Liquid crystalline network (LCN) actuator normally deforms upon thermally or optically induced order-disorder phase transition, switching once between two shapes (shape 1 in LC phase and shape 2 in isotropic state) for each stimulation on/off cycle. Herein, we report an LCN actuator that deforms from shape 1 to shape 2 and then reverses the deformation direction to form shape 3 on heating or under light only, thus completing the shape switch twice for one stimulation on/off cycle. The deformation reversal capability is obtained with a monolithic LCN actuator whose two sides are made to start deforming at different temperatures and exerting different reversible strains, by means of asymmetrical crosslinking and/or asymmetrical stretching. This desynchronized actuation strategy offers possibilities in developing light-fueled LCN soft robots. In particular, the multi-stage bidirectional shape change enables multimodal, light-driven locomotion from the same LCN actuator by simply varying the light on/off times. Liquid crystalline network (LCN) actuators switch reversibly between two states. Here, the authors report an asymmetrically processed LCN actuator which allows desynchronized actuation between three states using light and thermal stimuli. By adjusting the alignment of mesogens and/or the distribution of crosslinking domains, monolithic LCNs can display a wide range of predesignated deformations, including those based on contraction/extension, bending, twisting and their various possible combinations19. Accordingly, a myriad of complex shapes 29 as well as robotic and bionic motions 40 have been achieved, making the field flourish. Up to date, the reversible shape change of LCNs only involves two shapes corresponding to the isotropic state (disordered state) and the LC phase (ordered state), respectively. The shape evolution between these two states is typically unidirectional, which means that, for example, a ring (shape 1 in LC phase) evolves directly into a flat strip (shape 2 in isotropic phase) upon heating  have become a particularly promising material system for intelligent actuators and soft robots, thanks to their ability to reversibly and macroscopically deform upon the order-disorder phase transition, when properly processeding Fig. , generating Fig. . Due to 43. Generally, when light is on, a flat strip bends first and then flattens. This is known to originate from the formation at short light-on times and the subsequent elimination at prolonged illumination times of a stress gradient along the thickness direction of the strip, which can be induced photochemically or photothermally through the use of photoswitches or photothermal agents in LCNs, respectively. However, in all cases, as the light-induced gradient and its variation over time is essential, when light is off, the strip cannot bend and flatten again, because the cooling is uniform for photothermally-based actuators, or because the photoreaction is ceased for photochemically driven actuators42. As shown below, this apparent deformation reversal (bending/unbending), which is the intermediate state as the LCN sample moves to the equilibrium state under irradiation, is different from the built-in deformation-reversal capability explored in the present study.One may notice that, the reversal of the bending direction under continuous illumination was reported for some photomechanical actuatorsIn this study, the reversible deformation-reversal actuation is achieved without the necessity of having a temporal, light-induced stress gradient. We adopt a strategy based on desynchronizing the thermomechanical responses of the two sides of a monolithic LCN by structurally implementing different actuation temperatures and forces, which, we show, can be obtained through regulating the photocrosslinking times and/or draw ratios of the two sides. Since the deformation-reversal behavior stems from structurally inscribed desynchronized actuation, it occurs when the LCN is subjected to uniform heating and can be observed in a thermal equilibrium state. Using this strategy, it is possible to adjust or control the deformation-reversal actuation in terms of the magnitude and the shape-changing directionality. The LCN actuators can also be programmed by photopatterning or through offsetting LC alignment to display wave- or helix-based deformation-reversal behavior under uniform stimulation. All these features are hard, if not impossible, to realize through the formation of a temporal thermomechanical  gradient. The deformation-reversal capability of LCN actuators, highlighted as an exploitable property in our study, offers possibilities in developing soft robotic applications. As a proof of concept, by taking advantage of the fact that one deformation-reversal actuation cycle can be divided into multiple stages, we prepared LCN actuators that can execute light-driven locomotion in multiple fashions  by tuning only the light-on/off times for desired temperature variations.21, are detailed in −2), the polymer was photocrosslinked through the generated cinnamyl dimers, thereby locking the oriented mesogens for the reversible deformation . Interestingly, the crosslinking density can affect the LCN’s order-disorder phase transition and further influence the actuation behavior. Figure LC-I), and (2) the effect of disordering the LC order by the crosslinks (acting as defects), which tends to reduce TLC-I45. As for the broadening of the phase transition region, it can be caused by both polymer chain stretching and crosslinking. As previously reported46, stretching of the LCN can suppress the smectic phase in the polymer and emerge the initial smectic-nematic and nematic-isotropic phase transition peaks into a broad nematic-isotropic peak. Furthermore, increasing the crosslinking degree of unstretched polymer appears to exert a similar effect due to the chain constraints that hinder the smectic layer formation , their length changes were recorded using a dynamic mechanical analyzer in the iso-stress mode  . Figure 1 during cooling from 75 to 40 °C, which can be explained by the crosslinking-dependent actuation behaviors shown in Fig. 1 undergoes both deformation I and II stages having opposite bending directions and realizes the transformation among three different shapes. On subsequent heating, the actuator executes the opposite deformation-reversal steps  followed by crosslinking both sides of the film for different times, thus crosslinking degrees. The prepared actuators are coded as LCN Figure 10,120 dur49, for a bending angle comparison to be meaningful in characterizing the deformation-reversal behavior, we kept similar length and thickness for the different LCN actuators  can only bend unidirectionally during cooling. In contrast, the three samples crosslinked on both sides all exhibit deformation-reversal behavior, i.e., bidirectional bending. As the crosslinking-time difference increases, the deformation-reversal behavior becomes more pronounced with larger changes in bending angle during deformation I ( | dαI | ) and deformation II ( | dαII | )  and variable crosslinking time of side A . The change in bending angle of the LCNs as a function of temperature is shown in Fig. 20 min an| ) Fig. . Further| ) Fig. . Further| ) Fig. –5, a grator Fig. . As can I | and |dαII | decreased slightly, which could be attributed to some unrestored order during actuation under the used conditions, the initial performance can be recovered by simply setting the actuator in air at room temperature for a couple of hours. Some other factors, such as film thickness, stretching temperature and draw ratio , have been investigated to further verify the decisive role of asymmetrical crosslinking in imparting the LCN actuator with the deformation-reversal capability. When fixing the crosslinking times at 20 and 120 min for the two sides of the strip, respectively, the deformation-reversal behavior was observed for all samples investigated , although the performance of the actuator varies . However, it is hard to determine precisely the crosslinking depth and the crosslinking gradient. For a given dimension of the LCN strip, the deformation-reversal behavior is governed by the competition between the lightly and highly crosslinked layers likely sandwiching an uncrosslinked layer in between. This competition could be a complex interplay of the strain difference, geometry change, mechanical properties , or α1 < 0, dαI < 0, dαII > 0 (if X > Y). This exposes the limitation of adjusting the deformation-reversal behavior only by changing the crosslinking times. This is because crosslinking not only determines the signs of dαI and dαII (the shape change direction), but also affects the signs of α1 (the shape in isotropic phase). In this regard, we further enriched the attainable deformation-reversal behaviors of our LCN films by not only generating crosslinking difference, but also creating draw-ratio difference on both sides of the LCNs. Typically, a strip was stretched to a draw ratio and one side was exposed to UV light for photocrosslinking; then the strip was stretched again to a larger draw ratio and the other side was photocrosslinked. In other words, the draw ratio on one side indicates the strain at which that side is photocrosslinked, though the final strain on both side is the same for the LCN strip. As shown in Fig. 2 . As seen from the photographs in Fig. 2 can complete a −360° to 360° to −360° deformation mode during one cooling run; LCN2 undergoes ring to flat to ring two-stage deformation. These LCN actuators all show deformation where α1 < 0, dαI > 0, dαII < 0. It is easy to notice that the α1 of LCNs with λ of side A greater than 400% are shifted to negative values, indicating that, as λ increases, the shape in isotropic phase can be changed from rolling toward side A to curling toward side B  were achieved. Among them, the |dαI | and |dαII | of LCN2 reach up to 770° and 735°, respectively  having its mesogens aligned −45° with respect to the principal axis of the strip shows a slightly twisted form in isotropic phase, which then winds up and unwinds in the following cooling process. Furthermore, Fig. Not limited to simple bending-based deformation-reversal behavior, we also achieved twisting-based deformation reversal using the LCN strip with tilted LC orientation relative to its long axis. As shown in Fig. 1. The sample is initially flat with the highly crosslinked side facing upward. After exposure to light (30.5 mW mm−2) for ~2 s, the film arches up with the right side as the stationary point; further exposure for ~1 s, the film flattens down with the left side as the stationary point. As such, the film already takes a step forward with the light spot on it. Ceasing the light, the film cools down and rearches up with the right side as the stationary point, followed by flattening down as it cools further. In this way, the film can implement two steps forward in one light-on/off cycle. Due to the doubled number of steps, the microwalker can move at a speed of ~2 body length min−1. Combining photopatterning with asymmetrical crosslinking for deformation-reversal behavior, we further achieved a wave-shaped LCN walker that can move in-plane with one edge on the substrate without “body” lifting in air , it can walk with the two end sections performing deformation reversal to adjust the friction during motion, while its middle part, being self-shadowed after curling up, displays only unidirectional deformation  (ca. 130-nm thick) was coated on the crosslinked LCNs to serve as photothermal agents , which results in its motion through body rotation . In mode II . Under this condition, the actuator moves through the unidirectional uncurling (light-on) and curling (light-off) movements, at a faster speed of 31 mm min−1. In Mode III . Moreover, when the actuator lying on its edge flips under light irradiation and has its planar side parallel to the surface, it can also rotate  Extra effort is needed for bonding the two active layers. (2) Even if two active layers are combined in one actuator, the two layers still need to be specifically regulated to satisfy the two criteria mentioned above to exhibit deformation reversal. Otherwise, the shape change process may be dominated by one of the active layers. (3) Well-tuning of the deformation-reversal behavior may still need to rely on adjusting the processing factors of the different layers, while tuning by changing the polymer structure or composition is more complicated. Nevertheless, in light of the present study, this is possible and remains to be realized in the future.The key to imparting a monolithic LCN actuator with the deformation-reversal capability is an appropriate desynchronization of the two sides in terms of actuating temperature (starting temperature and temperature range) and actuating strain (degree of reversible deformation). We show that this can readily be obtained by crosslinking the two sides to different crosslinking densities (varying photocrosslinking time) and/or by crosslinking the two sides at different mechanical stretching (elongation strain). Once prepared, the desynchronized actuation and the resultant deformation-reversal behavior are structurally inscribed in the LCN actuator, and it requires a uniform heating and cooling of the actuator over the thermal order-disorder phase transition to activate the actuation. This means that, in principle, the deformation reversal and its enabled intricate shape switch, twice bidirectional shape shifts on one stimulation on/off cycle as well as multimodal locomotion can be achieved by either direct heating/cooling, or light-on/off, or electric field on/off or magnetic field on/off, all this through a stimulation-induced thermal effect. A case worth discussing and comparing is the widely reported bilayer or multilayer actuators that naturally have different actuator sides. In addition to the use of two or more layers of often different polymers, generally, one side layer is a passive layer and used to adjust the mechanical response and/or to facilitate the shape processingIn conclusion, we report an atypical monolithic LCN actuator that can complete the shape switch (over order-disorder phase transition) twice in one stimulation on/off cycle, instead of once for known LCN actuators. Basically, in one reversible deformation cycle, the LCN actuator can possess four deformation stages with the deformation direction reversed three times, involving the conversion among three shape states. The required deformation direction reversal is obtained by applying the desynchronized actuation strategy, meaning that the two sides of an LCN strip are purposely prepared to start deforming at different temperatures and with different strains. We show that this property can be obtained through asymmetrical crosslinking of the two sides or through an asymmetrical stretching/crosslinking process . Introducing asymmetrical draw ratio on top of the asymmetrical crosslinking to the two sides of an LCN can further enhance the tunability of the deformation parameters and enrich the attainable deformation-reversal behaviors. Combining with the tilted mesogen alignment and photopatterning techniques, the deformation-reversal feature can be imparted to more sophisticated geometries, like helix and wave shapes. More importantly, this intriguing actuation characteristic can be exploited for light-guided locomotion of LCN soft robots. By simply adjusting the light-on and light-off times, which determines the deformation stages, the same LCN actuator can exhibit multiple modes of locomotion with varying speed. This study starts from the basis of the actuation mechanism of LCN actuators, but adopts another perspective to realize a distinct deformation feature that could potentially be extended to various LCN systems and beyond, which can greatly expand the soft robotics development in terms of fabrication, control, and operation.All used reagents, polymer synthesis and characterizations are described in −2) at 40 °C (above Tg) for X and Y min, respectively, for photocrosslinking. It should be noted that after the compression molding, the two sides of the film are marked as A and B, respectively. This is to standardize the operation details in the subsequent preparation process of the material as much as possible and reduce the error.Using a homemade device, the compression-molded polydomain LCP film (thickness 0.35 mm) was stretched uniaxially at 58 °C (in LC phase) to a draw ratio (λ) of 400%. Then the side A and side B of the film with uniaxial orientation of mesogens were exposed to UV light  was prepared by firstly crosslinking the side A of the unstreched polydomain LCP film for 20 min, then stretching the film to λ = 400% before crosslinking the side B for 120 min. Other LCN2 actuators were obtained through the two-step elongation and crosslinking procedures. By stretching the LCP film to λ = 200% or 300% and crosslinking the side A for 20 min, restretching the film to λ = 400% and irradiating side B for 120 min, LCN2 and LCN2 were fabricated. To prepare LCN2, LCN2, and LCN2, the LCP film was stretched to λ = 400% and side B of the film was crosslinked for 120 min, followed by restretching the film to λ = 540%, 700%, or 1000% and irradiating the side A for 20 min.LCN51. Dopamine hydrochloride  and Tris base  were dissolved in 100 mL of water. Afterward, the LCN actuators were immersed into the solution and stirred for 72 h  were obtained according to a literature methodSupplementary informationPeer Review FileDescription of Additional Supplementary FilesSupplementary Movie 1Supplementary Movie 2Supplementary Movie 3Supplementary Movie 4Supplementary Movie 5Supplementary Movie 6Supplementary Movie 7Supplementary Movie 8Supplementary Movie 9


Dr. Yi Liu is the facility director at the Molecular Foundry, Lawrence Berkeley National Laboratory. Trained as a supramolecular chemist and after a postdoctoral stay and working on click chemistry in Sharpless’s group he started his independent research career working on organic electronics, porous materials, and covalent organic frameworks (COFs). His aim is to develop nanostructured electronic materials through the design, synthesis and manipulation of tailor-made molecular constituents. Tell us a little bit about your research background and how you became interested in the chemistry of porous materials and COFs.I was trained as a supramolecular chemist in Fraser Stoddart’s group at UCLA during my graduate studies. Back then I used covalent and non-covalent bonds to organize electron donors and acceptors. My postdoc was about using click chemistry to make super-strong adhesive polymers in Barry Sharpless’s group. This group is known for pioneering very traditional organic chemistry. Later the group developed a different perspective on organic reactions in which they use very simple but highly efficient chemistry to make useful materials. I think that is a very important perspective. I developed a very strong interest in organic electronics since my graduate school and started working in this area when I moved to Berkeley lab. I became interested in COFs and in porous materials in general because I feel this is a reticular platform where charge carriers can be manipulated via the proper choice of building blocks and chemistry. I worked a lot with linear conjugated polymers. COFs emerge as an ordered, higher dimensional polymeric system which also have a very defined structure control. Particularly 2D COFs are reminiscent of other 2D materials such as graphene and transition metal dichalcogenides, where structure anisotropy plays a fundamental role. That is where I got interested and got into the field. The fact that the area is built off the concept of dynamic covalent chemistry is another reason why I am interested in COFs, as dynamic covalent chemistry represents the forefront of supramolecular chemistry and is one of my affections.Looking back on 15 years research on COFs, what were the highlights in COF research and which expectations remained unfulfilled?First of all, I think it has been a wonderful 15 years for COFs. Starting as a subfield of metal organic frameworks (MOFs) it now grows bigger and bigger and so does the impact. There are more excellent contributions than I can think of, but I will start with the seminal work 15 years ago on the original boronate ester COFs and later on the imine COFs. My personal favorite would be the growth of COF single crystals and the unambiguous structure identification by X-ray studies. That work is very challenging but was elegantly executed. The importance is apparent to me as a milestone in terms of confirming the extended high ordered COF structure. Before that, the closest was to use micro electron diffraction, which is a TEM technique, to resolve and model the structure but I think the single crystal work is particularly very remarkable.COFs are a class of light weight, ordered, porous organic polymeric material. Many reported functional COFs have taken advantage of the porous properties. There is greater expectation though, for COFs to outperform what is already out there, especially the closely related MOF family. I am quite excited about COFs being depicted as a conceptual framework that can guide the flow of mass, ions or charges, and I remain highly interested in seeing the development of semiconducting COFs and their integration in optoelectronic devices. That may bring new aspects to the table, complementary to what has been demonstrated by linear conjugated polymers. Such expectation however still remains unfulfilled.In your opinion, which challenges need to be tackled in order to move the field further?The first one came to my mind is the control of crystallinity. With few exceptions, the majority of COFs are semicrystalline powders. It is easier to see the crystalline part, but the amorphous components are usually hidden. In other words, COFs are often obtained as imperfect polycrystals with uncontrolled defects and tiny crystalline domains, which may be overwhelming and limit the material’s properties. The semicrystalline nature also poses a characterization challenge. XRD is currently the primary characterization method but it is not capable of providing localized information in smaller domains. On the other side it is laborious to use TEM and other more localized methods to characterize such a system. Characterization has been a challenge and the solution may lie in the discovery of new and improved chemistry to increase crystallinity. Still we only have around a dozen of reliable chemical reactions to make COFs, which dictates crystallinity and properties. Breakthrough in controlling the thermodynamic synthesis, either by new reactions or creative means to control the crystallization process, may bring us closer to better control the structural integrity, analogous to how carbon–carbon bond formation can be biased toward diamond production under the rightful conditions.Another challenge for certain applications is the poor solution processability. COFs are 2D or 3D polymers with periodic structures. In essence, they are crosslinked polymers, which imply that they typically have low solubility and are incompatible with solution processes. Depending on the applications, we need a clever way to deposit COFs on substrates or interfaces to overcome the processability barrier, which is not a problem for the linear polymer counterparts which have much better solution processability. This is especially important when it comes to optoelectronic applications.What needs to be done in order to make COFs more widely adopted and to get a foothold in industry and industrial applications?This is an important question to ask. I do feel the field fits more within the academic venue at the current stage. An upfront issue is the cost. Compared to traditional polymers or even to the closely related MOFs, the COF monomers are more like specialty chemicals. Chemists are able to design and make any monomer, but at the end the availability can be an issue. Another problem is upscaling. Most of the lab scale COF synthesis happens on the milligram scale, which is completely reasonable and fine for discovery purpose, though there is a missing link between the lab scale and the larger scale of a batch production. It also appears that the hydrothermal synthesis is quite sensitive to small changes of reaction conditions, which adds additional concerns for upscaling production or industrial applications.Another point I want to make is, that in order to advance COFs toward industry we still need to demonstrate a unique application of COFs which outperforms other materials candidates.Where do you see the field going next?I think it is all related to what we have discussed before. I am looking at it in two parts: structure wise we still need to find better ways to grow large area and more crystalline and less defective COFs, ideally with facile access to single or few layer structures. This might benefit from advances in other areas, in particular from advances in synthesis such as high throughput synthesis with automation or potentially autonomous synthesis. This might help to accelerate the discovery and to improve the structure integrity. The development of artificial intelligence and machine-learning algorithms might help with this process, if coupled with high throughput synthesis.Function wise, energy storage is one of the areas that COFs will continue to contribute to. This area would benefit from new carbon based porous materials as components in batteries or capacitors, electrodes, membrane separators, or solid state electrolytes. In my view these are territories where COFs might find a unique position. It is also potentially a greener materials source in terms of the element availability and environmental footprint.In terms of optoelectronics, much more needs to be demonstrated in order to find applications in this field. Personally, I like to see a high mobility COF based transistor or a COF based organic photovoltaic with a reasonable efficiency. These are areas where we see great advances based on linear conjugated polymers, and COF presumably can be a strong competitor if we find the right building blocks and smart ways to improve processability. That would be a big step toward application of COF materials in microelectronics or nanoelectroncis.The interview was conducted by Senior Editor Dr. Johannes Kreutzer.


H. sapiens structure. Docking of the substrate, intermediates, and product all resulted in negative binding energies, though the substrate had a lower energy than the others by 40%. The α-H of C10 was found to be 1.4 angstroms closer to FAD than the β-H, explaining previous reports of the reaction occurring on the meso face of the substrate. A lack of homology in sequence or length in the unstructured loop indicates a lack of function for the protein reaction. This docking study supports a reaction mechanism proposed previously whereby all hydride abstractions occur on the C10 of the tetrapyrrole followed by tautomeric rearrangement to prepare the intermediate for the next reaction.Protoporphyrinogen oxidase (PPO) is a critical enzyme across life as the last common step in the synthesis of many metalloporphyrins. The reaction mechanism of PPO was assessed in silico and the unstructured loop near the binding pocket was investigated. The substrate, intermediates, and product were docked in the catalytic domain of PPO using a modified Autodock method, introducing flexibility in the macrocycles. Sixteen PPO protein sequences across phyla were aligned and analyzed with Phyre2 and ProteinPredict to study the unstructured loop from residue 204–210 in the  These metals are coordinated with the pyrrole nitrogens at the center of these rings. Examples of biologically important metalloporphyrins include chlorophylls (Mg2+), hemes (Fe2+), vitamin B12 (Co2+), and coenzyme F430 (Ni2+).The porphyrin pathway plays a central role in the synthesis of many pigments fundamental to sustaining life across the biological realm ,2,3. MacBiosynthesis of porphyrins consists of 7 core enzymes that culminates in the action catalyzed by protoporphyrinogen oxidase (PPO) converting the colorless precursor protoporphyrinogen IX (protogen) into the fully conjugated, bright red pigment protoporphyrin IX (proto) . PPO itsmeso face (α-H) and tautomerism around the neighboring tetrapyrrole ring resulted in the formation of a double bond  forming the methylene bridge between the B and C pyrrole rings followed by tautomeric rearrangements . In ordeProto was obtained from x-ray analysis of protoporphyrin IX dimethyl ester by removing the methyl esters. The fully conjugated product of the reaction catalyzed by PPO is known to be a non-flexible planar structure and was docked in PPO without breaking the cyclic tetrapyrrole. The location of the catalytic domain was defined using a gridbox that encompassed the FAD forming the ‘roof’ of the cavity and, the top of α-8 helix of PPO upon which the tetrapyrrole rings are centered at the ‘bottom’ of the cavity, and Arg97 involved in stabilizing the rings on the right side of the cavity .After the docking, RMSD of the clusters with best poses for each tetrapyrrole intermediate provide insight to the changes in the curvature of the tetrapyrroles during the oxidation between protogen and proto . The RMSWhen docked in the catalytic domain the binding energy of protogen averaged −10.59 ± 1.08 kcal/mol . The binThe docking protocol allowed for the propionate groups of either ring C or D to interact with the guanidino functional group of Arg97 , all themeso hydrogen reported by Jones et al. [A key feature of the reaction postulated by Koch et al.  is that s et al. . − [Hydride abstractions are involved in many enzymatic reactions ,22,23. H− . Following the docking, an alignment was created of the top 80 related sequences to the human PPO sequence from the crystal structure used for this research to investigate the unstructured, flexible loop region near the catalytic domain, from residues 204–210. The flexibility and proximity could indicate that the loop has a function in the binding of the substrate or the release of the product. Residues that are conserved in homologous proteins between species and phyla are often associated with important function . AlignmeA novel docking model for protogen in the catalytic domain of PPO was created using a protogen structure derived from the crystal structure of a close precursor in the pathway, copro, bound to the catalytic domain of uroporphyrinogen decarboxylase. The intermediates in the pathway were created on the same backbone structure for docking, and the product proto was created based on previous crystallographic data. Binding energy for protogen was the most favorable with an average of −10.59 ± 1.08 kcal/mol, and the intermediates and product showed binding energies ~40% higher. The binding energies are consistent with the flexibility of the molecules, protogen being the most flexible and each intermediate thereafter reducing flexibility with the addition of double bonds. The docked molecules with the most energetically favorable positions all maintained an association between the propionate group of ring D and Arg98, which held the α-H of C10 within the appropriate range for redox reactions to occur with the N5 of FAD (3.6–3.8 Å). The findings here unify early experiments noting the hydride abstractions with the current understanding of the catalytic domain of PPO and known precursor conformations in the pathway. Finally, the unstructured loop near the opening to the catalytic domain was investigated for possible interaction with the substrate. A complete lack of homology, even within a phyla, indicates that no particular residue interacts with the substrate, but the conservation of the flexible region may indicate the necessity of accommodating substrate and product movement near the catalytic domain. Conformation of protogen was from derived from the structure of coproporphyrinogen III (copro) cocrystallized with uroporphyrinogen decarboxylase (1R3Y) . This stThe crystal structure of human PPO was obtained from 3nks . The atoProtogen and all of the reaction intermediates were docked into the catalytic domain of PPO using a modified Autodock method using Lennard-Jones potential and dummy atoms to overcome the software’s limitation  in handling flexible ring systems  12,13,3,31. ThisThe human PPO sequence was blasted in the NCBI database and the top 80 related sequences were selected. A second data set of 16 high quality PPO sequences spanning multiple phyla were selected to determine base homology . The two


Widespread neurocognitive impairment is well‐established in individuals at ultra‐high risk (UHR) for developing psychoses, but it is unknown whether slowed processing speed may underlie impairment in other neurocognitive domains, as found in schizophrenia. The study delineated domain functioning in a UHR sample and examined if neurocognitive slowing might account for deficits across domains.n = 38) or minimal antipsychotic exposure  and 50 matched healthy controls. Primary analyses compared group performance across neurocognitive domains before and after covarying for processing speed. To examine the specificity of processing speed effects, post hoc analyses examined the impact of the other neurocognitive domains and intelligence as covariates.The cross‐sectional study included 50 UHR individuals with no (ps ≤ .010), with medium to large effect sizes (Cohen's ds = −0.53 to −1.12). Only processing speed used as covariate eliminated significant between‐group differences in all other domains, reducing unadjusted Cohen's d values with 68% on average, whereas the other domains used as covariates averagely reduced unadjusted Cohen's d values with 20% to 48%. When covarying each of the other domains after their shared variance with speed of processing was removed, all significant between‐group domain differences remained .UHR individuals exhibited significant impairment across all neurocognitive domains (all Slowed processing speed may underlie generalized neurocognitive impairment in UHR individuals and represent a potential intervention target. The study delineated neurocognitive domain functioning in 50 ultra‐high risk individuals and 50 healthy controls, examining if slowed processing speed might contribute significantly to impairment in other neurocognitive domains in the ultra‐high risk group. The results showed that the ultra‐high risk individuals exhibited significant impairment across all neurocognitive domains, and processing speed was able to account for the significant between‐group differences in all other domains. Inclusion and exclusion criteria are listed in Table 2.2To assess if participants met UHR criteria, the Comprehensive Assessment of At‐Risk Mental States (CAARMS) was applied , as did a neurocognitive composite based on all six domains (α = 0.82). Table The Danish version of the National Adult Reading Test  based on means and standard deviations (SDs) of the healthy controls. Skewed and/or kurtotic distributions were approximated to normality by appropriate transformations  were truncated at −4 SDs to avoid undue distortion of group means and profile shape  to examine the association between MATRICS domain performances and group relationship. A significant multivariate effect of group by domain was followed up by Student's or Welch's t test, as appropriate. A two‐way mixed ANOVA tested whether the shape of neurocognitive profile differed between groups. Group was the between‐subject factor, and domains were the within‐subject factor, with a two‐way significant effect of group by domain interaction indicating a deviation from flatness in the UHR individuals’ profile, that is, that some neurocognitive domain scores are significantly different from each other. To examine the influence of processing speed on between‐group neurocognitive domain variability, primary adjusted analyses included multivariate and univariate analyses of covariance (MANCOVAs and ANCOVAs), controlling group domain performances for speed of processing. To examine the specificity of processing speed effects, post hoc analyses examined the impact of the other neurocognitive domains and intelligence as covariates.For group comparison of demographic and clinical characteristics, Student's t test or Mann–Whitney test, as appropriate, compared domain and overall composite performances in subsamples of UHR individuals that (had) received medication vs. those that did not.Potential confounds on neurocognition within the UHR group were investigated using Pearson or Spearman correlation analyses as appropriate . Welch's α ≤ 0.05. For ease of interpretation, the primary effect size reported for all between‐group comparisons of neurocognitive functioning is Cohen's d  was used for all analyses.Two‐sided significance level was set to d Cohen, . In ANCO33.1Information on demographic and clinical variables in the UHR and healthy control group is summarized in Table 3.2F = 5.989, p ≤ .001; Wilks' Λ = 0.721, d = −1.24. Univariate group comparisons confirmed significantly lower performances across all domains (ds = −0.53 to −1.12). A two‐way mixed ANOVA with a Greenhouse–Geisser correction did not detect a significant group by domain interaction, F = 1.534, p = .19, d = −0.25. This suggests that there is no deviation from flatness in the UHR group's neurocognitive profile relative to healthy controls. It indicates that no domain score is significantly different from any of the other domain scores; thus, there is a nonselective pattern of neurocognitive impairment across the domains in the UHR group. The UHR individuals performed significantly worse on tests measuring premorbid and current intelligence.Mean or median neurocognitive performance for each measure across groups is summarized in Table 3.3F = 0.969, p = .44; Wilks' Λ = 0.950, d = −0.46, suggesting that this domain may account for significant group differences in all other domains. In post hoc MANCOVAs covarying each of the other domains, that is, attention/vigilance, F = 2.896 p = .018; Wilks' Λ = 0.865, d = −0.79, working memory, F = 3.787, p = .004; Wilks' Λ = 0.831, d = −0.90, reasoning and problem solving, F = 5.249, p ≤ .001; Wilks' Λ = 0.780, d = −1.06, verbal learning and memory, F = 5.444, p ≤ .001; Wilks' Λ = 0.774, d = −1.08, and visual learning and memory, F = 5.482, p ≤ .001; Wilks' Λ = 0.772, d = −1.09, the multivariate group effect was not removed, suggesting that these domain deficits do not underlie group differences in other domains in general. A MANCOVA comparing all six domains between groups while controlling for estimated current intelligence was also significant, F = 3.406, p ≤ .001; Wilks' Λ = 0.818, d = −0.94, suggesting that generalized domain impairment is not simply attributable to lower intelligence.The primary MANCOVA model controlling for speed of processing while comparing the five remaining domains showed that processing speed made the otherwise highly significant multivariate group difference nonsignificant, d values with two‐thirds (68%). Post hoc ANCOVAs controlling for each of the other domains and intelligence did not suggest the same global impact on domain functioning, with d values averagely being reduced from 20% to 48%.ANCOVAs comparing individual neurocognitive domains between groups while controlling separately for each other domain as well as intelligence were performed to explore multivariate test results in detail, as presented in Table d values was now minor or even negligible. Supplementary, it was also assessed if the processing speed measures with a substantial motor component, when used as a composite covariate, were able to remove the significant between‐group differences across the other five neurocognitive domains. The revised processing speed composite encompassed three measures, that is, the BACS Token Motor Task, BACS Symbol Coding, and Trail Making Test A, thus excluding the two nonmotor BACS Verbal Fluency tasks originally included in the broader processing speed domain. When this revised processing speed composite was included in a MANCOVA as a covariate while comparing the five other neurocognitive domains between the two groups, the significant multivariate between‐group difference was eliminated, F = 1.271, p = .28; Wilks' Λ = 0.936, suggesting that the revised speed domain may account for significant group differences across the other domains. Post hoc univariate ANCOVAs comparing each neurocognitive domain between the two groups while controlling for the revised processing speed composite were next performed. The results suggested that covarying the revised processing speed domain eliminates the significant between‐group difference across all individual neurocognitive domains except attention/vigilance. For detailed information on these univariate analyses, see Table Given the hypothesized key role of speed of processing, other domains used as covariates might have eliminated significant group differences due to their shared variance with processing speed. We therefore carried out supplementary analyses, regressing each domain on speed of processing and using the standardized residuals as individual processing speed‐independent covariates in ANCOVAs comparing groups across domains  was also more pronounced in our UHR sample than in meta‐analyses, with effect sizes ranging from g = −0.21 . Even though many UHR studies have detected comparable or even larger effect sizes across domains , which is partially financed by an independent grant from the Lundbeck Foundation based on international review and partially financed by the Mental Health Services in the Capital Region of Denmark, the University of Copenhagen, and other foundations. Her group has also received a research grant from Lundbeck A/S for another independent investigator‐initiated study. All grants are the property of the Mental Health Services in the Capital Region of Denmark and administrated by them. She has no other conflicts to disclose. All other authors declare that there are no conflicts of interest in relation to the subject of this study.Lasse Randers: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Writing of the original draft, Review and editing of the manuscript. Jens Richardt M Jepsen: Conceptualization, Data curation, Formal analysis, Methodology, Writing of the original draft, Review and editing of the manuscript. Birgitte Fagerlund: Conceptualization, Data curation, Methodology, Resources, Writing of the original draft, Review and editing of the manuscript. Dorte Nordholm: Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Review and editing of the manuscript. Kristine Krakauer: Data curation, Investigation, Project administration, Review and editing of the manuscript. Carsten Hjorthøj: Formal analysis, Review and editing of the manuscript. Birte Glenthøj: Funding acquisition, Resources, Review and editing of the manuscript. Merete Nordentoft: Conceptualization, Funding acquisition, Methodology, Resources, Review and editing of the manuscript.https://publons.com/publon/10.1002/brb3.1962.The peer review history for this article is available at Table S1Click here for additional data file.Table S2Click here for additional data file.Table S3Click here for additional data file.Table S4Click here for additional data file.


Professor Xiaodong Zou is a full professor and chair of Inorganic and Structural Chemistry and deputy head of the Department of Materials and Environmental Chemistry at Stockholm University. After completing her PhD in structural chemistry and electron crystallography, she started at the beginning of her independent career to apply these techniques in solving structures of porous materials. She first worked on development of inorganic porous materials, such as zeolites and related open-frameworks. A joint project with Prof. Michael O’Keeffe drew her attention to metal organic frameworks (MOFs), covalent organic frameworks (COFs) and reticular synthesis. Since then, she is working on developing structure characterization techniques for porous materials. Tell us a little bit about your research background and how you became interested in the chemistry of porous materials and COFs.My research background is structural chemistry and crystallography. More than 90% of atomic structures were solved using X-ray diffraction. This requires growth of large crystals which is actually very difficult in many cases. Electron crystallography could be used for studying very tiny crystals. However, it was not well developed when I started my PhD in this field more than 30 years ago. We developed methods and imaging processing software for structure determination using electron crystallography. After my PhD I was looking into areas where I could apply our techniques. I became interested in research on porous materials because it was the field which really needed these techniques. There are many new and interesting materials with complicated structures developed in this field that could not be solved by X-ray diffraction. In order to understand the properties and to develop the applications further, it is important to know the structures. I first worked on zeolites and related inorganic open-frameworks, and later on MOFs and COFs. We solved the structures of many porous materials that remained unknown for years or decades. It is really rewarding to be the first person to find out the structure and how the channels and pores look like. I started to be interested in COFs when I visited Prof. Michael O’Keeffe at Arizona State University in 2005. He introduced me to reticular chemistry and I loved this design concept. O’Keeffe predicted a series of carbon-based crystalline porous materials based on reticular chemistry, and encouraged me to try to synthesize them. Unfortunately we could not get any crystalline materials. Therefore I became more interested in developing structure characterization methods to help people who are developing new porous materials. Most of my work since then has been focused on method development and structure determination.Looking back on 15 years research on COFs, what were the highlights in COF research and which expectations remained unfulfilled?There have been many important developments during these 15 years. One highlight is the discovery of new reactions that can link different organic building blocks to form porous crystalline COFs. From the first COFs synthesized based on condensation of boronic acids, new COFs have been developed in many different chemical systems using other condensation reactions. It shows that this is a general approach that can be widely applied. Understanding the importance of reversible bond formation has been the key for synthesizing new COFs and improving their crystallinity. Of course, with all these developments of new COFs, a challenge has been how to determine their structures. Most COF structures are deduced by combining powder XRD and model building, where many important structure details are unattainable, such as atomic positions, bond connectivity and bond nature, locations of guest species. Recently several groups succeeded in growing larger crystals of COFs by slowing down the crystallization process. This is also an important highlight. With large crystals, the missing structure details of COFs could be obtained by high resolution single crystal X-ray diffraction. This improves our understanding on how a real COF material looks like. Electron crystallography had also made a big impact on COF structural studies, single crystal structures of several COFs have been determined by 3D electron diffraction from nano- and micro-metre sized crystals.There are many studies on applications of COFs such as gas storage and separation, catalysis, and sensing. COFs have shown large potential for applications in many areas, but much more work needs to be done in order to identify which areas will be the most promising ones for COFs.In your opinion, which challenges need to be tackled in order to move the field further?I think structure characterization is one of the challenges. Although there have been progresses in making large single crystals, most COF materials are still synthesized as polycrystalline powders. There is a need to develop methods which allow us to perform phase analysis and structure characterization of such powder materials with high throughputs. This would greatly help the COF community to develop the material further. We have been developing automated electron diffraction methods to study individual small particles in a powder and determine their structures. We are also developing electron diffraction methods for fast screening of the particles in a TEM and perform high throughput phase analysis of the materials. These methods can be helpful to understand crystallization processes of COFs, have a better control of the synthesis and subsequently improve the crystallinity of the materials. There are of course many other challenges in terms of synthesis and applications. We need developments in different areas, such as synthesis, characterization, modelling and application in order to push the field further.What needs to be done in order to make COFs more widely adopted and to get a foothold in industry and industrial applications?I think there are two things important: One is to identify key applications. Now there are many researchers exploring different applications on the academic level. If we can find key applications which are unique for COFs, they would become interesting for industry. The other is to make the COF synthesis scalable and COF materials processable. For industrial applications, cost is also an important issue. Research on COFs is still in its infancy. COFs have many unique features compared to other porous materials. It will move into industry once we find a unique application, although this may take time.Where do you see the field going next?The field is going towards materials engineering and applications. We need more people to explore the potential of different applications of COFs. Different applications require different materials, thus development of new materials is also important. The field also needs developments of new characterization methods. I hope one day in the future we will have tools to visualize a COF material at the atomic scale regardless of whether it is crystalline or amorphous, to see where the atoms are positioned, how the channels look like and how guest molecules interact with the framework.The interview was conducted by Senior Editor Dr. Johannes Kreutzer.


Cross-domain relation extraction has become an essential approach when target domain lacking labeled data. Most existing works adapted relation extraction models from the source domain to target domain through aligning sequential features, but failed to transfer non-local and non-sequential features such as word co-occurrence which are also critical for cross-domain relation extraction. To address this issue, in this paper, we propose a novel tripartite graph architecture to adapt non-local features when there is no labeled data in the target domain. The graph uses domain words as nodes to model the co-occurrence relation between domain-specific words and domain-independent words. Through graph convolutions on the tripartite graph, the information of domain-specific words is propagated so that the word representation can be fine-tuned to align domain-specific features. In addition, unlike the traditional graph structure, the weights of edges innovatively combine fixed weight and dynamic weight, to capture the global non-local features and avoid introducing noise to word representation. Experiments on three domains of ACE2005 datasets show that our method outperforms the state-of-the-art models by a big margin. The Internet of Things (IoT) is a large-scale paradigm. In the IoT paradigm, devices communicate with each other regardless of their owner. Communication is not only between machines and people, but also between machines  and betwCollecting traffic information from social networks and using it for travel safety are two challenging issues in Intelligent Transportation Systems (ITSs). The transportation network can be monitored through sensor devices and social network data. Currently, ITSs uses sensor devices to monitor all aspects of the transportation network. ITSs can use social network data to support traffic and control management and to check transportation services. The analysis results of these data can help travelers travel safely, solve problems related to traffic congestion, and increase travel in urban areas. However, ITSs may not be able to collect accurate traffic information from these sensors. In addition, in social media, information related to traffic comes with cross-domain text information, which makes the task of transport text mining and classification more difficult for ITSs ,7,8,9. TRelation extraction refers to extracting the semantic relation between two candidate entities, i.e., triples such as . In real life, the target domain often has only a small amount of labelled data or even no labelled data; thus, using source domain-labelled data to improve the extraction of relations from the target domain is necessary. However, due to the different data distributions of different domains, directly applying a model trained on the source domain to the target domain results in great performance decreases.Most works have solved this issue by extracting shared features between the source and target domains and then using these features to perform relation extraction. References ,12,13 usOther works focused on aligning the domain-specific features between domains. References ,19 belieTo solve the problems mentioned above, inspired by , we propThe word information is propagated through graph convolutions on the tripartite graph so that the domain-specific word representations are aligned. After adapting the non-local features, i.e., obtaining the aligned word representations by the graph adaptation network, we perform adversarial training to extract the shared local features between domains. These word representations are aligned before being fed into downstream modules; therefore, the model can extract shared features effectively. Finally, the shared features are fed to a fully connected layer to perform relation extraction. Because we assume that only source domain data have labels, the relation classifier is trained using only source-labelled data. In addition, to prevent the transfer of irrelevant information from words, we select valuable edges and remove irrelevant edges depending on their fixed weights. Using this method, the model not only preserves useful information for adapting non-local features but also speeds up the calculation process.We propose a novel graph adaptation network to align domain-specific features. Local features and non-local features are transferred simultaneously for cross-domain relation extraction. This is also the first work to adapt the non-local features between domains.Unlike the traditional graph convolutional network, our network combines fixed weights and dynamic weights as the edge weights of the graph. In addition, rather than using a fully connected graph, we only keep valuable edges based on their edge weights. These strategies can transfer useful information effectively and avoid introducing irrelevant noise.Experiments have shown that non-local features such as word co-occurrences are also important for cross-domain relation extraction. The proposed method to calculate weights and select edges can capture more non-local features and better avoid noise interference than other methods.The major contributions of our work are as follows:The rest of this paper is organized as follows. Data collection and data query are important applications in wireless sensor networks (WSN) and the IoT, and data collection and data query are usually information-centric. We noticed that WSN is a special network, in which each sensor will involve sensor data in different fields . Each field contains a large amount of data information, and the data distribution in different fields is different. In the last decade, deep learning (DL) has made breakthroughs in natural language processing (NLP), image processing and reinforcement learning, so that making breakthroughs in the field of artificial intelligence (AI) occupy the dominant position. Presently, much research contributed to information mining and the utility of massive data . In addiCross-domain relation extraction aims to solve the problem of the training set and test set with different data distributions. Reference  was the Massive domain adaptation methods were also applied in other tasks. Reference  used speRecently, graph structures were widely used in natural language processing tasks to capture non-local features. Reference  applied i = 1, 2, …, n) denote the first and second candidate entity respectively, f trained on D to the test datasets X is the input space and Y is the set of relation labels, the goal of the learning algorithm is to build a classifier f:Given a set of labeled corpus f, and applies f to target domain. The goal of the learning algorithm is to build a classifier f:Given a set of source labeled corpus In our work, we take In brief, our model consists of four modules: an adaptation module, an embedding layer, a shared feature extractor and a relation classifier. The adaptation module contains a domain discriminator and a graph convolutional network (GCN) layer, which are responsible for local shared feature extraction and non-local feature alignment, respectively. The adaptation module mainly contributes to extracting the shared features between domains and aligning domain-specific features. The traditional approach only adapts sequential features but ignores non-sequential or non-local features. Our adaptation module consists of two processes: local information adaptation and non-local information adaptation. Local information adaptation is applied at the sentence level; in other words, we extract the shared features of the source and target sentences. While non-local information adaptation is a word-level adaptation, it uses a GCN to align domain-specific word representations. The remainder of this section elaborately introduces the adaptation layer.(1)Local information adaptation (sentence-level).To make the shared feature extractor capture domain-invariant features, a domain discriminator is added after the shared feature extractor. It takes In this equation, To confuse the domain discriminator, a gradient reversal layer (GRL)  is used Through reversing the gradient before domain discriminator, the parameters of domain discriminator are optimized to reduce the domain discriminator loss (2)Non-local information adaptation (word-level).Word-vectorized representations, such as word2vec  and GlovG = Word co-occurrence tripartite graph construction: The key idea of non-local information adaptation is, in the tripartite graph, if two domain-specific words have connections to more common domain-independent words in the graph, they tend to be aligned together with higher probability, i.e., have similar word representation . Given tm is the length of the dictionary), the weights are normalized by:a is the minimum element of b is the maximum element of We use two types of weights for the graph edges: fixed weights and dynamic weights. First, the pointwise mutual information (PMI) of two words is used as a fixed weight. The PMI is an algorithm for calculating the correlation between two variables. Here, we use the PMI to measure the co-occurrence relationship of two words:The graph only keeps edges with is” and “the” are often domain-independent, and they have a high probability of appearing together with domain-specific words; therefore, the fixed weight between them is large. However, these stop words have little semantic meaning and harm the word representations. (2) Some domain-specific words are rare and seldom appear together with domain-independent words, so the fixed weight is almost 0, but these words should probably also be aligned; fixed weights cannot achieve this.Fixed weights capture the word co-occurrence features, but they have difficulty aligning domain-specific words. There are two limitations of using fixed weights: (1) some English stop words such as “i, i in graph, i and i:j to node i, i are normalized by To compensate for the limitations of fixed weights, inspired by , attentiDynamic weights can be adjusted during training to provide a flexible way to train the parameters of the model. In addition, each fixed weight can be seen as a global weight because it is calculated based on statistics of the whole corpus, while a dynamic weight can be seen as a local weight because it only uses two sentences per calculation. In the end, we combine the fixed weights and dynamic weights as our final graph weights:The process of calculating the edge weights is illustrated in Graph convolutions on the tripartite graph: In this section, we first introduce the graph convolution operation and edgewise gating mechanism and then elaborate on how these methods are used in our model.V is the node set and E is the edge set, the graph convolution operation is applied on every node and propagates information to other nodes along the edges. For a 1-layer GCN, the information only transfers to neighboring nodes, and the information of an n-layer GCN can transfer to further nodes as n increases. The information propagation from layer k to layer i and its neighbors. j. A GCN  is used Edge-wise gate  is propoHere n-layer GCN, According to , we inteExternal knowledge, such as entity positions and dependency trees, is important for relation extraction ,46,47. FReal-valued word embedding vector. We obtain one word’s embedding vector ed as in . This pri and j denote the two entities’ in a sentence, for each word k, its relative distances are Words’ relative distances from candidate entities. Use He will blowa cityoffthe earthin a minute if he can get the hold of the means to do it”, the bold words are candidate entities, and their entity types are GPE and LOC. Entity types are essential knowledge for relation extraction. In some cases, we can infer the relation of two candidate entities only by the entity types. In our setting, we only indicate that the entity types and nonentity words are randomly initialized in the same vector. Every word has two entity type vectors Entity type. Each entity type is predefined, and every entity has an entity type to which it belongs. In the sentence “Sematic chunks. A chunk is an indivisible fixed phrase in a sentence, and we inform the model to regard all chunks as a whole so that the semantic information of the chunks is not disrupted. We use the B-I-O format to indicate chunks, and every word obtains a chunk vector Shortest dependency path between two entities. The shortest dependency path refers to the shortest path between two entities in the dependency tree. See n can be represented as vAfter getting all above types of embedding vectors, we transform every word into a real-valued vector v, where r is numbers of words the kernel spanned and v is the word representation matrix after GCN layer. A feature We use a simple CNN architecture proposed by  as shareHere, c, i.e., d unchanged and use different r) and each kernel size has multiple feature maps. For a fixed kernel size, different feature maps will get different i is:Then we perform max-over-time pooling operation  on c, i.m is the number of feature maps. Finally, we concatenate all k is the number of kernel size.h with i-th source domain data, and r is the number of relation type. The relation classify loss Since there only exists labels in the source domain, the relation classifier only takes the shared features i has relation j. i containing the relation j.Here During training, we combine all the losses mentioned above to get the final loss function and optimize jointly:n is the number of shared word appeared in the target domain sentence. Then we sort the elements of w in descending order. Finally, the top m words are selected to form the source domain sentence and we set (1)DatasetWe conduct our experiment on the English part of the ACE2005 dataset. There exist 6 domains (broadcast news (bn), broadcast conversations (bc), newswire (nw), weblog (wl), usenet (un), and conversational telephone speech (cts)) and 7 unidirectional relations (artefact (ART), gen-affiliation (GEN-AFF), org-affiliation (ORG-AFF), part-whole (PART-WHOLE), person-social (PER-SOC), physical (PHYS) and none (METONYMY)). If we consider the directions of the entity pairs, there are 11 directional relations . We process the datasets as in  Table 1Table 1.From (2)EvaluationP) and recall (R) by averaging:r is the number of relation type and i-th relation. The macro-F1 is calculated using:The precision, recall and macro-F1 are used as evaluation method in our experiment. Specifically, we calculate precision (ace-data-prep (https://github.com/mgormley/ace-data-prep). The embeddings are randomly initialized and optimized during training except for these pretrained word embeddings because we found that there is no improvement when pretraining all embeddings. All the sentences are padded or cut to 155 characters. The learning rate is set to 0.001 and halved every 4 epochs. We use Adam [We use pretrained, 300-dimensional word embeddings generated by word2vec . The depuse Adam  as the ouse Adam  is used We use the following baseline models for evaluation purposes:NNM & log-linear model: Basic neural network models (NNM), such as CNNs and RNNs, were used in  to improFCM & hybrid FCM: The feature-rich compositional embedding model (FCM) was proposed in . The keyLRFCM: The low-rank approximation of the FCM (LRFCM)  is an imDANN: The domain adversarial neural network (DANN)  was the GSN: The genre separate network (GSN)  uses a dCVAN: The cross-view adaptation network (CVAN)  uses croAGGCN: The attention guided graph convolutional networks (AGGCN) , a novelMAPDA: A novel model based on a multi-adversarial module for partial domain adaptation (MAPDA) is proposed in this study . This pa(1)Performance comparison with existing methods.After adding adversarial training, DANN+CNN achieves results that are comparable to those of the state-of-the-art model (CVAN) in terms of macro-F1. (2)Data distribution of source and target domains.In this paper, the source domain dataset bn+nw and the target domain dataset cts are taken as examples. To see the change in data distribution more intuitively, we used the high-dimensional data dimensionality reduction algorithm t-distributed stochastic neighbor embedding (t-SNE) to map the data distribution in two-dimensional space. The data distribution before and after applying our method is shown in (3)Performances with different numbers of GCN layers.L ≤ 6 because there is no improvement obtained when L > 6.From Equation , we knowEquation  as 0.4 aL > 4, the macro-F1 declines to different degrees for all three domains, and we assume that the word representations are distorted due to too many instances of information propagation.From (4)Performances with different thresholds Effects of the GCN and dynamic weights.To illustrate the effectiveness of the GCN and the use of dynamic attention weights, we draw the precision-recall curves for every relation type in This illustrates the effects of the GCN layer. When adding DA to the GCN layer, the improvement is highly obvious, especially under the PER-SOC relation; specifically, there is a 10% improvement in precision when recall >0.7 in x-axis denotes the shared words of different domain sentences, the red part of the y-axis denotes source-specific words, and the blue part denotes target-specific words. Each pixel corresponds to the weight i-th shared word and the j-th specific word described in Equation (i-th shared word are normalized. The colors between the word new and some target-specific words such as Mexico and Kansas are deepest because their fixed weights are large according to Equation (Funeral and School, also have deep colors, while the colors of words with weak domain relevance, such as functions and apointed, are relatively shallow. These facts illustrate that the dynamic weight mechanism pays more attention to aligning specific words with stronger domain relevance; the sole use of fixed weights cannot be achieved.An example of graph weight visualization is shown in Equation , and theEquation . In addiWe also present some typical examples in In this article, a novel graph adaptation network for cross-domain relation extraction is proposed. First, a novel graph adaptation network is constructed to align domain-specific features. The model aligns domain-specific features through applying graph convolutions on a source-shared-target words tripartite graph. Secondly, unlike the traditional methods only adapting local or sequential features, our model adapts local and non-local features jointly to improve the performance of cross-domain relation extraction. Finally, a cross-domain relation extraction model is constructed by inputting the fused all features into softmax.In addition, unlike the traditional graph convolutional network, in order to compensate for the limitation of fixed weight, a dynamic attention weight is combined together with fixed weight. To transmit useful information more effectively, we retain valuable edges based on their edge weights. Experiments on the three domains of ACE2005 datasets verified the effectiveness of GCN layer and dynamic attention weight, which achieve state-of-the-art on all three domains.We will further explore the cross-domain relation extraction when a few labeled examples can be explored or new relations appearing in the target domain. In addition, we hope our work can be applied to other domain adaptation tasks in the future.


This article revisits the concept of relative deprivation and asks whether it is still useful for criminology. The article traces the way relative deprivation has been used in the past to understand crime and how it has connections to other, more recent, additions to debates on social justice. I argue that relative deprivation has disappeared even in the place that it had become the key explanation for crime—left realism. In so doing, I explore the resurrection of left realism in criminology—what I refer to as “post-millennial left realism”—first, by those who were associated with it originally, and then with Hall and Winlow’s  shift in emphasis to what they term “ultra-realism.” I maintain that relative deprivation is still a powerful concept for bridging several related areas that should still be central to the concerns of criminology—in part, because it is still a major concern in popular social science and social psychology. Why has it disappeared in criminology? I present an argument that suggests that the absence of certain research methods, such as ethnographic and qualitative or small-scale survey methods, has impoverished our understanding of the lived reality of people experiencing the social transformations of a networked, precarious society. The massive polarization and disruption in politics and social discourse, as well as the worldwide economic, public health, and social transformations  have demonstrated the continued relevance and analytical power that relative deprivation, in its elaborated form, brings to questions of crime and justice. One of the fullest accounts of the concept of relative deprivation was found in the work of W. G. Runciman in 1966. Since then, the concept of relative deprivation—the idea that people may feel deprived on any dimension in comparison to others in society—has seen its status as a theory that might explain crime rise and fall. Criminologists that collected under the moniker of “left realism” originally identified relative deprivation as a central cause of crime. Relative deprivation has been largely absent from criminology for many years, however, despite the re-emergence of realist criminology in various forms. This article represents a sequel, of sorts, to an article I wrote several years ago Webber .The reason for re-visiting the concept now is that the original arguments for its re-evaluation are still relevant in a world that has changed dramatically since my earlier work , one of the pioneers of left realism, Jock Young, argued against Garland’s contention that criminology was moving away from theories of relative deprivation and toward greater reliance on control theories. Young maintained that the evidence pointed to a different shift—one toward theories of relative deprivation as the motivating cause of crime in criminological research and public explanations of crime in surveys  that affected the poor and working class. Central to relative deprivation is the idea of subjectivity—that people often make judgments that are contradictory to their own best interests and that rationality is bounded by error , left realists were able to show that the media and political narratives of crime in inner-city housing estates did not match the perception of those that lived there  poor, at the same time that those agents ignored or legitimized crime and deviance committed by the powerful .Although Runciman’s work is largely missing in all forms of left realism, despite relative deprivation appearing throughout the pre-millennial form of left realism Webber , we can relative and absolute deprivation. To be “relatively deprived,” a person need not be suffering deprivations that are harmful to that person’s existence, as is the case in many definitions of absolute deprivation. Thus, relative deprivation applies to those who are poor and those who are rich and everyone in between. For example, Person A earning the average income for their country might compare themselves to Person B earning the same salary but feel deprived because Person B seems better able to invest money in a house that appreciates in value more than does Person A’s home. In other words, Person A has clothing, food, shelter and a stable job, but still feels relatively deprived when compared to Person B. But, crucially, this is only relative deprivation if a third party would deem this disparity as unfair, rather than as envy. As noted below, it has to be value-neutral. The sense of deprivation must be “earned” as a result of a failure of social justice. In this example, the feeling of deprivation would constitute relative deprivation if it could be established that certain areas had seen their house prices inflated unfairly through differential investment in the local economy.The first aspect of Runciman’s perspective that needs to be addressed is the distinction between less stark on some consumer variables, as evidenced by, for example, the ubiquity of televisions, cars or even smart phones. At the same time, though, this technologically interconnected world has widened political divides and broadened the chasms between political ideologies . Indeed, I contend, creating relatively deprived in-groups who compare themselves unfavorably to out-groups is the driving force of politics and consumerism in the post-millennial world. The power to manipulate subjective measures of comparability for political and commercial gain, through “fake news,” “deep fakes”  and data-fed advertising, where companies harvest your personal data to personalize their marketing and messages, has increased exponentially as the internet has become embedded and ubiquitous  citizens, ignited both social media on the web and passions in the streets of cities around the world. The power of the web to publicize abuse of power—and then to act as the means to galvanize a collective, group response through fostering shared symbols of in-group solidarity, coupled with the frictionless communication afforded by networked-mobile devices—provided the means for the expression of collective anger and frustration. This is relative deprivation, supercharged by the web and resulting in mass civil protest in major cities around the world. For the pre-millennial left realists, relative deprivation caused crime. In the post-millennial world, relative deprivation has returned to being the more analytically powerful tool that can help explain not just crime, but also mass civil protest Gurr .created, rather than preordained). Similarly, new ultra-realists criticize the identity politics of intersectionality—the study of how different social variables such as class, gender, race and sex can combine to produce different social outcomes  by those who champion intersectionality. For example, the lived reality of people categorized as BAME or LGBQT will not be shared equally by everyone within in any given category. The harms of capitalism, exposed by ethnographic insight, are more powerful than the subcultures of identity politics. But viewing such lives through a lens of relative deprivation would reveal that no matter what economic system one lives under, one would still suffer deprivation because the creation of comparative reference groups is a social psychological reality regardless of politics. Consequently, for perspectives like ultra-realism that posit revolutionary change before progress can be made, comparative processes will continue to create intersections of various identities even after such monumental economic shifts have taken place.possession, and as a key feature of one’s identity to make as much money as possible with few consequences and without consideration of others. Thus, for ultra-realists, relative deprivation might well be a useful explanation for crime, but it highlights the logical flaw in redistribution discourses: taking from the rich and distributing the resources to the poor does not guarantee peaceful equilibrium. Those who lose the most would see themselves as victims and seek to correct their loss. Regardless of whether or not ultra-realism might benefit from a renewed appreciation of relative deprivation as it has developed beyond left realism, the next part explores where relative deprivation has been employed to an analytically useful effect.There is no reason why relative deprivation, in its more elaborated and complexly rendered form, cannot be a key concept within ultra-realism. Its rejection is based on the argument that humans do not have a natural sense of justice, and perhaps also that it was shackled to left realism and that left realism was flawed as a pragmatic program of small administrative changes, rather than a wholesale undermining of the economic system that gave rise to such inequality. Relative deprivation, then, becomes a logically problematic idea because it suggests that there will always be inequalities regardless of the political economy. Powerful interest groups attack policies of financial redistribution not because they feel threatened that they might become poor, but because they have to give up something they regard as a any definition of relative deprivation must include a sense of deservingness. The person who feels deprived ought to feel that they are justifiably entitled to whatever it is that they are lacking. Indeed, as already argued, this sense of fair entitlement, following Rawls’  cause of crime. For Runciman, and the later elaborations in social psychology, relative deprivation is a far more complex set of processes. In other words, criminology is concerned with answering narrow questions of causative links which, in turn, has limited the way in which relative deprivation has been used in criminology. In contrast, by understanding the social comparative processes that lead to subjective outcomes, such as relative deprivation or relative satisfaction, we can begin to engage with and embrace Runciman’s consideration of Rawls’  concept The precariat experiences the four A’s—anger, anomie, anxiety and alienation. The anger stems from frustration at the seemingly blocked avenues for advancing a meaningful life and from a sense of relative deprivation. Some would call that envy, but to be surrounded and constantly bombarded with the trappings of material success and the celebrity culture is bound to induce seething resentment.In addition to social psychology, relative deprivation has been discussed in other areas of the social and political sciences. Several popular books have tackled questions of inequality and the precarious futures of neoliberal capitalism  . This is exactly what the original formulation of relative deprivation, and its elaboration in social psychology, set out to accomplish. As I have argued above and elsewhere (e.g., Webber can pursue. If we take cybercrime as an example, it is still early in our understanding of why young people might shift from using technology for games and a chat to buying and selling stolen credit card data on carding forums. In addition, when an explanation is presented to explain young people’s behavior, it invariably entails RCT (Webber and Yip In sum, relative deprivation is not a panacea for criminology, and I do not claim that the social psychological research on reference groups is the answer to deep-seated criminological conundrums. Instead, I wish to assert that it is still a good idea to “think with”—an idea that seems to have disappeared not through any lack of analytical robustness it might bring. Effectively, relative deprivation has been neglected because it has fallen out of fashion and so has not made the jump to the post-millennial form of left realism, except for a few exceptions, and even then, and again, not in the elaborated sense that I have promoted in this article. Relative deprivation, as a concept, is now coupled with the tendency toward disdain that sociological criminology has toward psychological ideas, and so the idea has become outdated Webber . But, asOne lesson to take from Standing’s  precariaOne positive outcome of the global COVID-19 pandemic is that entire countries allowed their freedoms to be restricted for the common good of preventing the spread of a deadly disease. As I write this in the social isolation required of British citizens, the pandemic is still dominating and destroying the lives of people throughout the whole world. Global cooperation has now been accompanied by the protests of Black Lives Matter. The shifts in attitudes, as previously well-defined reference groups have fractured, have yet to settle into an identifiable whole. It may be that the shifts become permanent and that the attitudes to collective living, either in terms of public health, race or sexuality, become more collaborative and caring. Alternatively, we might witness even more hardened oppositional groups and clearer lines of demarcation—or, worse, newly constituted oppositional groups, which could mean more harmful rhetoric, more crime and more terrorism. Relative deprivation—and the underlying theories of comparative reference groups, human behavior, and stereotyping—can help us understand these monumental changes. An appeal to social justice, if loud enough, can shift trenchantly held opinions.


H and VL, influences the shape of the antigen binding site, that is, the paratope, and is essential to understand antigen specificity. ABangle characterizes the VH‐VL orientation by using five angles and a distance and compares it to other known structures. Molecular dynamics simulations of antibody variable domains (Fvs) reveal fluctuations in the relative domain orientations. The observed dynamics between these domains are confirmed by NMR experiments on a single‐chain variable fragment antibody (scFv) in complex with IL‐1β and an antigen‐binding fragment (Fab). The variability of these relative domain orientations can be interpreted as a structural feature of antibodies, which increases the antibody repertoire significantly and can enlarge the number of possible binding partners substantially. The movements of the VH and VL domains are well sampled with molecular dynamics simulations and are in agreement with the NMR ensemble. Fast Fourier transformation of the ABangle metrics allows to assign timescales of 0.1‐10 GHz to the fastest collective interdomain movements. The results clearly show the necessity of dynamics to understand and characterize the favorable orientations of the VH and VL domains implying a considerable binding interface flexibility and reveal in all antibody fragments  very similar VH‐VL interdomain variations comparable to the distributions observed for known X‐ray structures of antibodies.The relative orientation of the two variable domains, VH and VL. The relative orientation of the VH and VL domains influences the shape of the antigen‐binding site and is a major objective in antibody design. In agreement with NMR experiments and molecular dynamics simulations, we show a considerable binding site flexibility in the low nanosecond timescale. Thus we suggest that this flexibility and its implications for binding and specificity should be considered when designing and optimizing therapeutic antibodies.Antibodies have become key players as therapeutic agents. The binding ability of antibodies is determined by the antigen‐binding fragment (Fab), in particular the variable fragment region (Fv). Antigen‐binding is mediated by the complementarity‐determining regions consisting of six loops, each three of the heavy and light chain variable domain V Besides lengths and sequence of the CDRs, the relative orientation of VH and VL is a third very important factor that determines the shape of the antigen‐binding site.H and VL domains to one another is an additional structural feature of antibodies, which directly increases the repertoire of antibody specificity.H‐VL domain orientation directly change the binding site geometry and have an effect on the specificity of the paratope, the antigen‐binding site, for target antigens.H1‐CL.H‐VL domain orientation is crucial in understanding the antigen‐binding process. Antibody‐antigen binding can be understood in terms of the conformational selection mechanism.Antibodies have become an important tool in therapeutics and clinical diagnostics.H‐VL domain movements. We compare the VH‐VL domain orientation observed in our simulations to the NMR ensemble of a single‐chain variable fragmentThis work uses experimental Nuclear Magnetic Resonance (NMR) Nuclear Overhauser Effect (NOE) data in combination with molecular dynamics simulations to understand the V2The NOEs and suggested structures for the scFv complex and the Fab were provided by the group of Mark Carr at University Leicester.All structures were prepared in MOE 2.1The scFv with and without IL‐1β, Fv, and the Fab structures were simulated for 1 μs using molecular dynamics as implemented in the AMBER 18 simulation package.2.2The NOE distances are intramolecular NOEs and define the distances between amide protons.2.3H and VL) using six measurements . The ABangle script can calculate these measures for an arbitrary Fv region by aligning the consensus structures to the found core set positions and fitting the planes and distance vector from this alignment. This tool available online was combined with an in‐house python script to reduce computational time and to visualize our simulation data over time. The in‐house script makes use of ANARCIABangle3H‐VL domain orientation in these six metrics. These high variations are obvious in comparison to the distributions of angles and the distance of the originally published 352 crystal structures found in the PDB displayed in the background. Comparison of 1‐μs molecular dynamics simulations of the scFv without the presence of the IL‐1β .Figure S3: Complex scFv ABangle measure of 1 μs molecular dynamics simulation.Figure S4: Fab ABangle measures of 1 μs molecular dynamics simulation.Figure S5: FFT Spectrum of the HL angle fluctuations of the Fab simulated NMR ensemble.Figure S6: Comparison of experimental scFv complex NOEs with the calculated NOEs of 1 μs simulations with and without the antigen bound and with the complex NOE restraints. The plot shows the scFv fragment. The gap is caused by the peptide (G4S) linker, which directly connects the light and the heavy chain variable domains.Figure S7: Overlay of the HL angle distributions of the Fv (green) and the scFv (forestgreen) fragments.Figure S8: Overlay of the HC2 angle distributions of the Fv (green) and the NOE Fab simulations (blue) fragmentsTable S1: Average and standard deviations of the six ABangle measures for all six considered antibody fragments.Figure S9: Overlay of the ABangle histogram (blue) with the angle variations observed in the 0.1 to 10 ns timescale (orange).Figure S10: Illustration of the ABangle angle and distance definitions.Click here for additional data file.


A β titanium alloy is an excellent candidate for cryogenic applications. In this study, the deformation behavior of Ti-36Nb-2Ta-3Zr-0.35O with cold swaging was investigated at cryogenic temperatures to verify its practical application value. The microstructure after tensile tests was observed by transmission electron microscope in order to reveal the cryogenic deformation mechanism. The results show that the mechanical properties of this alloy have a strong temperature dependence: an increase in strength with a non-monotonic trend (first increase and then decrease) in elongation is found when the temperature decreases from 297 K to 77 K. At 200 K, a strength-ductility synergy is obtained and is mainly due to the occurrence of {211} <11> mechanical twinning accompanied with the ω plate located at the twin boundaries, which is the first time it is detected in titanium alloy at a cryogenic temperature. However, at 77 K, martensitic transformation (β phase to α phase) is induced by the tensile deformation, leading to the increase of strength with a massive sacrifice of elongation. These findings provide insights for understanding the deformation mechanisms and optimizing the mechanical properties of titanium alloys at a cryogenic temperature. Titanium alloys are being used more widely as the cryogenic engineering material due to their unique properties at cryogenic temperatures such as a high strength-to-weight ratio, good toughness, low expansion coefficient, and excellent corrosion resistance ,2,3. At 4, has a slightly higher tensile strength and a similar elongation than the α type titanium alloy [4. The deformation modes of TC4 are related to the morphology of α grains. In globular grains, the dominant system is the prismatic slip, but only the basal slip system is observed in lath colonies. With the addition of interstitial elements, the elongation of TC4 ELI increases to 18% and, meanwhile, the yield strength is as high as 1500 MPa [4 ELI at 20 K and accounts for practically half of the plastic deformation.For α titanium alloys, an ultra-high tensile strength (1200–1400 MPa) is obtained at 4–77 K  and therum alloy . Ambard um alloy  found thIn recent years, the properties of β-titanium alloys at cryogenic temperatures has drawn some attention ,15,16,17Our group has studied the plastic deformation behavior of gum metal with two different deformation histories at 77 and 200 K . We founThe Ti-36Nb-2Ta-3Zr-0.35O (TNTZO) alloy used in this work was prepared by powder metallurgy and cold/hot deformation. The detailed preparation process has been described in our previous work . Before −4 s−1and temperatures of 77, 200, and 273 K. At each temperature, three identical specimens were tested and every mechanical property value was averaged from these three independent tests. The following method was used to obtain the elongation to fracture, which has been used in our previous work [Flat dog-bone-shaped specimens of a 9.5-mm gage length were used in tensile tests, which were carried out at a strain rate of 10ous work . Vickers3OH) and 10% sulfuric acid (H2SO4).The fracture surface was observed in a FEI Quanta FEG 250 scanning electron microscope  operating at 15 kV. The microstructure of samples cut from the uniform deformation region after tensile tests was analyzed by a titan G2 60-300 transmission electron microscope . TEM samples were prepared by a Twin-jet electro-chemical polishing technique and the etching solution contains 90% methanol  is characterized by the Transmission Electron Microscope. It has been proven that the ω plate along the {211} <111> twin boundary is introduced by the external stress nucleate on both the (211) composition plane of the mechanical twin. In addition, the shear along {211} <111> caused by the tensile stress is responsible for deformation twinning and ω transition ,23. Ther4 was more than two times that of an untreated sample. In this work, the time of every tensile test at 77 K is no longer than 10 min, which is too short to precipitate α plates only owing to the cryogenic treatment. Therefore, it is then reasonable to conclude that the formation of the α phase is induced by the co-work of stress and cryogenic temperature.The stability of the β phase is the dominant factor determining the deformation mechanisms of β titanium alloys and is always evaluated by the Bo-Md phase stability diagram ,26,27,28The trend of mechanical properties of EP0.58 with the temperature decreasing could be explained as follows. The formation of the plate-like ω phase along the {211} <111> twin boundary has been found in other titanium alloys with similar composition ,21,29, wIn summary, the mechanical properties and deformation mechanism of the TNTZO alloy with cold swaging by 58% were investigated at different temperatures (from 77 K to 293 K). For the cold swaged alloy, a strength-ductility trade-off deviation is obtained at 200 K. During the tensile deformation at 200 K, the deformation twinning is formed, which also induces the ω phase to precipitate in the twin boundaries. The high tensile strength (1355 MPa) achieved at 200 K is owing to the fine-grained strengthening, resulting from deformation twinning and the precipitation strengthening of the hard ω phase. The good ductility (elongation: 11.6%) is due to the twinning induced plasticity effect. At 77 K, a significant increase of strength with a massive decrease of elongation is detected, which was mainly caused by martensitic transformation (the β phase to the α phase).