof twists causes a stronger actuation.34 After being twisted, the filament was coiled around a metallic mandrel.By varying the twisting and coiling directions, it was possible tomake contracting and expanding actuators. An expanding heterochiralstructure was used for testing and comparing the actuator propertiesbecause expansion was more unlimited as a moving direction. Then,the twisted specimen was thermoset for 150 min at 120 °C in ahydrothermal oven.PSMS50 was chosen forpreparing polymer actuators due to the higher shape fixity  DigitalJacquard loom. In a TC2 loom, each warp thread could be programmedto work independently, allowing for the weaving of complex, multilayeredfabrics and weave patterns. Moreover, while the warp movements ofthe loom were computerized, the weft insertion was done manually byhand, further allowing newly developed materials, in this case, PSMS50coiled actuators, to be prototyped with ease. The five-layered fabriccomprised 100% cotton warp and wefts consisting of Linen-Tencel, Cotton,glow-in-the-dark yarn, and PSMS50 actuators. The top layer of thefabric was woven with 100% cotton and PSMS50 actuators, in which theplain weave bonded the actuators to the textile structure so thatthey did not escape the woven layer during actuation. The cotton yarnsand the plain weave structure complemented and aided the actuationof PSMS50 actuators within the textile network. The cotton yarns providedthe required flexibility to the top layer, allowing the actuatorsto contract and expand easily. The weft of the other 4 layers wasformed by Linen-Tencel. The linen weft was conducive to creating thediamond structure, as the rigidity of the yarn helped to create well-definedcells that retained their shape without wrinkling during the actuation.This enabled the fabric to open into a fully 3D form during actuation.The interplay of rigid yarns and shrinking yarns in weaving has beentested in earlier work to develop 3D fabrics.g-starch. The spectra were recorded between4000 and 500 cm–1 under a scan rate and resolutionof 16 and 4 cm–1, respectively. The proton nuclearmagnetic resonance (1H NMR) spectra conducted by a BrukerAV III 400 NMR spectrometer were used to further evaluate the chemicalstructure of the homopolymers and copolymer. A mixture of chloroform-d and trifluoroacetic anhydride  was used todissolve the sample before subjection to measurement. The gel permeationchromatography (GPC) was performed on an Agilent Multidetector machineto measure the number and average molecular weights, as well as thepolydispersity index of the homopolymers and copolymer. A mixtureof chloroform  and trifluoroacetic anhydride was used as a solvent. Polystyrene standards, dissolvedin the same solvent, were used for calibration. The degree of OSAsubstitution was quantified by an elemental analysis performed ona Thermo Flash Smart CHNSO Elemental Analyzer. First, the calibrationcurve was plotted considering the measured values of carbon, hydrogen,and nitrogen elements in the native starch. The curve is depictedin Figure S1, where the measured valueswere graphed versus the theoretical values, that is, C as 44.45%,O as 49.34%, and H as 6.21%. The measured elemental values were thenmodified due to the calibration curve. After that, the corrected valueof the carbon element was employed to calculate the degree of substitution(DS), considering C is the carbon concentration in the sample, 162.14 stands for the molecularweight of the anhydroglucose unit, and 288.26 and 350.5 are the carbonmass and OSA molecular weight, respectively. The DS was then usedto calculate the weight percent of grafted OSA concerning the molecularweight of OSA (350.5 g mol–1) and the anhydroglucoseunit (162.14 g mol–1). The reported values werethe mean of three replicates ± the error. Different crystallizationproperties of the homopolymers and copolymer, including melting point(Tm), crystallization temperature (Tc), melting enthalpy (ΔHm), crystallization enthalpy (ΔHc), and degree of crystallinity (χc),were obtained by differential scanning calorimetry (DSC) analysisperformed on a TA Instruments MT-DSC Q2000 machine. A two-cycle methodwas applied under a nitrogen atmosphere, where the thermal historyof the sample was removed through the first heating–coolingcycle, and the aforementioned properties were extracted from the secondcycle. The temperature range was between −20 and 250 °C,and the heating/cooling scan rate was fixed at 10 °C min–1. The crystallinity was calculated by using H0 is the enthalpy of a 100% crystalline sample.Fourier transform infraredspectroscopy (FTIR) was run on a PerkinElmer FTIR with an attenuatedtotal reflection (ATR) machine to investigate the chemical structureof the synthesized PA11, PA1218, and copolyamide as well as starchand OSA-H0 wascalculated considering the weight percent of homopolymers,i.e., PA11 and PA1218, based on –1,36 but since there was no value for a100% crystalline PA1218 in the literature, the enthalpy of a 100%crystalline PA1212 (292.2 J g–1) was used.37For copolyamide, Δx is theweight percent of filler particles, e.g., OSA-g-starch.DSC was further used toinvestigate the crystallization propertiesof the developed composites. A similar thermal cycling test was performed;just the degree of crystallinity was calculated using Figure S2). Furthermore,the thermal decomposition of the developed biocomposites was monitoredby TGA . The samplewas heated under a nitrogen atmosphere from room temperature to 800°C at a heating rate of 10 °C min–1. Themorphology of the starch particles before and after OSA grafting,as well as the dispersion level of OSA-g-starch intothe polymer matrix, were evaluated by scanning electron microscopy(SEM) images taken by a Zeiss Sigma VP (Entry-level SEM) machine.The imaging was done from the surface of the particles, while it wascarried out from the cryofracture cross-sectional area of plain matrixand biocomposites. A thin layer of gold–palladium was sputteredon the sample surface prior to the subjection of SEM imaging. Themechanical properties of the samples were evaluated by tensile testingdone by a Universal Tester Instron 5944 machine. Tensile modulus,yield stress, tensile strength, tensile strain (or elongation at break),and toughness, i.e., the area under the stress–strain curve,were extracted and reported. The test was conducted based on ASTMD638–02, with a stretching rate of 5 mm min–1. The samples were conditioned for 48 h at a temperature of 23 °Cand a relative humidity of 50%. Each measurement was repeated 5 times,and the mean value ± standard deviation was reported. The thermomechanicalcharacteristics of the samples, including the storage modulus (E′), loss modulus (E″), andloss factor (tan δ), were evaluated versus temperatureby dynamic mechanical analysis (DMA) conducted on a TA InstrumentsDMA Q800 machine under an air atmosphere. The temperature was rampedup from −20 to 140 °C with a rate of 5 °C min–1. The preload, frequency, and strain rate were 1 N,5 Hz, and 1%, respectively.38 The glass transitiontemperature (Tg) was extracted from thetan δ curve.The grafting of OSA on starch was qualitatively investigated bythermogravimetric analysis (TGA) thermograms performed on a TA InstrumentsTGA Q500 machine (l × w × t) washeated to 80 °C with a heating rate of 5 °C min–1 and kept 5 min at this temperature. Then, the force was appliedto 18 N at an increasing rate of 0.5 N min–1. Subsequently,the sample was cooled to −30 °C followed by a 5 min isothermalstep at this temperature. After that, the temporary shape was recoveredby removing the load with the rate of −0.5 N min–1 until reaching the minimum force of 0.1 N. This process was repeatedfor 4 cycles. Rr and Rf were calculated based on 39m, εp, εu, and N indicate the strainafter stretching (before cooling), strain after recovery, strain inthe fixed temporary shape, and cycle number, respectively.39 It should be highlighted that the upper limitfor temperature was set at 80 °C for the plain copolyamide matrixand PSMS10; however, it was 70 °C for PSMS50 because, at highertemperatures, the elongation of the sample was higher than the operatingrange of the device. The viscosity of the samples and their viscoelasticperformance were investigated by melt rheology testing in an oscillatorymode on an Anton Paar Physica MCR 301 machine. The tests were conductedat 160 °C, the same temperature used for melt blending, withparallel geometry (PP25). A strain sweep test was performed from 0.01to 100% at a fixed angular frequency of 1 Hz to find the linear viscoelasticregion. Afterward, the melt strength and flowability were investigatedwithin the linear viscoelastic region, i.e., a fixed strain rate of1%, through a frequency sweep test ranging from 0.01 to 100 Hz. Thetrend of both moduli, i.e., storage modulus (G′)and loss modulus (G″), as well as the complexviscosity (|η*|) versus angular frequency was plotted and explored.The shape memory effect of the plainmatrix and biocomposites wascharacterized by Q800 DMA using a controlled-force mode. A strip-shapesample with the dimensions of 5 mm × 3 mm × 0.5 mm  and amide II (−NH–CO−)presented two peaks at 1633 and 1541 cm–1, C=Obending formed a bond at 1465 cm–1, and a bond at938 cm–1 originated from amide IV.41 It is noteworthy to mention that the N–H bond peak intensityat 3291 cm–1 reduced considerably in PA1218 andcopolyamide, indicating a decrease in the amount of amide functionalgroups, suggesting an increase in the aliphatic segments’ chainlength.43FTIR spectroscopywas employed asa fundamental analytical tool to investigate the chemical structuresof the homopolymers and copolyamide. As shown in 1H NMR spectra were used to determinethe structureof the synthesized homopolymers and copolyamide concerning the hydrogen-1nuclei. The results are plotted in 44 proving the successful synthesis of thePA11 and PA1218 homopolymers as well as the copolymer through theemployed polycondensation reaction. Specifically, the resonance atapproximately 3.67 ppm ,52 further proved the claim of theesterification reaction. When the spectra are interpreted, it shouldbe taken into account that the C–C–O stretching andO–C–C stretching bonds of the ester groups at 1240 and1047 cm–1 possibly overlapped with the characteristicpeaks of the anhydroglucose unit.The relative intensitiesof some peaks changed in the OSA-The degree of substitutionand the weight percent of the graftedOSA, were estimated based on the elemental analysis. The obtaineddata are summarized in 53 Besides, their surface was smooth. After surface treatment, theparticle size did not change considerably; however, the surface wasno longer smooth and became rough, suggesting a successful graftingof OSA molecules.56The effect of surface treatment on the morphologyof starch particleswas monitored using SEM imaging. The micrographs are shown in g-starch distributed uniformly into the polyamide matrixat a filler content up to 50 wt % with no sign of particle agglomeration.These observations indicated good miscibility of copolyamide and surface-treatedstarch and improved compatibility and interfacial adhesion betweenphases,59 achieved via the graftingof OSA on the starch surface.The microstructureof thebiocomposites was evaluated by using cryofracture cross-sectionalSEM imaging. The SEM micrographs of the biocomposites, as well asthe plain copolyamide and the biocomposite containing 50 wt % of unmodifiedstarch, i.e., PNS50, are shown in 61 As can be observed in g-starch, whichcounts as another piece of evidence of the excellent miscibility ofthe two phases.18In contrast, the PNS50composite containing 50 wt % unmodifiedstarch exhibited significant surface defects along with the formationof voids, confirming that those two substances were inherently incompatiblewith poor interfacial adhesion between the phases.g-starch. This indicates that thestarch particles were not destroyed under the applied shear stressin the extruder, which has already been introduced in the literatureas another reason for the absence of any cracks and folds on the cross-sectionalarea of the biocomposites.62 It is worthnotifying that some researchers observed significant agglomeratesand defects in the composites with significantly lower starch loadings,64 highlighting the advantage ofthe employed method for the surface treatment of starch. In this study,successfully incorporating such a high loading of starch particlesin the polyamide matrix introduces an eco-friendly, green, durable,and sustainable plastic that may significantly reduce the carbon footprintand greenhouse gas emissions.66Overall, the averagediameter of the starch granules was about30 μm, which is in good agreement with what was observed previouslyfor the plain OSA-g-starch particles on the crystallizationbehavior of the developedcomposites was thoroughly investigated by using DSC. The DSC curvesare illustrated in Figure S5, and the correspondingthermal transition data are summarized in 67 Alternatively, it suggests the formation of the α-crystallineform with a relatively uniform thickness.The impact of OSA-68 Previous studies have reported that higher crystallization temperaturescan lead to increased crystallinity in the polymer matrix.37 Consequently, the degree of crystallinity (χc) increased from 10.85% in the plain copolymer to 12.96% inthe PSMS50 composite. This enhancement confirms that the granulardispersed morphology favored the crystallization of copolyamide andthat the nucleation effect was reinforced by the presence of surface-modifiedstarch particles.69Furthermore, the presence of high concentrations ofstarch ledto an increase in the melting point. This can be attributed to thestrong hydrogen bonding between starch particles and polymer chains,which restricted the chain mobility and delayed the melting of thepolymer chains. The crystallization temperature also shifted to highervalues, indicating that the starch particles acted as nucleation sitesduring polymer crystallization from the melt, lowering the energybarrier of nucleation. As a result, less supercooling of the meltwas required to initiate crystallization, leading to a shift in thecrystallization temperature to higher values.70 Additionally, crystalline regions play a significant role in enablingand controlling the shape memory effect, which could prove beneficialin these materials.71 These claims andfindings will be further investigated in the subsequent sections togain a deeper understanding of the overall composite behavior andits potential application.Moreover, the higher meltingpoint observed in composites withfiller contents higher than those of the plain matrix can also beattributed to their relatively higher crystallinity or change in thecrystalline phase. It is worth noting that the relatively higher crystallinityin the composites is advantageous, as it can lead to a higher stiffnessand tensile modulus. This is because crystalline regions provide strongerand more efficient load transfer pathways.73 and long-chain aliphatic polyamides43 the tensile modulus was significantly lower. In otherwords, the copolyamide behaved more like a strong and soft polymer,74 which could be an advantage for blending withmostly rigid biobased fillers like starch.76 Accordingly, the yield stress, tensile strength, tensile strain,and toughness were systematically reduced in the biocomposites, whilethe tensile modulus increased. For instance, in the biocomposite containing50% surface-modified starch, i.e., PSMS50, tensile strength and tensilestrain decreased, respectively, by ∼68 and ∼45%, probablydue to the brittleness, rigidity, and poor mechanical properties ofstarch compared to polyamide. At the same time, the tensile moduluswas enhanced by approximately 25%, a notable improvement that canbe attributed to the higher crystallinity observed in the compositescompared to that of the plain matrix. Crystalline regions within apolymer matrix play a pivotal role in reinforcing mechanical properties,particularly by increasing the material’s stiffness. The alignmentand close packing of polymer chains in these crystalline regions facilitatesuperior load transfer and bolster resistance to deformation, ultimatelyculminating in enhanced stiffness.77 Itis worth notifying that the tensile strain dramatically dropped to10% in the biocomposite containing 50 wt % native starch, i.e., PNS50,proposing a brittle composite. In contrast, it was more than 200%in PSMS70 with an even higher surface-treated filler loading. Thephotograph captured from bent PSMS70 -grafted starch as a compatibilizer and prepared a PLA/thermoplasticstarch biocomposite blend (50/50) with different concentrations ofcompatibilizer. They reported a 50% reduction in both tensile strengthand tensile modulus of the biocomposite but a 9% increase in tensilestrain. Similarly, a significant decrease in Young’s modulusand tensile strength of PLA has been reported after blending the matrixwith thermoplastic starch.80 Likewise, Zhang etal.81 prepared PLA/starch composites (55/45)compatibilized by maleic anhydride and observed a 15 and 10% reductionin tensile strength and tensile strain, even in the biocomposite containing2% maleic anhydride as a compatibilizer. Moreover, Zhou et al.63 observed a 13% decrease in the tensile strengthof PVA/corn starch (80/20) biocomposite.The trend of copolyamide’s mechanical properties as a functionof starch concentration was compared with the research works performedon starch-based thermoplastic composites. A few researchers claimedimprovement in the mechanical properties of the polymer matrix aftermelt blending with starch. For instance, Yusoff et al.g-starch dispersion into the copolyamide matrix,the trends of dynamic storage modulus (E′)and dynamic loss modulus (E″), as well asthe damping factor (tan δ), were evaluated as a functionof temperature from −20 to 140 °C by dynamic mechanicalanalysis (DMA). The results are presented in Tg). This sharpreduction of moduli around Tg can be explainedby the fact that at low temperatures, the molecular segmental motionswere frozen and thereby caused a high level of E′from 900 to 1900 MPa. Upon increasing the temperature, the rigid segmentalstructure relaxed gently, resulting in higher molecular chain motionsin the system.82 On the other hand, below Tg, both E′ and E″ increased systematically with the increase inthe OSA-g-starch content, which could be due to thehydrogen bonds formed between the hydroxy group of starch and aminegroups of polyamide. It should be highlighted that the modulus athigh temperatures showed a slight improvement after the matrix wasblended with surface-modified starch particles, particularly in thePSMS50 composite. This enhancement could be highly advantageous forshape memory applications. The modulus of a material refers to itsstiffness or resistance to deformation. In the case of shape memorypolymers (SMPs), the modulus at high temperatures plays a criticalrole in determining the recovery stress and, consequently, the overallperformance of SMPs in various applications.85 When an SMPis deformed above its switching temperature (Tg or Tm)—in this specificinstance, Tg—the high-temperaturemodulus governs the force it can generate during shape recovery. Achievinga balance between the modulus at high and low temperatures is essentialfor efficient shape memory behavior. Moreover, the incorporation ofsurface-modified starch particles in the PSMS50 composite has demonstratedthe potential for enhancing the high-temperature modulus, furthercontributing to improved shape memory properties, as will be discussedin the following sections.To further evaluate theOSA-86 Likewise,the peak in tan δ curves, Tg, shifted to lower temperatures upon increasing OSA-g-starch content. Namely, Tg shifted from54 °C in the copolyamide matrix to 48 °C in PSMA50 and thento 40 °C in PSMS70. This phenomenon suggests that the biocompositesbecame less viscous after the temperature was raised, and more molecularchain mobility was achieved. In other words, the flexibility of thematrix was developed due to the uniform dispersion of filler particles,which acted mainly as plasticizers rather than reinforcing agents.88 The reduction in Tg upon introducingstarch particles might be an advantage for thermoresponsive shapememory polymer actuators because the actuators could start to activateat lower temperatures.As the temperature increased, theflexible long-chain structure of grafted OSA molecules reduced therigidity of biocomposites.Tg appears in a fully misciblepolymer composite system, lying between the Tg of the individual polymers.89 The Tg for potato starch with 13–15% moisturecontent is reported to be between 75 and 95 °C.90 Accordingly, a well-defined peak in the loss factor indicatedeffective miscibility between polyamide and starch as well as a homogeneousblend microstructure. On the other hand, the height of the tan δcurve pertains to the matrix/filler interphase internal energy dissipation.87 The height of tan δ was relativelylower in biocomposites than in the plain matrix, indicating that theaddition of OSA-g-starch particles considerably reducedthe viscoelastic damping factor of the polyamide matrix, and accordingly,the sample became less rigid.A single g-starch in the copolyamide matrix and gain more insightinto the processing characteristics of the developed biocomposites.First, the linear viscoelastic region was found through a strain sweeptest conducted at 160 °C and a fixed angular frequency of 1 Hz.The data are plotted in Figure S6, whereboth plain matrix and biocomposites, except PSMS70, behaved independentlyof shear strain within the 0.01 to 100% strain range, indicating alinear viscoelastic region. Accordingly, a shear strain of 1% wasselected for the frequency sweep test to guarantee that the measurementwas in the linear viscoelastic regime. The frequency sweep resultsare illustrated in G′) and loss (G″) moduli,as well as complex viscosity (|η*|) were depicted as a functionof angular frequency between 0.01 to 100 Hz at 160 °C. G″ dominated G′ at the testconditions, particularly at lower frequencies, indicating a fullyrelaxed state of the polymer chains, in which the molten polymersrevealed more liquid-like or viscous behavior than solid-like or elasticones,91 suggesting good processabilityof the compounds under the applied shear stress in the extruder.Rheology measurements wereperformed to assess further the microstructural effects of OSA-92 As shown in 93 This behavior suggeststhe excellent fluidity of the molten polymer in the extruder.94The other parameter that reflects the processingflow performanceof composite materials is complex viscosity.96 have reported a systematicincrease in both complex viscosity and storage modulus of the matrixupon adding the filler due to the generation of a network-like structureinside the molten polymer, which led to the increase of the molecularentanglements of polymer chains, as well as owing to the interfacialadhesion and hydrogen bonding between the filler and polymer chains.Nevertheless, in this study, the complex viscosity and moduli decreasedupon increasing OSA-g-starch content, indicatingthe lubricating effect of surface-treated starch. This trend couldbe attributed to the reduced entanglement of polyamide chains obtainedby voluminous OSA molecules, which brought in more free volume intothe polyamide matrix, as well as to the orientation of starch particleswith flexible OSA chains along with the applied shear forces thatpromoted the chain mobilities, behaving thus similarly to previouslyreported for composites containing surface-modified lignin93 and clay97 particles.Many researchersTo support the benefit of the surface treatment on the processabilityof the polyamide matrix, the viscosity of the composite containing50 wt % native starch, i.e., PNS50, was included in 99 One-way shape memory polymers (1W-SMPs), as the name suggests, havethe ability to return to their original or permanent shape from afixed temporary shape in a one-time manner. Two-way shape memory polymers(2W-SMP), on the other hand, can actuate between two different shapes,typically a higher-temperature shape and a low-temperature shape (temporary shape), upon exposureto the appropriate stimuli. The partially recovered shape is determinedby geometry-defining netpoints in the polymer structure, which inthe case of polyamide, is expected to be its crystalline domains.101 Similarly to 1W-SMPs, the polymer is initially deformed into a temporaryshape at a temperature above its thermal resetting temperature, i.e.,at the temperature that is above the melting point for geometry-definingdomains. The polymer is then cooled under tension to lock it intoits temporary shape, as in 1W-SMPs. To revert to its partially recoveredshape, the 2W-SMP is heated to a transition temperature significantlybelow its thermosetting temperature, and cycling the temperature betweenlow and transition temperatures will enable repeatable actuation.103Shape memory polymers(SMPs) are a class of smart materials forwhich a temporary shape can be set, and the return to a permanentor partially recovered shape is further driven by exposure to an externalstimulus, such as temperature change, light, voltage, and humidity.104 developed amorphousfluorinated polyimide with a high shape recovery temperature of 220°C. Furthermore, Shi et al.83 synthesized Tg-based SMP by introducing ionic moieties topolyether ether ketone (PEEK) with a switching temperature of around200 °C. This work was expanded into a Tm-based SMP by integrating sodium oleate, leading to a higherswitching temperature of 230–240 °C resulting from themelting of sodium oleate. These limit their utilization in some applications,such as biomedicine, where fast recovery and low activation temperatureclose to the human body are essential.101 Additionally, from the point of view of shape memory textiles, manycommon textile materials cannot resist heating to the activation temperaturesof the above-mentioned SMPs. Introducing plasticizers to the polymermatrix can affect the thermal properties of the SMPs, leading to adecrease in Tg. For instance, plasticizerscan weaken the H-bonds and reduce the transition temperature. Subsequently,the polymer chain movement at a specific temperature can increase.106 To this goal, in the current study, we developed a novel copolyoamidethat presented a relatively low Ttrans at around its Tg, i.e., 55 °C.Furthermore, we incorporated surface-modified starch particles intothe synthesized copolyamide not only to improve the green contentof the synthesized polyamide but also to reduce the Tg, which might lead to a decrease in the switching temperatureof the polyamide.High activation temperatureand low recovery are major drawbacks of many thermoplastic SMPs. Forinstance, Koerner et al.g-starch,were tested via a cyclic thermomechanical test. The recovery temperaturewas chosen to be Tg + 10 °C to allowfor as complete recovery as possible. The results are provided in Table S2. In the pure copolyamide matrix, Rr and Rf were, respectively,85.13 ± 4.99 and 89.39 ± 2.68%, indicating excellent shaperecovery properties of the newly synthesized copolyamide. The calculatedvalues for both Rr and Rf were as high as reported for common shape memory polymers.108 High storage modulus below Tg and excellentrubber elasticity above Tg contributedto high Rr and Rf. After 4 cycles, the film could still return to its originalshape, indicating an excellent thermally induced shape memory.109First, the 1W shape memory and shape recoveryof the plain polymerand biocomposites, including 10 and 50% OSA-Rr and Rf improved by increasing the number of test cycles, leadingto the better shape memory effect resulting from memory stress reductionand shape memory training.46 During the first cycle,thermal shape recovery leads to a reduction in the entropy and disentanglementof some entanglements, diminishing the ability of polymer chains toreturn to their previous random coils. This stretching and looseningof physical entanglements result in fewer remaining entanglementsavailable for disentanglement in subsequent cycles of thermomechanicalloading. Consequently, the shape recovery value is lower during thefirst cycle compared to subsequent cycles.111The effect of processing history could be seen in the first cycle;however, both Rr and Rf values comparable with those of the unmodified copolyamide matrix.This confirms that the presence of starch particles did not interruptpolyamide chains’ mobility even at a relatively high fillercontent of 50 wt %, chiefly thanks to the employed compatibilizationprocess. Both Rr and Rf were slightly enhanced in the biocomposites, demonstratingthat the OSA-g-starch particles enhanced the mobilityof the polymer chains and helped them easily arrange and recover totheir original shape by disrupting the interchain supramolecular interactions.The degree of crystallinity (χc) in shape memorypolymers significantly influences their shape memory properties. Increasedcrystallinity content often results in a more pronounced and dependableshape memory effect.112 Consequently, thePSMS50 composite with relatively higher crystallinity exhibits slightlyhigher Rr and Rf values. However, it is essential to avoid excessive crystallinity,as it may render the polymer overly rigid, thus limiting its deformabilityand shape recovery capabilities. It is noteworthy that the shape recoveryresults for PSMS50 were obtained at a lower operational temperature,i.e., 70 °C. In other words, the incorporation of surface-modifiedstarch particles not only improved the shape memory performance ofthe polyamide matrix but also significantly reduced the transitiontemperature, making the composite interesting for low-temperatureactuating applications.Both biocomposites, i.e., PSMS10 and PSMS50, revealed qualitativelysimilar behavior over heating/cooling cycles with relatively high1W Tg and Tm, the 2W shape memory effect was investigated. To showcasethe potential application of the developed biocomposite in textiles,a heat-responsive fabric was created using thermos-responsive 2W shapememory PSMS50 actuators. The fabrication process involved the useof a custom-built twister-coiler device and thermosetting at a highertemperature of 120 °C, enabling the demonstration of repeatablethermal actuation. The 2W SM actuation of PSMS50 coils and the integratedheat-responsive fabric are depicted in Video S1, Video S2, Video S3, and Video S4. The choice ofusing coiled actuators is based on amplifying the thermally inducedtorsional motion of twisted filament when the filament is coiled,as explained previously by Haines et al.5 Additionally, Owing to these biocomposites exhibitedtwo distinct thermal transitiontemperatures, Video S3 and Video S4, showed that the shape-changing deformation initiatedat approximately 50 °C, with the greatest range of movement achievedat around 100 °C. Notably, the fabric endured 5 heating/coolingcycles without any change in the actuation/recovery performance, confirmingthe durability of this smart textile.Upon heating, the shape-morphingfabric exhibited rapid and significantheat-induced deformation, followed by successful recovery to its initialdimensions during the cooling process. Infrared thermal camera recordings,along with The cotton yarns incorporatedinto the fabric provided the necessaryflexibility to the top layer, facilitating contraction and expansionof the actuators. In essence, the cotton yarns, along with the plainweave structure, synergistically complemented and facilitated theactuation of the PSMS50 actuators within the textile network. Thisdesign allowed for the heat-driven alteration of a 3D textile andcould be further utilized to reveal other hidden functionalities offunctional yarns integrated into the nonactuated textile structure.Consequently, this construction concept enables the gradual exposureof new textile functionalities based on temperature variations.In summary, the integration of the developed shape memory polymeractuators from the PSMS50 biocomposite into the textile structureshowcased significant and heat-induced deformation. This approachprovides a straightforward pathway for designing dynamic shape-changingtextiles that enhance the capabilities of the current smart textiles.g-starch, including a highconcentration of 70 wt %, were successfully melt-blended with thecopolyamide matrix at a relatively low processing temperature of 160°C. The developed biocomposites were thoroughly characterized,and it was observed that the OSA-g-starch disperseduniformly within the polymer matrix, resulting in improved interfacialadhesion and good compatibility. The biocomposites exhibited excellentstiffness/toughness balance, along with remarkable rheological propertiesand processability, even at a high filler loading of 70 wt %. Moreover,the shape memory and shape recovery properties of the pure polymerand biocomposites, including those with 10 and 50% OSA-g-starch, were evaluated through cyclic thermomechanical testing,confirming their outstanding shape recovery capabilities. Importantly,it was demonstrated that the shape memory behavior of the polyamidecould be tailored and the actuation temperature could be reduced byincreasing the starch particle content. In conclusion, this work presentsthe concept of developing responsive fabrics by integrating thermoresponsiveshape memory polymers into textiles, thereby adding value to traditionaltextiles available in the market and paving the way for the advancementof smart and functional textiles.This study highlights the potential of utilizingbiobased materialsto address sustainability concerns associated with petroleum-basedfibers in functional textile applications. A series of biocompositesconsisting of surface-modified starch and polyamide were successfullydeveloped. To prevent the thermal degradation of starch particlesduring the melt blending process, a novel low-melting-point copolyamidewas synthesized by incorporating monomers with different alkyl chainlengths through copolymerization. The resulting copolyamide exhibiteda significantly low melting point of 135 °C. To enhance the compatibilitybetween starch particles and the copolyamide matrix, a simple, solvent-freemethod involving the grafting of octenyl succinic anhydride (OSA)onto the starch surface through esterification was employed. The successfulgrafting of the OSA was confirmed through various analyses, includingFTIR, TGA, elemental analysis, and SEM images. Subsequently, differentconcentrations of OSA-


Coastal marine ecosystems worldwide are increasingly affected by tide alterations and anthropogenic disturbances affecting the water quality and leading to frequent algal blooms. Increased bloom persistence is a serious threat due to the long-lasting impacts on ecological processes and services, such as carbon cycling and sequestration. The exploration of eco-environmental feedback and algal bloom patterns remains challenging and poorly investigated, mostly due to the paucity of data and lack of model-free approaches to infer universal bloom dynamics. Florida Bay, taken as an epitome for biodiversity and blooms, has long experienced algal blooms in its central and western regions, and, in 2006, an unprecedented bloom occurred in the eastern habitats rich in corals and vulnerable habitats. With global aims, we analyze the occurrence of blooms in Florida Bay from three perspectives: (1) the spatial spreading networks of chlorophyll-a (CHLa) that pinpoint the source and unbalanced habitats; (2) the fluctuations of water quality factors pre- and post-bloom outbreaks to assess the environmental impacts of ecological imbalances and target the prevention and control of algal blooms; and (3) the topological co-evolution of biogeochemical and spreading networks to quantify ecosystem stability and the likelihood of ecological shifts toward endemic blooms in the long term. Here, we propose the transfer entropy (TE) difference to infer salient dynamical inter actions between the spatial areas and biogeochemical factors (ecosystem connectome) underpinning bloom emergence and spread as well as environmental effects. A Pareto principle, defining the top 20% of areal interactions, is found to identify bloom spreading and the salient eco-environmental interactions of CHLa associated with endemic and epidemic regimes. We quantify the spatial dynamics of algal blooms and, thus, obtain areas in critical need for ecological monitoring and potential bloom control. The results show that algal blooms are increasingly persistent over space with long-term negative effects on water quality factors, in particular, about how blooms affect temperature locally. A dichotomy is reported between spatial ecological corridors of spreading and biogeochemical networks as well as divergence from the optimal eco-organization: randomization of the former due to nutrient overload and temperature increase leads to scale-free CHLa spreading and extreme outbreaks a posteriori. Subsequently, the occurrence of blooms increases bloom persistence, turbidity and salinity with potentially strong ecological effects on highly biodiverse and vulnerable habitats, such as tidal flats, salt-marshes and mangroves. The probabilistic distribution of CHLa is found to be indicative of endemic and epidemic regimes, where the former sets the system to higher energy dissipation, larger instability and lower predictability. Algal blooms are important ecosystem regulators of nutrient cycles; however, chlorophyll-a outbreaks cause vast ecosystem impacts, such as aquatic species mortality and carbon flux alteration due to their effects on water turbidity, nutrient cycling (nitrogen and phosphorus in particular), salinity and temperature. Beyond compromising the local water quality, other socio-ecological services are also compromised at large scales, including carbon sequestration, which affects climate regulation from local to global environments. Yet, ecological assessment models, such as the one presented, inferring bloom regions and their stability to pinpoint risks, are in need of application in aquatic ecosystems, such as subtropical and tropical bays, to assess optimal preventive controls. Algal blooms are a manifestation of abnormal changes in phytoplankton communities in aquatic ecosystems, such as estuaries and lakes ,2. DespiDespite this tremendous damage to aquatic ecosystems, the mutual influence between blooms and the environment has received little attention from scientists and policy makers. Algal blooms are, in fact, the byproduct of nitrogen (N) and phosphorus (P) change but can alter the N/P balance  and tempDespite the limited literature of the effects of blooms into the environment, some studies have explored the relationship between phytoplankton and water quality in bloom conditions , climatiThis type of biocomplexity study, such as the one we propose, would be necessary to define micro–macro feedback useful for risk assessment, management and policies aimed to minimize eco-environmental imbalances, leading to a decrease in ecosystem health, such as due to blooms. Blooms are the epitome of marine ecosystem health because their emergence is largely related to altered ecohydrological factors at the basin-scale, from land and ocean, leading to quick and persistent increases in phytoplankton with short-term impacts  and longMarine microbial food webs consist of heterotrophic protists, phytoplankton, prokaryotes and viruses . Together, they are responsible for a large part of the production, respiration and nutrient transfer in oceans; they affect, for instance, the carbon cycle both in blue carbon habitats and in the ocean via the carbon pump. As marine ecosystems are increasingly affected by anthropogenic disturbances both from land and ocean, predicting ecosystem responses above critical environmental pressure relies on a better understanding community dynamics, including their composition, spatial/temporal distribution and interactions.Long-term observations are especially useful for this, and both Galbraith and Convertino  and GalbCoastal and marine ecosystems that experienced marine heatwaves, which were particularly significant in 2014–2015 worldwide, provide a unique opportunity to study how warming affects community dynamics  and how imbalance of the latter affects the environment back in the long term. The presented tool for ecosystemic risk assessment and the results from FL Bay are the main innovations of this paper.The topological network structure is an effective and intuitive way to describe the dynamical dependencies among diverse of analogous units of an ecosystem, or ecological communities composed of hundreds or thousands of populations of species ,19. ThisYet, causal network discovery and inference models   as well Connectomics is broadly defined as the study of structural and functional networks (the connectome), which are maps of a system (such as the nervous system), mainly in the brain; however, this concept has been extended to ecosystems  to charAs the connectome is the salient information of ecosystems, its knowledge allows one to improve predictive skills in the short and long term to represent ecosystem patterns. For the aforementioned needs, i.e., to detect the trajectories of spreading blooms and their potential environmental impacts, we demonstrate the capability of an information-theoretic approach to infer bloom networks and biogeochemical feedback. The optimal information flow model was developed initially for inferring species interaction networks in any ecosystem from abundance data  and was The ecological time series underpinning ecological dynamics are particularly important for assessing ecological states and early warning signals of shifts  before tThrough the model, we specifically infer and analyze the spatial ecological corridors determining bloom spread and direct interactions between CHLa and environmental factors to quantify the environmental effects of ecological dysbiosis; previous efforts (see Wang and Convertino ) focusedPreviously, CHLa has often been used as an indicator of blooms given its sensitivity to environmental changes, ease of monitoring and ability to reflect phytoplankton biomass effectively  but has Due to its unique lagoon configuration and climate, Florida Bay  regularlThe proposed TE network inference model that can be used for variable interaction discovery and prediction at multiple scales is explained. Its structure is graphically shown in The Florida International University Southeast Environmental Research Center (FIU SERC) established a water quality monitoring system of 28 spatially distributed stations in Florida Bay , where e−1 on CHLa, universally applied to all stations, to distinguish bloom from non-bloom states across all stations. Initially, the dataset for this study spanned 2004 to 2006, corresponding to before, during and after a severe bloom outbreak in Florida Bay in 2005 [We used a threshold-based quantile regression method  to esta in 2005  in terms in 2005 .Then, the dataset  was filtered to include only those months and stations with CHLa values exceeding the critical blooming threshold, i.e., those months and stations indicating sustained bloom conditions. As a result, the final dataset contained 18, 63 and 136 rows of measurements  for the 2004, 2005 and 2006 bloom periods , respectively. More generically, 2005 can be considered as epitomic of bloom outbreaks, while 2004 and 2006 are representative of early and post-bloom periods.X stands for all other environmental variables except for CHLa, and t. The specific TE chosen in Equation  that affect TE calculated on the pdf divergence and asynchronicity. The TE variability of an area, or the whole system, can be decomposed into eco-environmental interactions  and the ecological areal interactions underpinning bloom spread. This variability affects the organization propagation of CHLa  and, in an information-balance equation, can be written as the spatio-temporal convolution of the aforementioned components composing the ecosystem connectome, i.e.,H(CHLa)⏞eH(CHLa)⏞eEquation  is focusIn this paper, we specifically analyze the spatial ecological corridors determining bloom spread and direct interactions between CHLa and environmental factors  by quantifying the predictive information flow between them [Transfer entropy  as follows:X and Y at time X and Y up to X to Y, whereas Y to X.The information flow, and thus the predictive relationship between variables, is bi-directional. In this paper, we took the form of bivariate TE  and caldynamics , while lY are all other environmental factors for eco-environmental feedback in Equation  in contn et al. . For eacThe time series used to calculate the transfer entropy differences (Equation ) in thisd is the threshold value, or Pareto critical value, of significant causal relationships as necessary and sufficient to predict bloom dynamics. If X is a significant cause of Y; otherwise X is a weak cause of Y, or X and Y are mutually causal without any preferential direction in causality . Divergence and asynchronicity, as highlighted by Li and Convertino , are relSpreading can be related to marine currents; however, in this study, the purpose is not to define the precise mechanisms underpinning the ecological patterns but rather to define the patterns’ backbone networks, which are the salient spreading networks. This also identifies the potential coastal areas of influence of biogeochemical loads in FL Bay and the maximum extent of blooms—something that is poorly quantified but necessary for bloom prevention.In analogy, runoff in terrestrial basins are predicted equivalently to CHLa, where the amount (and distribution) of water in different locations changes in an asynchronous way and is dependent on river network spreading to define the timing and divergent volume. True causality, leaving aside the feasibility of its assessment, must be included considering all areas where CHLa can spread, which, in a bay, is virtually everywhere; however, this is challenged by the data limitation that is constrained only to the used stations in this case.The properties of network edges, representing eco-environmental interactions, depend on Equation ). The edEquation  suggest  in 2004 A with a  the bay B.In that year, the spatial influence was the strongest near stations 25, 16, 14 and 12 in the northwest and station 28 in the south region. The edge colors indicate that the bloom at all stations was moderately strong and also very likely to continue in the NE direction. Station 28 seems largely affected by many other stations in the bloom spread and yet is likely a sink node with potentially strong ecological effects also considering its proximity to the FL coral reef.The matrix for 2006 C shows tWe show how, by analyzing the information flow among spatially distributed nodes, it is possible to model the spatial spread of a phenomenon, such as algal blooms. In addition, this approach is able to detect sources, sinks, directions and salient pathways of bloom spreading. Due to various unaccounted factors, such as wind intensity and direction, current direction and bathymetry, there is a certain dynamic spatial change of blooms that is not attributed to the aforementioned factors. However, the model can take into account any environmental factor if available and can attribute the degree of variability of CHLa.In a complex network sense, the bloom spatial network in 2004 is small in scale and regular in topology but has an obvious active station (station 16) that is an actively connected hub for bloom spreading. Therefore, it is much easier to take measures against blooms at this time (whether possible) or to prevent triggers by controlling environmental determinants. This area is well-know to be heavily influenced by nutrient efflux from the Everglades .Particularly with the outbreaks of blooms in 2005 and later in 2006, the network has many more areas that are very active and affected, yet bloom management becomes more difficult. Over time, a spreading network transition is observed from a regular/small-world in 2004, to scale-free in 2005 and regular (or uniform) topology in 2006 with long-range connections.The investigation of the impact of CHLa extremes  on ecosystem health is a poorly covered topic in science. To explore how algal blooms impacted the water quality in Florida Bay, we analyzed how CHLa impacted other water quality variables using TE see . We focuAt the most active station in 2004  with a higher impact on the latter B. In 200At stations 3 and 6 , blooms induced changes in the water temperature, salinity, total phosphorus and turbidity, while, at stations 2 and 5 (characterized more by submerged marshes), blooms led to substantial fluctuations in the total nitrogen, total phosphorus, salinity and turbidity. Information flow patterns (TE patterns) suggest that blooms first strongly caused water temperature alterations, then enhanced the salinity and nitrogen and later impacted other nutrients (phosphorous) and the turbidity. This is aligned with an understanding of the underlying microbiological processes .In the vicinity of station 2, blooms caused a large change in salinity, while the effects on TN, TP and TURB were less significant. As blooms are a manifestation of eutrophication in water bodies, large amounts of phytoplankton cause dramatic changes in the total phosphorus and turbidity, such as near station 3, with a minor influence on the temperature and salinity. Around station 5, the bloom had a strong influence on the turbidity and salinity, with a minor impact on the TN and TP due to the deeper water in this area.Despite the bloom near station 6 being relatively short, it still caused elevated changes in both the salinity and turbidity and, in a minor way, in the water temperature and total phosphorus. In general, the occurrence of blooms had serious effects on the total phosphorus, salinity and turbidity in the eastern zone of Florida Bay; a worrisome condition because of the highly valuable biodiversity in that area comprising a wide set of sponge, fish and coral species.Our results reveal that algal bloom severity also caused environmental degradation a posteriori beyond the direct causal effects of environmental change (particularly from temperature in the ocean and nutrients from estuarine efflux) in triggering blooms a priori. Certainly, the primary causal pathway is about temperature leading to CHLa changes; however, the inferred networks also manifests the feedback of CHLa change on temperature. While this can be minor with respect to the first mechanism, it is also possible in relation to algal overgrowth and local temperature increase.This substantiates environmental changes due to ecological imbalances , such asRising temperature, also related to local eutrophication, implies more evaporation from waterbodies and yet higher salinity if the hydrology is not changed. Of course, if the algae grow overly much (in term of biomass), a large amount of oxygen is depleted when they die, and this creates hypoxia and cascading risks, such as the death of species and the emergence of toxins. This can also lead to an exceeded capacity of zooplankton to sink carbon to the bottom of the ocean and, thus, an increase in the size and frequency in blooms, which is not good for the generated temperature, which is a co-occurring risk factor.OTE quantified the extent to which blooms around one area can predict CHLa dynamics  in other areas: higher OTE values indicate higher area interactions with higher spreading and predictability. In 2004, the OTE ranged between 0 and 1.7; most values with a non-zero probability were between 0 and 0.6.We explored the interaction dynamics of blooms by analyzing the annual probability distribution, or pdf, of the outgoing transfer entropy OTE; see  pre-, peOTE but a high probability of low OTE. The pdf is bimodal with a leptokurtic character. In an ecological sense, the dynamics are characterized by highly localized blooms and few traces of bloom emergence in other areas. Thus, the bloom spatial network system was relatively contained in 2004 and corresponds to a regular/small-world topology  and the largest spreading network (which was in 2005) signifying potential long-term effects.By flipping the pdf, it is possible to obtain information on the ecosystem potential landscape about the energy dissipation, likelihood of shifts and relative stability of bloom conditions . The eneThe energy potential also gives the number of ecological states  with TE differences to infer bloom spatial dependencies, which were used to pinpoint risk areas and pathways to target monitoring and controls. Blooms showed non-trivial spreading patterns manifested by network transitions with different stability results that determined their persistence and potential ecological effects. For FL Bay, we predominantly highlighted the spatial trends and the neglected impacts of algal blooms on the water quality. The following specific results are worth highlighting.We showed how CHLa patterns carry information regarding the underpinning ecohydrological networks  that support ecosystem function and services. Salient Pareto interactions were defined via thresholding TE differences with a threshold of causal significance that was set to consider the top 20% of TEs , i.e., necessary and sufficient interactions to predict the risk of bloom spreading.More generally, the discovery and inference of the ”ecosystem connectome”  allows for the assessment of ecosystem health  as well as the investigation of causal determinants and their sources, proximity to ecosystem shifts and targeted ecohydrological controls.Through spatial analysis of bloom spreading networks, we showed how regions not previously involved in blooms  were caused by large imbalances of CHLa in the western and central blooms, which were causally involved. The latter regions were characterized by CHLa that was more randomly distributed and a higher probability of CHLa extremes. This probabilistic structure, reflecting the spatial distribution of CHLa, is likely tipping eastern regions to similar bloom endemics. From the perspective of complex networks, this bloom event (2004–2006) evolved from a spatial network with a localized trigger area and a small-world topology to a random topology with long-range spatial diffusion.In 2005, when most stations were blooming, the spatial spreading network was scale-free  withIn terms of temporal dynamics, subsequent to the first bloom outbreak, persistent and recurring blooms were observed for several NE areas with long-lasting environmental impacts on turbidity and salinity aggravated by temperature increases. Bloom sources were related to central coastal marshes and, to a lower extent, mangrove habitats. We further showed that blooms were a recurring and persistent phenomenon over a long period of time with continuous outbreaks in interdependent regions. This led to higher energy dissipation and larger instability dictated by the more random distribution of CHLa, which was associated with a more uniform network with long-range connectivity regardless of habitats, likely leading to the loss of ecological heterogeneity.The analysis of biogeochemical factors affecting water quality showed that the occurrence of blooms could only affect small fluctuations of temperature at the beginning of the blooms; however, repeated bloom outbreaks largely affected other biogeochemical factors  that are poorly systemically controllable due to the loss of vegetation and other keynote species.The concentration of CHLa can be influenced by temperature and salinity, and changes in the CHLa concentration can, in turn, have indirect effects on water temperature through various ecological processes. In some regions, facilitated by shallow-water habitats, a water temperature increase can stimulate phytoplankton growth and increase the concentration of CHLa. The increased CHLa can, in turn, absorb more sunlight, which can lead to local warming of the water.In the long term, the persistence of blooms, i.e., high CHLa, may also alter nutrient cycling as highlighted by other studies with the term “oceanic positive feedback mechanism” , and ourAlthough the intensity, duration and spatial distribution of blooms are governed by a multiplicity of factors, CHLa variability (independently of any trigger) still has a wide degree of predictability and control in an ecosystem perspective considering both predictive and ecological engineering models. We proposed a data-based inferential model to be used for ecological intelligence to look into patterns of risk (source and pathways), trajectories and determinants.Our proposed spatial and biogeochemical network inference model provides valuable information for the forecasting and management of blooms—for instance, by pinpointing monitoring and nature-based solutions in source areas, such as coastal blue-carbon habitats to inhibit progressive eco-environmental imbalances and the related impacts. Further work will look into the precise quantification of critical thresholds  as early warning signals of environmental factors (including controls) that lead to persistent blooms and accounting for systemic stress, that is reflected by the condition of habitats as their ecological history.


The transcription factor BarH-like homeobox 2 (BARX2), a member of the Bar-like homeobox gene family, is involved in cell proliferation, differentiation, immune responses and tumorigenesis. However, the potential role of BARX2 in the development of liver hepatocellular carcinoma (LIHC) remains unclear. Therefore, we aimed to study the biological role of BARX2 in hepatocellular carcinoma. Through the UALCAN, GTEx PORTAL, TIMER 2.0, LinkedOmics, SMART, MethSurv, Metascape, GSEA and STRING public databases, the BARX2 mRNA level, prognostic value, coexpressed genes, associated differentially expressed genes, DNA methylation and functional enrichment of LIHC patients were studied. The relationships between BARX2 expression and various clinical or genetic parameters of LIHC patients were determined using data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and BEAT LIHC databases. In addition, the biological function of BARX2 in LIHC was studied in vitro. Through large-scale data mining, our study showed that BARX2 was differentially expressed between different normal and tumour tissues.BARX2 expression in LIHC tissues was significantly lower than that in corresponding controls, especially in patients with T2-4 stage disease. In patients with LIHC, overexpression of BARX2 was an independent poor prognostic factor associated with poor cytogenetic risk and gene mutations. Genomic hypermethylation of the BARX2 gene was associated with upregulated BARX2 expression and poor overall survival (OS) in LIHC. Functional enrichment analysis showed that BARX2 had an immunomodulatory role and was involved in the inflammatory response in LIHC occurrence. In conclusion, the oncogene BARX2 may serve as a new biomarker and prognostic factor for patients with LIHC. The immunomodulatory function of BARX2 deserves further validation in LIHC. While hepatocellular carcinoma is one of the major types of liver cancer, it is also the second most malignant disease causing cancer-related death worldwide2. WHO surveys show that by 2025, approximately one million people will be affected by liver cancer each year3. Due to the early dissemination of LIHC to the liver, this disease is usually multinodular at clinical diagnosis and LIHC tumour cells have a marked affinity for intravascular growth and can invade the portal vein or hepatic veins4. In recent years, tumour immunology has received increasing attention in the context of LIHC pathogenesis, and studies have shown that immunosuppressive molecules are effective in hepatocellular carcinoma8. In addition, some scientists have demonstrated that by targeting the signalling pathways of tumour angiogenesis and tumour proliferation, the survival time of patients can be prolonged; in summary, tumour immunotherapy has reshaped the clinical treatment prospects of hepatocellular carcinoma10.Liver cancer is a global public health problem, with an annually increasing incidence rate11. To date, two members of the BARX protein family have been identified in mammals, named BARX1 and BARX2 for the order in which they were discovered12. They are widely expressed in a variety of tissues; perform diverse biological functions in cell proliferation, differentiation, growth factor signalling, transcriptional regulation and embryonic development; are key regulators of the respiratory, circulatory, digestive and immune systems; and are implicated in inflammation, cancer and other diseases16. BARX2 was discovered in 1996 by Jones et al. in the process of screening a mouse embryo expression library, and this new homologous cassette gene is similar to Barx1 and the Drosophila Bar gene, hence the name Barx213. BARX2, also known as barh-like homodimer 2, is located at 11q24-q25 and encodes a homologous transcription factor consisting of 254 amino acids17. The gene encoding human Barx2 maps to chromosome 1, 1q25, with four exons ranging from 85 to 1099 bp18. BARX2, a homologous structural domain factor of the Bar family, regulates factors that control the expression of cell adhesion molecules and affects cell differentiation in various developmental contexts21. Dysregulation of BARX2 and its oncogenic role in the development of various tumours such as ovarian, gastric and lung cancers have been reported24. However, the role of BARX2 in hepatocellular carcinoma is very poorly understood. Here, we conducted an in-depth study on the expression of BARX2 in hepatocellular carcinoma and its prognostic value. Furthermore, the clinical significance, potential molecular functions and regulatory networks of BARX2 in LIHC patients were investigated by bioinformatic analysis of datasets available from public databases.BARX (BarH-like homeobox) is a class of transcriptional regulators encoding transcription factors containing homologous structural domains, and is named because of its similarity to the Bar subclass of homologous structural domain proteins involved in Drosophila eye developmentFirst, we used a GTEx dataset to analyse the expression levels of physiological BARX2 genes in different normal tissues. As shown in Supplementary Fig. Subsequently, we compared BARX2 expression levels between tumour and normal samples from 33 cancers in the TCGA and GTEx datasets Fig. . Except We investigated the genetic alterations in the BARX2 gene in different types of tumours in the TCGA dataset on cBioPortalWeb. As shown in Fig. Subsequently, we investigated the BARX2 DNA methylation status of LIHC patients from the TCGA dataset using the SMART App and the MethSurv database. For probes in the promoter region of the BARX2 gene , the BARX2 DNA methylation level  did not correlate with the expression level of BARX2. However, we observed that hypermethylation of the BARX2 gene body region  was positively correlated with BARX2 gene expression , we found that BARX2 expression was significantly associated with OS in patients with nine cancer types Fig. A–I. In pKaplan‒Meier curves and log-rank tests showed that the OS of LIHC patients with high expression of BARX2 in the TIMER 2.0 database was significantly better than that of LIHC patients with low expression, and a Cox proportional risk model was used to confirm the potential of BARX2 as a prognostic factor for LIHC patients from the TCGA dataset. Univariate Cox regression analysis showed that BARX2 expression , T stage  and pathologic stage  were associated with OS , BARX2 expression was negatively correlated with EMT and invasion. In contrast, BARX2 expression was positively correlated with inflammation. BARX2 expression in glioblastoma multiforme (GBM) and uveal melanoma (UM) was negatively associated with almost all tumour biological behaviours, such as DNA repair, DNA damage, invasion and metastasis. BARX2 expression in lung adenocarcinoma (LUAD) was positively associated with almost all tumour biological behaviours, such as angiogenesis, differentiation, EMT and hypoxia Fig. A. We use27. Dysregulation of BARX2 is associated with infectious diseases and cancer31. However, there are few studies on the role of BARX2 in the occurrence of LIHC28. Therefore, in this study, the expression and prognostic value of BARX2 in multiple cancers was comprehensively explored using publicly available datasets. In addition, the clinical significance and role of BARX2 in LIHC were explored for the first time by bioinformatic analysis and functional analysis. There were some observations in this study.Currently, the clinical diagnosis and treatment of hepatocellular carcinoma are well established; hepatocellular carcinoma, the main type of liver cancer, has a poor prognosis with a five-year survival rate of no more than 20% due to its potential for recurrence and metastasis. Therefore, there is an urgent need to explore new biomarkers to improve the prognosis of patients with hepatocellular carcinoma. Numerous studies have shown that BARX2 plays important regulatory roles in processes such as inflammation, angiogenesis, cell proliferation and differentiation33, the current study shows that BARX2 is also expressed downregulated in different types of tumours such as breast, gastric and colon cancers31, suggesting that BARX2 expression is tissue specific. However, Stevens and Meech et al. found increased expression of BARX2 in breast cancer tissues31, with upregulation of the expression of MMP9 and inhibitor of metalloproteinases 4 (TIMP4), which ultimately promoted invasion and migration of breast cancer cells, contradicting our current results, probably due to differences in the tumour tissue subtypes studied. On the other hand, the prognostic value of BARX2 expression in cancer patients has been rarely reported thus far34. In addition, a PPI network of BARX2-related partners was constructed, and 66 hub genes were ultimately screened. Among these hub genes, 14 genes had an impact on the OS of LIHC patients  and uveal melanoma; however, expression in lung adenocarcinoma (LUAD) was positively correlated with tumour biological behaviour Fig. A. Our co38. However, its specific mechanism of action and clinical application need to be further validated.MethodsIn this study, the clinical significance and role of BARX2 in LIHC were first investigated by bioinformatic analysis, and the role and mechanism of BARX2 in LIHC occurrence were predicted by enrichment analysishttps://xena.ucsc.edu/). We used Toil software to process the raw RNA-seq data and extract BARX2 gene expression data from the GTEx and TCGA datasets for subsequent analysis39. The gene expression profiles of the GSE36376 dataset in the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/), containing RNA-seq data for 433 LIHC patients40, and the Beat LIHC dataset (http://www.vizome.org) were also used to investigate the relationships between BARX2 mRNA expression and clinicopathological factors in LIHC patients. Expression analysis of BARX2 in tumour and normal tissues was performed with The UALCAN database ; the dataset contained RNA-seq and clinical data for 33 cancer types in the TCGA dataset41. We used it to analyse the expression of BARX2 in different types of tumour samples and to download box plots from the UALCAN website. The GTEx portal contained RNA-seq data for 54 nonlesional tissue loci from nearly 1000 individuals42. We used it to analyse the expression of BARX2 in different normal tissues and download box plots from the GTEx portal. To study the difference in BARX2 expression between tumour and normal tissues from the GTEx and TCGA datasets, if each group of data was normally distributed, a two-sample Student’s t test was used; otherwise, the Mann‒Whitney U test was used. RNA-seq data were normalized by log2 transformation. We normalized RNA-seq data by log2 transformation and analysed the data with IBM SPSS Statistics 28 software.We downloaded The Cancer Genome Atlas (TCGA) gene expression RNA-seq data for 31 normal tissues  and the Genotype-Tissue Expression (GTEx) gene expression RNA-seq data using UCSC Xena 43, compared BARX2 expression levels among different cancer cell lines and downloaded box plots from the CCLE website.We obtained RNA-seq data, DNA methylation data, gene mutation data and copy number data for human cancer cell lines from the CCLE database (http://timer.comp-genomics.org)44 is commonly used to explore the prognostic significance of genes in different types of cancer. We used this database to explore the prognostic value of BARX2 expression on overall pancancer survival. Kaplan‒Meier survival analysis and the log-rank test were used to calculate p values45.TIMER 2.0  for pancancer analysis of BARX2 gene alterations according to the online instructions47. The results of genetic alteration characteristics of the BARX2 gene, such as frequency, mutation type and copy number alteration (CNA), among different tumours in the TCGA database are displayed in the “Cancer Types Summary” module of the cBioPortal website.We used the cBioPortal website (http://www.bioinfo-zs.com/smartapp/)48 and the MethSurv database (https://biit.cs.ut.ee/methsurv/)49, which contains Infinium Human Methylation 450 K BeadChip data, RNA-seq data, and clinical data for 33 cancer types from the TCGA dataset. The relationships between BARX2 DNA methylation levels or gene expression and the prognostic value of OS in LIHC patients were then investigated.We analysed BARX2 DNA methylation levels in LIHC patients through two public databases, the Shiny Methylation Analysis Resource Tool (SMART) App database (http://www.linkedomics.org/login.php)51 to detect coexpressed genes associated with BARX2 expression in RNA-seq data for LIHC patients in the TCGA cohort. We calculated Pearson correlation coefficients and generated volcano plots for the coexpressed genes on the LinkedOmics website. The heatmap we generated via the Limma package in R 4.2.2 shows results for the coexpression of BARX2 and immune-related genes.We used the LinkedOmics database (http://bioinformatics.psb.ugent.be/webtools/Venn/)52 to explore the overlapping genes between the DEGs and coexpressed genes, which laid the foundation for further analyses.We divided the LIHC patients from the TCGA dataset into two groups (BARX2 low and BARX2 high) based on the median level of the BARX2 gene from the RNA-seq data. We used the Limma package in R4.2.2 to screen and plot DEGs in volcano plots comparing the BARX2-low and BARX2-high groups of LIHC patients. Then, we used the Draw Venn diagrams online tool (http://metascape.org/gp/index.html#/main/step1) to perform functional enrichment analysis of the screened overlapping genes, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and tissue enrichment analyses56.We used the Metascape database (www.broadinstitute.org/gsea)57 to perform gene set enrichment analysis (GSEA) of the screened overlapping genes and identified abundant signalling pathways associated with LIHC60. For all GSEA analyses, “c2.cp.kegg.v7.1.symbs.gmt” was selected from the MSigDB genome as the reference genome. We performed statistical analysis of the enriched signalling pathways with normalized P<0.05, false discovery rate (FDR) q<0.25, and normalized enrichment score (NES) >1.5 used to define statistically significant enrichment.We used the GSEA v4.1.0 database (https://string-db.org/) for PPI network analysis to build a PPI network based on the DEGs in the circRNA–miRNA‒mRNA network61. Visualization of the network was performed using Cytoscape. Then, the plugin MCODE in Cytoscape_v3.9.1 software was used for visualization of PPI networks and identification of hub genes in the PPI networks64. “MCODE” is a Cytoscape plugin that has been used to identify important modules and hub genes with the cut-off criteria of degree ≥ 2 and k-core ≥ 2.We used the STRING database (http://timer.cistrome.org/), which includes multiple algorithms such as TIMER, EPIC, MCPCOUNTER, XCELL and TIDE, to assess the correlations of BARX2 expression with the levels of immune infiltration in different TCGA cancers.We applied the TIMER 2.0 tool 64 was used to collect information on the frequency of alterations, mutation types, mutation sites and three-dimensional (3D) structures of candidate proteins in all TCGA tumours. For single-cell datasets, the TISCH2 tool (http://tisch.comp-genomics.org/home/) provides meta-information, cell type annotations, expression visualization and differential gene expression information.cBioPortal  classification, sex, cytogenetic risk, and chromosomal alterations. A t test or ANOVA was performed to evaluate intergroups differences in continuous variables with a normal distribution such as age, BARX2 gene expression level, cell cycle assay results, and Annexin V-APC and PI staining results. Otherwise, the Mann‒Whitney U test or Kruskal‒Wallis test was used. All P values were bilateral, and P < 0.05 was considered statistically significant. We used IBM SPSS 28.0 to perform statistical analysis .We used the χSupplementary Legends.Supplementary Figure S1.Supplementary Figure S2.Supplementary Figure S2.Supplementary Figure S4.Supplementary Tables.