In contrast to traditional expertise domains like chess and music, very little is known about the cognitive mechanisms in broader, more education-oriented domains like mathematics. This is particularly true for the role of mathematical experts’ knowledge for domain-specific information processing in memory as well as for domain-specific and domain-general creativity. In the present work, we compared 115 experts in mathematics with 109 gender, age, and educational level matched novices in their performance in (a) a newly developed mathematical memory task requiring encoding and recall of structured and unstructured information and (b) tasks drawing either on mathematical or on domain-general creativity. Consistent with other expertise domains, experts in mathematics (compared to novices) showed superior short-term memory capacity for complex domain-specific material when presented in a structured, meaningful way. Further, experts exhibited higher mathematical creativity than novices, but did not differ from them in their domain-general creativity. Both lines of findings demonstrate the importance of experts’ knowledge base in processing domain-specific material and provide new insights into the characteristics of mathematical expertise. Numerous studies have revealed that experts, defined as individuals who show “consistently superior performance on a specified set of representative tasks for a domain” (p. 277)4, possess a large and well-organized domain-specific knowledge base that was acquired over several years of intensive (deliberate) practice. This knowledge base allows them to process information in their domain more effectively1. Individual differences in domain-general abilities  also play important but subordinate roles for expertise5. Most of these insights come from studies in so-called traditional expertise domains such as chess and music, which require very specific training7. Much less is known about expertise in core academic subjects such as mathematics.Research on the psychological correlates of expert performance has substantially improved our understanding of the foundations and development of exceptional cognitive performance9), in mathematics, experts are typically defined as individuals who study or have studied mathematics at university level. Consequently, novices represent individuals without tertiary education in mathematics. The majority of previous studies comparing both groups focused on selected domain-general and domain-specific abilities and reported that experts perform better in intelligence, working memory, and some basic numerical tasks such as number comparison and mental arithmetic10. When experts and novices were matched for intelligence, only differences in some domain-specific tasks  were observed11. A central limitation of this research lies in the administration of rather basic numerical tasks such as number comparison or arithmetic. Even though these tasks also draw on mathematical knowledge, they cannot capture the actual impact of the experts’ broad knowledge base on information processing.In contrast to traditional expertise domains that offer a reliable indicator of skill level  and has been attributed to the experts’ ability to connect the to-be-memorized information with their extensive knowledge base. More specifically, their superior conceptual knowledge would allow experts to recognize familiar patterns and store them parsimoniously in short-term memory21.In traditional expertise domains, the relevance of experts’ domain-specific knowledge for information processing was first observed and has been mainly investigated using a memory task originally employed by de Groot23. A recent meta-analysis showed that mathematical achievement is positively related to mathematical creativity in children and adolescents (r = 0.5324) and to a smaller extent in adults (r = 0.2425). Additionally, mathematical giftedness has been closely associated with mathematical creativity26. While there are a few biographical studies on eminent mathematicians28, where mathematicians emphasized that mathematics is a creative art, that they see mathematics everywhere, and that they are driven by creative avocations, there are no studies yet in which mathematical creativity was measured quantitatively in math experts using well-established creativity tasks. As domain-specific expertise is regarded as highly important for creativity29 it can be expected that experts in mathematics, compared to novices, will be more creative in their domain due to their extensive mathematical knowledge. However, it is still unclear if experts are only more creative in their domain or if they are more creative in general. This question leads back to the debate of whether creativity is a domain-specific or a domain-general construct31. Empirical evidence for both sides can be found, with mathematical creativity sometimes being related to other measures of creativity35, and sometimes not39. A theoretical model trying to integrate both the domain-specific and the domain-general perspective is the Amusement Park Theory (APT40). Using an amusement park as a metaphor, creativity is seen as a hierarchical four level model ranging from highly domain-general to highly domain-specific. At the first level are initial requirements which are important for creativity in all domains . At the three subsequent levels, creativity becomes more domain-specific with the underlying traits and skills needed to be creative being highly specific . Traits required for creativity are more similar for domains that are within the same general thematic area. Thus, the inconsistent findings regarding the relationship between mathematical and general creativity could derive from a differential reliance on traits and skills on the first  and the subsequent levels, which has also been shown by Willemsen et al.35 in a large sample of primary school children.Domain-specific knowledge has also been regarded as an important foundation for creative thinking42. So far, there were no studies yet that tested whether experts in mathematics show superior memory performance for structured but not for unstructured material. Also, there is no quantitative research on creativity of experts in mathematics; thus, it remains unclear whether they are only more creative in their domain or in general. This research gap is also addressed in the present study.To summarize, research on mathematical expertise is scarce, and very little is known about the interaction between the experts’ extensive knowledge base and information processing. In the present study, we investigate this for memory performance and creativity. In expertise research, short-term memory tasks for domain-specific material are the seminal and among the most frequently employed experimental paradigms for this purpose as they are highly sensitive to individual differences in domain-specific conceptual knowledge. The more elaborate the individual’s conceptual knowledge base is, the more patterns can be identified in the learning material, and the better is the recall even after short presentation times13 and mathematical as well as non-mathematical  creativity tasks to 115 experts in mathematics and to 109 gender, age, and educational level matched novices. For the memory task, in which mathematical information is presented in a structured and unstructured condition, we hypothesized that in the structured condition, experts remember more items than novices. In the unstructured condition, no or a smaller performance advantage of experts is expected. For the creativity tasks, we hypothesized that experts are more creative in mathematics than novices, but it remains unclear whether this is also the case for domain-general creativity.We presented a mathematical adaptation of the well-established memory task by Chase and Simonn = 349) did not finish the study. We further had to exclude two participants due to insufficient knowledge of German and not having a Bachelor degree. This resulted in our final sample of 224 adults. A sample size of 200 (100 per group) allows the detection of Cohen’s d > 0.35 , thus at least medium effect sizes. Participants were affiliated to one of two groups: Experts (n = 115) or novices (n = 109). Experts were defined as individuals who have already completed a Bachelor degree (or higher) in mathematics and now study mathematics, and as individuals who currently work at a university in the field of mathematics, either as doctoral candidate, as post-doctoral research fellow or as professor. For novices the same definition was applied but they were from a subject with no to minimal explicit mathematical content . Both groups were demographically very similar .13 memory task from the domain of chess. Domain-specific material was presented very shortly and needed to be remembered for an evaluation phase. In close collaboration with two mathematicians, we constructed six mathematical items that were each presented in two conditions: the structured and unstructured condition. In the structured condition, the material was structured in a mathematically meaningful way, so that the material should be highly familiar to experts. In the unstructured condition, the units of the structured material were presented in a randomized order.The mathematical memory task was designed to be conceptually equivalent to Chase and Simons’Pascal’s triangle In the structured condition we used the sixth, seventh and eighth row of the Pascal’s triangle resulting in 24 numbers. (2) Numerical series In the structured condition 15 numbers were generated according to the rule an = n2 + 2 (3) Cayley table In the structured condition we used the multiplicative group of integers modulo 7, resulting in 36 numbers. In all three numerical items, the unstructured condition was generated by arranging the numbers of the structured condition randomly. For the analyses we used the sum of correctly recalled numbers at their respective position per item per condition.The first three items of this task involved numerical material see Fig. . (1) PasGraphs In the structured condition we showed a quadratic function graph reflected across the y- and y-axis in a coordinate system. In the unstructured condition three graphs of quadratic functions, which were not reflecting each other, were presented. (5) Triangles In the structured condition we showed an isosceles triangle reflected across the y- and y-axis on a coordinate system. In the unstructured condition we presented three isosceles triangles varying in height which were slightly shifted on the coordinate system. For analyses we manually counted how many of the previously defined points (intersections and vertices for Graphs and vertices for Triangles) were correctly drawn on the coordinate system. For the Graphs item a maximum of 15 points could be reached, for the Triangles item, it was a maximum of 9 points.The next two items involved figural material see Fig. . (4) GraTheorem and Proof For the structured condition we used the following sentences “Theorem: Suppose n is an integer. If n is even, n2 is also even. Proof: If n is even, we can write n as n = 2 k. We then see that n2 = (2k)2 = 4k2 = 2 × 2k2. Therefore, n2 is even”. In the unstructured condition, we randomized the order of the words. For analyses we manually counted how many words were correctly recalled, with a maximum of 36 points to be reached.The last item used verbal material in the form of a theorem with the corresponding proof. (6) 42 and by experimental pilot studies. Overall, participants needed between 15 and 20 min to complete this task. More information about the specific study material and the explicit scoring of the measurements can be found in the The order of all items was randomized for each participant. After a short instruction to remember the material and which type of material  will be presented, participants were told that they have 20 s to memorize the material and 90 or 180 s, depending on the material, to reproduce the beforehand seen material. The timing was determined by theoretical considerations adhering to the work of Gobet and SimonN = 289) to calculate the psychometric properties. All six items of the domain-specific memory task displayed substantial variance and, except for one item, there was no evidence of bottom or ceiling effects. The item that showed a ceiling effect was the structured condition of the Triangles item. Here, a maximum of 9 points could be reached, and 252 out of 285 individuals reached the maximum . Nonetheless, the results suggest that, overall, the mathematical memory had a satisfactory sensitivity. The reliability was evaluated by determining the internal consistency in terms of Cronbach’s alpha. This revealed an acceptable internal consistency (Cronbach’s α = 0.75) across all 12 measurements of the mathematical memory task (structured and unstructured condition of each of the six items). As the structured and unstructured items measure slightly different aspects of memory, we additionally computed the internal consistency for each condition separately. Due to the lower number of measurements, the internal consistency decreased . We further looked at convergent and discriminant validity in our final sample and assumed that the overall memory capacity for mathematical material should correlate higher with a related variable than with a dissimilar, unrelated one. To this end, we correlated overall memory capacity in the mathematical memory task with mathematical achievement (r(215) = 0.51, p < 0.001, 95% CI ) and with verbal intelligence (r(217) = 0.06, p = 0.359, 95% CI ). Indeed, our construct correlated significantly with another mathematical task, but not with a non-mathematical task.Considering that the mathematical memory task is a newly created task, we first analyzed its psychometric properties. Due to the dropout rate mentioned above, we had data from more participants than used for the final sample. Thus, we decided to use this larger sample . In this task, participants were presented with a figure showing in a pictograph how many books were sold per weekday. They had three minutes to make up as many problems as possible and to write down the mathematical formula of the solution.46). In the verbal task , participants have to find as many unusual uses as possible for a common everyday object, here a tin can. In the figural task (circles), participants must draw as many figures as possible from empty circles. We limited the maximum time for each task to 3 min.Domain-general creativity was measured with one verbal and one figural task from the Torrance Test of Creative Thinking (TTCT47). For the analyses we z-standardized scores and aggregated fluency, flexibility, and originality for each item. Then we calculated means for each creativity category , and for mathematical creativity and domain-general creativity in general. More information about the specific study material, scoring and reliability of the measurements can be found in the All mathematical as well as domain-general creativity items (except for the two overcoming fixation items) were scored for fluency (number of correct answers), flexibility  and originality . For every item in the verbal task (finding similarities) five words were presented, however, only four of them have something in common. Participants had to find out, which word does not belong to the others. We used the standard set of 20 items and adjusted the time limit to 3 min. In every item of the figural task (recognizing cubes) seven three-dimensional cubes with different patterns on each side are presented. Participants had to find out if the cube presented on the left side is identical to one of the cubes on the right side, and if yes, to which of them. To this end, the cubes had to be mentally rotated. We used the standard set of 17 items and adjusted the time limit to 5 min. All correct answers were summed up to a raw score for each task.Intelligence was measured using two tasks from the Intelligence Structure Analysis (ISAhttp://www.limesurvey.org). In the introduction general information about the study and detailed information about data protection were provided. After participants gave their written informed consent, participants created a unique code to guarantee anonymity. Next, participants reported their demographical data (5 min) and worked through the cognitive tasks. Tasks on mathematical memory (18 min), mathematical creativity (21 min), domain-general creativity (6 min) and mathematical achievement (15 min) were programmed with PsychoPy3 online50 and implemented via Pavlovia (https://pavlovia.org/). Each task was initiated via a specific link. Intelligence (10 min) was the last cognitive task and directly implemented in Limesurvey. At the end, participants reported their e-mail address to be contacted for monetary reimbursement. On average, participants needed approximately 75 min to complete the test session.The study consisted of an online test session created with Limesurvey  × 2 within-subjects (condition: structured vs. unstructured) mixed ANOVA for each of the six items  = 23.50, p < 0.001, ηp2 = 0.10; Numerical Series: F = 13.73, p < 0.001, ηp2 = 0.06; Caylay Table: F = 20.49, p < 0.001, ηp2 = 0.09; Graphs: F = 0.46, p = 0.501, ηp2 < 0.01; Triangles: F = 10.82, p = 0.001, ηp2 = 0.05; Theorem and Proof: F = 11.84, p < 0.001, ηp2 = 0.05). While experts recalled more units than novices in the structured condition of the numerical items  = 3.97, p < 0.001, d = 0.93, BF10 = 1.72e+7, BF01 = 5.83e−8; Numerical Series: MD = 1.65, p < 0.001 d = 0.84, BF10 = 11,501, BF01 = 8.70e−5; Caylay Table: MD = 5.77, p < 0.001, d = 0.89, BF10 = 486,661, BF01 = 2.06e−6), no group difference emerged in the unstructured condition . In the Triangles item, a group difference emerged in the unstructured condition , but not in the structured condition , probably due to a ceiling effect. Furthermore, experts (compared to novices) recalled more units both in the structured  and the unstructured condition of the Theorem and Proof item , but the difference between groups, indicated by the effect size, was much larger in the structured condition. There was no significant interaction in the Graphs item: experts recalled more units in both the structured  and the unstructured condition .The interaction The overall pattern of the results indicates that, except for the two figural items, the difference in recalled units between experts and novices was larger in the structured than in the unstructured conditions.condition was significant for all six items  = 478.97, p < 0.001, ηp2 = 0.69; Numerical Series: F = 118.79, p < 0.001, ηp2 = 0.35; Caylay Table: F = 550.55, p < 0.001, ηp2 = 0.71; Graphs: F = 97.40, p < 0.001, ηp2 = 0.32; Triangles: F = 415.30, p < 0.001, ηp2 = 0.66; Theorem and Proof: F = 71.78, p < 0.001, ηp2 = 0.25), confirming that structured material was better remembered than the unstructured material.The main effect group was also significant for all six items  = 25.02, p < 0.001, ηp2 = 0.10; Numerical Series: F = 25.83, p < 0.001, ηp2 = 0.11; Caylay Table: F = 23.67, p < 0.001, ηp2 = 0.10; Graphs: F = 52.12, p < 0.001, ηp2 = 0.20; Triangles: F = 11.67, p < 0.001, ηp2 = 0.05; Theorem and Proof: F = 19.42, p < 0.001, ηp2 = 0.08), confirming that experts remembered more units than novices.Additionally, the main effect To test whether experts are more creative than novices in mathematical and domain-general  creativity, we calculated independent t-tests for all items and both tasks (averaged across the respective items). There were no outliers removed and the assumptions of normality and homogeneity of variances were not violated. Experts showed a significant higher performance in all tasks with mathematical material  have a higher memory capacity for structured mathematical material but not or to a smaller extent for unstructured, mathematically meaningless material. In addition, our results in well-established creativity tasks revealed a higher performance of experts only in domain-specific  but not in domain-general  creativity tasks.The aim of the present study was to investigate the interaction between expert knowledge in mathematics and domain-specific information processing in memory as well as domain-specific and domain-general creativity. Using a mathematical adaptation of Chase and Simon’sPascal’s triangle, Numerical Series, and Caylay Table). In the verbal item (Theorem and Proof), the group difference in favor of experts was larger in the structured than in the unstructured condition. These results are in line with our hypothesis as well as with previous work in several domains19, and indicate that experts were successful in recognizing the underlying mathematical structure due to their large and well-organized domain-specific knowledge base. The items differed with regard to how helpful identifying the underlying structure was. For example, if one was able to identify the underlying structure of the Pascal’s Triangle, it was possible to reproduce it by using the appropriate procedural strategy. In contrast, for the Theorem and Proof, identifying the underlying structure  was beneficial for remembering but not sufficient to reproduce all units/words. In the latter item, the small group difference in the unstructured condition may have occurred because experts perceived some meaningful patterns by chance14.The interactions between group and condition in the mathematical memory task revealed the expected pattern of experts outperforming novices only in the structured condition in three of the six memory items, all containing numerical material , whereas novices may have tried to store the entire figure. This information processing difference could explain the similarly strong effect of expertise in the structured as well as unstructured condition of the Graphs item. Further, experts had higher figural intelligence scores than novices, which could allow them to manipulate figural information more easily in general. The lack of group difference in the structured condition in the Triangles item, in contrast, can very likely be traced back to a ceiling effect.In contrast to our hypothesis, we did not observe an expert advantage only for structured information in the figural items. In the 52. This divergence is very likely due to the expertise domain and the definition of novices. Even though the novices received no tertiary education in mathematics, they had a considerable amount of mathematical knowledge acquired in math education during primary and secondary school, while in the studies reported above, novices had no or minimal experience with the task material. The presentation time of 20 s was probably long enough for novices to also discover some of the underlying structures within our experimental materials.Another finding shown by the post-hoc effects is that novices remembered more units in all structured compared to unstructured conditions. This stands in contrast to findings from other domains, where novices performed similarly in both conditions53) and corresponds to results in non-experts showing that higher mathematical expertise is related to higher mathematical creativity54. They do not support the assumption that experts have more rigid thoughts, which hinders them to be creative55; rather, prior knowledge and experiences can be regarded as the basis for creative thinking23. Further, the present results suggest that mathematical creativity strongly relies on domain-specific skills. According to the APT40, mathematical and domain-general creativity fall into different general thematic areas, thus not requiring the same traits to be creative. This is further supported by a non-significant correlation of mathematical creativity with domain-general creativity . To some extent, the present findings correspond to the recent study by Palmiero et al.53 who reported a significant difference between expert musicians and self-taught musicians/non-musicians in domain-general verbal creativity but not in domain-general visual creativity. The authors explained this result as the consequence of shared musical and verbal processing mechanisms. While this is a plausible explanation, there is also another methodological possibility. Since the groups were not matched for intelligence, the differences could have also been driven by differences in general verbal intelligence, as according to the APT, intelligence is an initial requirement for creativity in all domains40. However, in the present study, experts and novices did not differ in verbal intelligence, even though the experts had a higher figural intelligence score than novices. Thus, this could be one reason why we did not observe group differences in our measures of domain-general creativity. Future research needs to clarify whether some of the reported associations between domain-general creativity and expertise are genuine effects or methodological artifacts.Our results in the creativity tasks revealed that experts exhibited higher performance in domain-specific  but not domain-general  creativity. The superiority of experts in domain-specific creativity replicates the findings from other expertise domains .A first limitation of the present study lies in the online administration of the tasks. While this procedure allowed us to investigate the up-to-date largest sample of experts in mathematics, the environment of the participants in the test session may have varied. Parameters influencing the performance like time of day, a quiet environment, and interruptions could not be controlled. Further, we have no insight into the participants’ commitment, even though one could argue that all participants who spent approximately 75 min on cognitively demanding tasks had to show at least some amount of engagement. Second, while both the mathematical memory as well as the mathematical creativity task could successfully differentiate between experts and novices, we do not know to what extent it can differentiate within experts. Further studies, using more complex items for both memory and creativity, should explore this question, especially as previous studies could show that even within experts differences occur in memoryIn conclusion, using a novel mathematical memory task, we could demonstrate that experts have a higher memory capacity for complex domain-specific material when it is representative of their domain of expertise. To some extent, this performance advantage generalizes to unstructured material in which the experts still may be able to benefit from their vast mathematical knowledge during encoding and retrieval. Experts also exhibited a higher mathematical creativity than novices but did not differ from them in their domain-general creativity, providing further evidence for a domain-specific perspective of creativity. Both lines of findings corroborate the importance of the experts’ knowledge base in the perception and processing of domain-specific material and provide new insights into the characteristics of mathematical expertise.Supplementary Information.


Recruitment of enzymes to intracellular membranes often modulates their catalytic activity, which can be important in cell signaling and membrane trafficking. Thus, re-localization is not only important for these enzymes to gain access to their substrates, but membrane interactions often allosterically regulate enzyme function by inducing conformational changes across different time and amplitude scales. Recent structural, biophysical and computational studies have revealed how key enzymes interact with lipid membrane surfaces, and how this membrane binding regulates protein structure and function. This review summarizes the recent progress in understanding regulatory mechanisms involved in enzyme-membrane interactions. Protein-membrane interactions play a crucial role in cell signaling and membrane trafficking . MembranHere, we emphasize recent biophysical studies that help to describe the mechanisms of allosteric regulation upon enzyme-membrane interactions, help to identify common themes amongst these proteins, and provide a future road map for further understanding this critical class of enzymes. We divide these enzymes into two major groups, those that primarily use an amphipathic helix for membrane engagement and those that use specialized membrane-interacting domains, including C1/C2, PH, PX and FERM domains. The examples are meant to be illustrative, not exhaustive.Many membrane-associated proteins contain an amphipathic α-helix that is often unfolded in solution but becomes folded upon membrane interaction. Such a protein segment may initially be attracted to the membrane through electrostatic interaction, but then folds into an α-helix with its nonpolar residues inserted into the membrane while polar residues face the lipid head groups. It has been proposed that these amphipathic helices especially bind to membranes with high curvature owing to their lipid packing defects .2 (PLA2). CCT is a rate-limiting and regulatory enzyme of phosphatidylcholine (PC) metabolism (Two well-studied enzymes known to interact with membranes through their amphipathic helices are phosphocholine cytidylyltransferase (CCT) and phospholipase Atabolism . Chemicatabolism  and are tabolism  has espeUpon membrane binding, the autoinhibitory (AI) segment of the membrane–lipid sensor domain (domain M) dissociates from the four-helix complex (2 αE + 2 AI helices) at the base of the catalytic domain see , as suggMembrane lipid composition and the phosphorylation status of the C-terminal region are two regulators of CCT membrane binding . High nePerhaps the most interesting aspect is the dual function of the amphipathic helix domain M: it acts as both an autoinhibitory device in CCTsol and an activating device in CCTmem. Membrane binding of domain M disrupts its autoinhibitory contact with the active site. The deletion of domain M or specifically the AI segment only leads to partial activation. For full activation, domain M must be present . Amphipa2 catalyzes the hydrolysis of acyl chains at the sn-2 position of membrane phospholipids to produce fatty acids that are important for downstream signaling. The activity of PLA2 is higher in the presence of lipid aggregates  than monomeric substrates  combined with MD simulations to understand the association mechanism of the recombinant human form of group IVA cytosolic (cPLA2), group V secreted (sPLA2), group VIA calcium-independent (iPLA2), and group VIIA lipoprotein-associated phospholipase A2 (GVIIA Lp-PLA2); they propose that PLA2 exists in at least three forms: the “closed” form in the cytosol, the “open” membrane-associated “unbound” form, and the “open” membrane-associated “bound” form when binding to a substrate (The Dennis lab  has madeubstrate .2 with phospholipid vesicles and MD simulations suggest that an amphipathic helix (residues 708–730) close to the active site penetrates the membrane. The hydrophilic residues of the amphipathic helix interact with the headgroups of the lipids and the hydrophobic residues interacts with the fatty acid chains  conserved region 2 (C2) domain and a catalytic domain. HDX-MS showed lower deuteration levels in some peptide regions derived from the C2 and catalytic domains in the presence of phospholipid vesicles (cPLAvesicles . Accordivesicles .2 (residues 114–120 and 360–368) are involved in membrane binding. MD simulations show that the volume of the active site is increased upon membrane binding and the amphipathic helical region (residues 100–130) is responsible for this conformational change  domains were first identified in PKCs and most of them bind to diacylglycerol (DAG) and phorbol ester . They ar2+ and DAG. Novel PKC isoenzymes  are activated by DAG alone. Atypical PKCs  are regulated by protein-protein interactions.PKC is a Ser/Thr kinase and is one of the best-studied paradigms of enzymes that are re-localized to membranes and then activated by lipids. All PKC isoenzymes consist of the N-terminal regulatory domain, C-terminal catalytic domain and a hinge between them. Conventional PKC isoenzymes  are activated by two second messengers CaThe activation of cPKCs requires a conformational transition from an inactive cytosolic form to an active membrane -bound form. The inactive form is autoinhibited by trapping the pseudosubstrate (PS) region into the active site of kinase domain. The autoinhibitory interaction is released upon interaction of the regulatory domain with lipid membrane.2+-sensing C2 domain clamps the autoinhibitory PS region in the active site of the kinase domain, and the DAG-sensing C1 domains is masked. Hydrolysis of phosphatidylinositol-4,5-bisphosphate P2) generates second messengers, DAG and inositol-1,4,5-trisphosphate (IP3). IP3 stimulates the release of Ca2+ from the endoplasmic reticulum. There are two major steps leading to the activation. First, mutagenesis and single-molecule studies show that Ca2+ binds to C2 domain and mediates the engagement of PKC on the plasma membrane through bridging the C2 domain to phosphatidylserine and PIP2 P2 . Thus, tIP2 , which rIP2 . It shouIP2 .Studies of energetics suggest that membrane binding of C1 and C2 provides the energy to release the PS domain from the active site . However2+-dependent manner. Some C2 domains bind to anionic lipids like phosphatidylserine and phosphoinositides (PIPs) and some bind to zwitterionic lipids like PC P2 in the PTEN/PI3Kα/Akt signaling pathway. The product PIP2, also the allosteric activator, facilitates PTEN hydrolysis of PIP3, which creates a positive feedback loop (2 (2 binding domain (PBD), near the active site (2+-independent C2 domain.Phosphatase and tensin homolog (PTEN) desphosphorylates PIPack loop . The cat loop P2 and full activation of PTEN   covers the CBR3 loop and Cα2 in C2 domain and the phosphatase domain, which interferes with the membrane-binding interface and results in a closed, inactive state of PTEN in the cytosol see  Odriozo. PBD binelix PBD . The unf of PTEN .There are some questions about the PTEN regulation that remain to be studied. First, since CTT is an intrinsically disordered region (IDR), it is difficult to obtain a picture of the proposed closed state . Second,3 to generate PIP2. The SHIP family, consisting of SHIP1 and SHIP2, share a high level of sequence and structural homology, but have different tissue expression patterns. A previous study  assays suggested that purified C2 domain bound to PIP2, although there are some concerns about PLO assays being artifact prone. These data indicate that the C2 domain is needed for the allosteric activation of SHIP1 and that allosteric activators such as PIP2 might bind to the C2 domain.The Src homology 2 domain-containing inositol 5-phosphatase (SHIP) dephosphorylates PIPus study  showed t2 and pleckstrin homology-related (PHR) domain (PHR-PP-C2) binds to PIP3. Molecular dissection showed that the full-length SHIP1 was autoinhibited by the N-terminal Src homology 2 (SH2) domain and the disordered C-terminus compared to the central PHR-PP-C2. They also found that phosphatidylserine enhanced the activity of full-length SHIP1, but the overall activity was lower than that of PHR-PP-C2 lacking autoinhibition. They suggest that the autoinhibition of SHIP1 is regulated by a mechanism that is partially resistant to phosphatidylserine-mediated activation, and phosphatidylserine may bind to the C2 domain, leading to allosteric activation.A recent study  used sin.Studies by the Lietha lab established that the C2 domain stabilizes the phosphatase (Ptase) domain and promotes membrane binding . With su2 induces higher activity of PHR-Ptase-C2 compared to Ptase-C2 and Ptase. The direct binding of the PHR domain and PIP2 was indicated by a fluorescence polarization assay. They proposed a model where both the PHR and C2 domains interacted with the membrane, resulting in optimal positioning toward the substrate and allosteric activation of SHIP2. They also proposed allosteric communication via the PHR-Ptase linker interacting with the Ptase-C2 linker.More recent MD simulations  with SHIA simulation and calculation work also compared the role of C2 domains in PTEN and SHIP2 . This stPleckstrin homology (PH) domains consist of a 7-stranded β sandwich structure together with a C-terminal α helix see  Lemmon . They ar3 or PIP2 binding to the PH domain allosterically activates Akt by unmasking the substrate binding site , is one of the key proteins in the phosphoinositide 3-kinase signaling pathways. There are three Akt isoforms, Akt1, Akt2 and Akt3, that all have an N-terminal PH domain, an unstructured linker sequence, a kinase domain, and a C-terminal regulatory domain. PIPing site .3 binding have been elucidated by small-angle X-ray scattering (SAXS) and HDX-MS experiments P2 in the plasma membrane leads to the binding of Akt and release of autoinhibition, the PH domain is displaced from the catalytic cleft, the activation loop and the hydrophobic motif are exposed for phosphorylationP3 is required for sustained Akt phosphorylation P2 results in Akt inactivation by returning it to the autoinhibited form. The phosphorylated activation loop and hydrophobic motif are released and then accessible for dephosphorylation P2 binding and phosphorylation are required for Akt activation. The significance of activation in the presence of PIP3 or PIP2 is thought to enhance the substrate specificity and reduce the potential crosstalk between signaling pathways Periments . In the ationsee   is a protein kinase that is critical in activation of B cells. Btk consists of a PH domain fused with a Tec homology (PH-TH) domain, a proline-rich region (PRR), a Src homology 3 (SH3) domain, a SH2 domain, and a C-terminal kinase domain. The activation of Btk depends on the recruitment to the plasma membrane. The PH-TH domain binds two PIPtivation .3 binding. After Btk is recruited to the membrane containing PIP3, a domain rearrangement can occur and likely leads to a second autoinhibitory structure with the PH-TH domain adopting the Saraste dimer P3 through both the canonical and peripheral sites on the PH domain to stabilize membrane interactions P3 concentration on the surface thus leading to better regulation of Btk (3-induced dimerization in Btk activation is not clear and awaits further study.Fluorescence correlation spectroscopy (FCS) shows that by binding to multiple PIPn of Btk . However2 and generate phosphatidylinositol and PI5P, respectively (Myotubularins (MTM) are phosphatidylinositol phosphatases, which dephosphorylate the D-3 position of PI3P and PIPectively . The proectively . The PH ectively . The str2, and they may play different roles in membrane associations and activation in different PLD isoforms (2 (Phospholipase D (PLD) is a transphosphatidylase that hydrolyzes PC to generate phosphatidic acid (PA) . Two canisoforms . A recenforms  domains have three compact lobes see  and are 2-rich membranes in focal adhesion induces a multistep activation sequence, which causes FAK oligomerization and an conformational change for autophosphorylation and activation  is a non-receptor tyrosine kinase and is composed of a N-terminal FERM domain, a central kinase domain, an unstructured proline-rich region, and a C-terminal focal adhesion targeting (FAT) domain. FAK binding to PIPtivation .2 in the membrane through electrostatic interactions P2 in the membrane and places the active site towards the membrane  to promote its activation synergistically with Rac1/Cdc42 Pc1/Cdc42 . Arachidc1/Cdc42 . Anotherc1/Cdc42 .2 bind to the core of the protein in the unbound, inactive form. Rotation of these helices exposes the hydrophobic side for binding to the membrane. Amphipathic helices enable the protein to sense changes in physical properties of the bilayer. Thus, proteins containing amphipathic helices not only bind to specific lipids but respond more generally to negative surface charge and lipid packing voids of the membrane.Here, we have described the mechanism of membrane binding and subsequent conformational changes for key peripheral membrane binding enzymes. These actions not only re-localize these enzymes to these membranes, but also modulate their catalytic activity. We have categorized enzymes into where an amphipathic α -helix or a specialized lipid-binding domain is most important for membrane engagement. The amphipathic α-helices in CCT and PLAIn contrast, lipid-binding domains consist of a binding pocket for a specific lipid, especially different phosphoinositide lipids, and enable the protein to be regulated by the concentration of a specific lipid in the membrane. In addition, the presence of more than one lipid-binding domain allows cooperation between lipids to increase the binding affinity and enzyme activity, diversifying enzyme regulation. For example, regulation of PKC’s function by two lipid-binding domains provides higher sensitivity in the activation process.The general regulation strategy used by many peripheral proteins is to displace an autoinhibitory domain or release the intramolecular interaction upon membrane binding. Some peripheral proteins undergo oligomerization or dimer dissociation during membrane binding, where the changes of intramolecular and intermolecular interactions both contribute to the allosteric regulation. Moreover, positive feedback via allosteric activation by reaction products is observed in some phosphoinositide phosphatases, which is an important part in lipid metabolism. These processes are also often induced by changes in the phosphorylation state of these proteins. Other post-translational modifications (PTMs), especially palmitoylation and acylation, are also important for membrane recruitment and are responsible for fine-tuning enzyme function. Cysteine palmitoylation attaches a long chain fatty acid to the protein, increases its hydrophobicity and membrane-binding affinity . Palmitoin vivo may also help us to understand how membrane localization and allosteric activation happen inside cells. These studies will continue to reveal important concepts in membrane binding and allosteric regulation, and provide novel mechanisms by which to modulate these functions through small molecules, serving both as chemical probes and potential starting points for pharmaceutical development.While much has been revealed about membrane binding and consequent allosteric regulation, high-resolution protein-membrane binding structures from X-ray crystallography or cryo-EM are still required to map the spatiotemporal activation sequence for most enzymes mentioned above. Imaging studies to reveal the co-localization of membrane-bound proteins with specific lipids


Psychometric properties of the Tilburg Frailty Indicator (TFI) have shown low internal consistency for psychological and social domains, and evidence for its structure validity is controversial. Moreover, research on TFI is frequently limited to community dwellings.To evaluate structural validity, reliability, and convergent and divergent validity of the Spanish version of the Tilburg Frailty Indicator (TFI) in both community-dwelling and institutionalized older people.n = 457) recruited from both community settings (n = 322) and nursing homes (n = 135). Participants completed the TFI and other frailty instruments: Fried’s Frailty Phenotype, Edmonton Frailty Scale, FRAIL Scale, and Kihon Checklist (KCL). Confirmatory Factor Analysis (CFA), and reliability and validity coefficients were estimated.A cross-sectional study was conducted on Spanish older adults . The three-factor CFA model showed better fit indices after depurating these items. Reliability estimates were good (CRI ≥ 0.70) for physical and psychological domains in the institutionalized sample, while in the community dwellings, only physical domain reliability was adequate. Convergent and divergent validity of physical and psychological domains was good, except for some alternative psychological measures highly correlating with the TFI physical component (KCL-depressive mood and Edmonton mood). However, the social domain showed low correlations with some social indicators.The findings of this study clarify some of the controversial validation results of the TFI structure and provide evidence to improve its use in psychometric terms.NCT03832608. Studies on frailty are increasing in literature on aging. There is no consensus on its definition, but it is generally recognized as a state of increased vulnerability that is associated with high risk of adverse outcomes, such as falls, disability, and even mortality , 2. TradWithin this construct, the Tilburg Frailty Indicator (TFI) is a multi-domain frailty instrument, developed in 2010 as a screening tool for frailty , 5. It hAmong all validations, only the Spanish , Turkish.  with CRI = 0.803; alpha for the psychological domain = 0.410, 95% CI  and CRI = 0.662; and for the social domain, alpha = 0.315, 95% CI  and CRI = 0.518. The estimates for the institutionalized older adults were: for the physical domain, alpha = 0.764, 95% CI  and CRI = 0.894; the psychological domain had an alpha = 0.608, 95% and CI  and CRI = 0.769; and finally, the alpha for the social domain was 0.378, 95% CI  and CRI = 0.682.Spearman’s correlations were calculated among the three dimensions of the TFI  and the domains of the alternative frailty scales . As there are several multidimensional instruments and dimensions that somehow relate to the three dimensions in the TFI, convergent validity will show if correlations among clearly related dimensions are large, and divergent validity will show if these correlations are lower for dimensions not so closely related. These correlations are presented in Table Convergent validity of the physical domain was fair, it significantly correlated as expected with physical measures, but those correlations were not superior to 0.5. Results related to the analysis of this domain also suggested reasonable divergent validity, showing that the construct of this domain was unrelated with cognitive function  and social dimensions, except for the KCL-depressive mood and Edmonton mood psychological domains, whose constructs were similar to TFI physical domain in both samples.The psychological domain correlated with other domains of the alternative frailty instruments, demonstrating some convergent and divergent validity with a similar pattern in both samples. However, the amount of the correlations cannot be considered adequate according to the COSMIN guide. Thus, this domain was related or similar to the psychological domains of the other scales used to compare  and this psychological dimension was unrelated with physical and social domains.The social domain suffers from both convergent and divergent validity problems. The correlations of this domain are unrelated with all other measures of frailty. This pattern of correlations does not even meet the COSMIN guideline adequacy criteria for related constructs. Although, in terms of convergent validity, there are related constructs in both samples with Edmonton’s social support dimension, there are unrelated constructs with KCL-socialization/isolation.Our findings aim to offer further insights on the TFI structure and provide evidence to improve its use in psychometric terms.The need to confirm the structure of the scale is clear, given the available evidence gaps in some relevant measurement properties , 21, 22.Similarly, in the institutionalized sample, the three-factor solution fit better than the one-factor, but only fit well after depurating the poorly behaving items . To our knowledge, no studies have previously analyzed the adequacy of the TFI in institutionalized older adults.p > 0.20 in the bivariate analyses [The need to review the TFI model and refine some indicators of the scale has also been suggested by other studies, when analyzing the poor correlations with other items or other similar measures. Among the indicators to be checked, the following have been pointed out: unexplained weight loss (2) , 10, 29,analyses .A recent systematic review of the psychometric properties of the TFI  conclude. [Reliability estimates were not equal in both samples, showing better values for the institutionalized older adults. The CRI values for the physical and psychological domains were good CRI > 0.70) in the institutionalized sample, while only the physical domain was satisfactory in the community-dwelling older adults. In both cases, social domain reliability estimates were not acceptable. These differences between the two groups may be due to a higher mean age, greater variability in the variables or a larger sample of frail people in the institutionalized group. These findings suggest that the TFI seems to be a good assessment tool to detect physical frailty, as indicated by other authors . Further0 in the . . However. . The mod. .In addition, although some authors refer to the adequate reliability of the scale considering the estimates of the total TFI –18, 21, Convergent and divergent validity for the physical and psychological domains are acceptable given the obtained results, except for some psychological measures whose constructs are similar to the TFI physical domain (KCL-depressive mood and Edmonton mood). These results are in line with several studies in which the construct of alternative psychological measures was related or similar to the TFI physical component , 16, 17.As mentioned before, previous validations have involved community-dwelling older adults. Thus, the use of the TFI in geriatrics still needs to be tested in different settings to explore its potential applications , 20, 22.In conclusion, there is a need to revise the TFI structure in more detail and refine some items. Depurating items such as weight loss, poor hearing, and poor vision improve the psychometric characteristics of the scale. The physical domain is a cornerstone as a frailty measure both in community-dwelling and institutionalized older people. However, social component needs further clarification in psychometric terms but also in how it stands within the construct of frailty.


Wheat seed classification is a critical task for ensuring crop quality and yield. However, the characteristics of wheat seeds can vary due to variations in climate, soil, and other environmental factors across different years. Consequently, the present classification model is no longer adequate for accurately classifying novel samples. To tackle this issue, this paper proposes an adaptive domain feature separation (ADFS) network that utilizes hyperspectral imaging techniques for cross-year classification of wheat seed varieties. The primary objective is to improve the generalization ability of the model at a minimum cost. ADFS leverages deep learning techniques to acquire domain-irrelevant features from hyperspectral data, thus effectively addressing the issue of domain shifts across datasets. The feature spaces are divided into three parts using different modules. One shared module aligns feature distributions between the source and target datasets from different years, thereby enhancing the model’s generalization and robustness. Additionally, two private modules extract class-specific features and domain-specific features. The transfer mechanism does not learn domain-specific features to reduce negative transfer and improve classification accuracy. Extensive experiments conducted on a two-year dataset comprising four wheat seed varieties demonstrate the effectiveness of ADFS in wheat seed classification. Compared with three typical transfer learning networks, ADFS can achieve the best accuracy of wheat seed classification with small batch samples updated, thereby addressing new seasonal variability. Wheat is a crucial staple crop worldwide, and its quality and yield directly impact global food security. The accurate identification of wheat seed varieties holds immense significance for enhancing the quality of wheat varieties and ensuring the market order . HoweverTraditional methods for identifying wheat seed variety rely on time-consuming and subjective visual inspection by experts . The advRecently, deep learning combined with hyperspectral images has been widely used in the field of crop seed variety recognition ,9,10. DaTransfer learning is a method for improving the performance of deep learning models on hyperspectral image data by transferring relevant knowledge from a source domain to a target domain, where training and test data exist in different feature spaces . Yang etGenerally speaking, the tasks of the source domain and the target domain are the same, and the samples are also similar, so their distribution in the feature space will be relatively close. However, interactions between genotype and environment can lead to changes in wheat seed spectral characteristics, especially due to environmental differences across different years. This interference will cause certain deviations in the distribution of the source wheat seeds and the target wheat seeds in the feature space. These biases are the main reasons that lead to the decline of wheat seed classification performance under different scenarios. Domain adaptation is a research area that focus on transfer learning models in the computer vision field . When thMost of the domain adaptive transfer learning strategies focus on learning domain feature representation, aiming to learn a feature that is specific to the category but not related to the domain ,24. In oThe remainder of this paper is organized as follows. In order to investigate the proposed model’s detection ability for wheat seeds from different years, this study collected four different varieties of wheat seeds harvested in 2019 and 2020, namely Bainong4199 (BN4199), Jimai44 (JM44), Weilong169 (WL169) and Zhoumai33 (ZM33). These four types of wheat seed are all semi-winter varieties with strong gluten and high-yield characteristics. All wheat samples were provided by Sinochem Modern Agriculture Co., Ltd. in Beijing, China. For each wheat, 1000 visually undamaged seeds with intact appearance were selected for hyperspectral image sample collection. A total of 8000 hyperspectral images of wheat seeds were collected. The specific information is shown in This study utilized a line-scan hyperspectral imaging system operating in the visible/NIR range (400–1000 nm) to collect hyperspectral images of wheat seeds. In this study, the spectral resolution of the hyperspectral camera was set to 6.38 nm, the slit range of the spectrometer was 25 μm, the exposure time was set to 250 ms, and the power of the light source was set to 60 w. Each batch of wheat seeds contained 100 samples, and the resulting hyperspectral image data cube had dimensions of 1000 × 1392 × 94, with 94 representing the waveband dimension. After correcting the hyperspectral images, the wheat seed of interest needed to be isolated from the background. For facilitating subsequent modeling, zeros were added around the seed to ensure that each wheat image had a consistent size of 60 × 80 × 94. The details regarding the hyperspectral imaging system and image pre-processing can be found in the paper [Domain adaptive is a novel transfer learning strategy. By mapping inconsistent source domain data and target domain data into a feature space and aligning their distributions in feature space, the knowledge from the source domain can be transferred to the target domain, thereby improving the detection accuracy in the target domain .G, nG, cG and dG represent feature extraction modules; nF, cF and dF denote the feature map; h1, h2 and h3 are classifier modules. The specific structures of each feature extractor and classifier in the model are shown in This study introduces the idea of domain feature separation based on domain adaptation to mitigate negative transfer and enhance the model’s detection accuracy. G of the model to obtain mappings of both source and target domain samples in the feature space.In the first stage, the samples are collected for hyperspectral image, image preprocessing, and zero-fill reconstruction to form a dataset consisting of sample data with the same data size. Then, the samples in the dataset are divided into source domain and target domain according to the year, which are input into the feature extractor nG and two private feature extractors cG, dG. After abstracting the samples into low-dimensional features, the total features are refined into shared features nF, class-specific features cF, and domain-specific features dF. The shared features nF refer to the similar features between samples from source domain and target domain. While the class-specific features cF may shift with domain changes, they contain knowledge helpful for classification tasks within their respective domains. dF includes information that reflects the difference between the source domain and the target domain. On the other hand, the domain-specific features dF includes information reflecting differences between the source domain and target domain. They are not helpful to the learning task and may have a negative effect on the target domain task, so the features are not transferred during learning. In the proposed structure, transfer training is performed on nF and cF that are domain-irrelevant features.In the second stage, the feature separation module is divided into a shared feature extractor nF is domain-irrelevant and class-irrelevant, the features from the source domain samples should have a similar distribution to that from the target domain samples. Therefore, cosine distance is used as a loss function to minimize the distribution distance between the shared features from source and the target domains in the feature space. The alignment process of nF is illustrated in the red box in sX and target domain sample tX; the sample pair is input into the feature extractor G and nG at the same time. Then, the adversarial training is carried out to calculate the Cosine distances between source sample sX and target sample tX, that is the Since the knowledge represented by the shared features cF contains features that can distinguish different seed varieties, it is suitable to be used as the input for the class classifier when combined with nF. Therefore, both are input into the class classifier h2 in series and trained using the cross-entropy loss function. Similarly, dF and nF are connected in series into the domain classifier h3 and trained using the cross-entropy loss function as the same. In order to further ensure that the refined features have their corresponding feature knowledge, the cF, dF and nF are connected in series and input into domain-class classifier h1 for domain-class combination prediction and trained as same as h2 and h3. The cross-entropy loss function used for Finally, the classifiers are constructed by combining the separated features. Since There are differences in the feature space of wheat seed classification from different years. The main research directions of feature-based transfer methods include adversarial domain alignment and reconstruction-based domain alignment. Hyperspectral images are 3D images with a spectral dimension. The reconstruction-based domain alignment methods have significant variations and are not applicable to wheat seed classification. Therefore, we chose a representative method of adversarial transfer learning called domain Adversarial Neural Network (DANN) for comparison. Additionally, pre-training and fine-tuning, which are commonly used transfer methods in the spectral domain, were also employed.DANN: DANN was proposed by Yaroslav Ganin et al. . It consPre-training model: The model first used the source domain sample set for pre-training, and then used a small number of samples from the target domain for updating all the parameters so as to transfer the knowledge from source domain to target domain. The model structure is shown in Fine-tuning model: The method was similar to the pre-training model, as it also utilized the source domain samples for training. In contrast, the parameters of the shallow feature extraction layer of the convolutional neural network are frozen. Then, a small number of samples from the target domain are used for updating to achieve the purpose of transfer learning. The model structure is shown in The pre-training model and fine-tuning model are based on the same initial CNN that was trained using the source domain samples. The updatable block represents the modules initialized with parameters from the initial model and then updated using target domain samples. The fixed block represents the modules with fixed parameters loaded from the initial model.In order to enhance the robustness of the detection model in the open environment, this study proposed a novel transfer strategy called ADFS. The wheat seeds harvested in two different years were used as experimental samples. The proposed ADFS was compared with three common transfer methods, and experiments were carried out for transferring from 2019 to 2020 as well as from 2020 to 2019.−3, a gradient decay rate The structure of initial CNN model is shown in The comparison of classification results is summarized in Interestingly, when wheat seeds from different years were used for cross-year prediction, the classification accuracies were less than 50%. The poor results were due to inconsistent data distribution in different years. It is difficult to meet the detection requirements by relying solely on the generalization performance of the CNN model itself. When using samples from the source domain, the model cannot learn the feature information of the target domain. It is apparent that WL169 has the worst classification accuracy in cross-year prediction. As can be seen from There were two years of wheat seed samples, and cross-year predictions for different years were compared. In the cross-year predictions, the source target samples were randomly divided into training sets and test sets according to 4:1. The 20% of the target samples with labels were used to update the model for pre-training and fine-tuning models. For DANN and ADFS, 10% of the target domain samples with labels and 10% of the target domain samples without labels were used to optimize the model parameters; the remaining 80% of the target domain samples were tested. The parameter The comparison of cross-year predictions output by different transfer learning models is shown in By introducing domain adaptive, the DANN and ADFS can achieve more than 90% classification accuracy after semi-supervised learning. At the same time, target domain labels are used less than the pre-training and fine-tuning model. The core of the adversarial domain adaptive method adopted by DANN and ADFS is to learn a good enough representation to reduce the difference of different data distributions at the representation level. The proposed ADFS model combines the idea of domain self-adaptation, uses different modules to extract class-specific features, and refines the distribution difference between the source domain and target domain. Adversarial training is used to ensure that the shared parameter spaces are aligned. After the feature is refined, some features that will produce negative transfer and affect the classification effect of the model in the target domain are discarded. Overall, ADFS outperforms DANN in terms of accuracy for wheat seed classification. The transfer performance of ADFS with 10% labeled samples and 10% unlabeled samples is better than 50% of the target sample training model.The confusion matrix of the classification results for the target test set by ADFS is shown in nF, which are alignment features across scenarios. It is apparent from this subgraph that the shared features are jumbled together and do not shift in distribution with the change in the scenario. The middle subgraph is a visualization of the combined shared features nF and class-specific features cF; these features are input into the class classifier. These features are domain-irrelevant, so the source domain and target domain cannot be distinguished. However, there is a difference between different categories. After adding domain-specific features dF, the visualization of the features is shown in the right subgraph. Samples of different domains and different categories can be well clustered together. However, the cluster is relatively scattered and some cluster boundaries overlap.The shared module captures some shared features across scenarios, the private module captures class-specific features. In order to provide richer judgment for the network, T-Distribution Stochastic Neighbour Embedding (T-SNE)  was usedIn order to further study the influence of labeled sample proportion on the stability of the ADFS, experiments of ADFS and DANN with different proportion samples were conducted in this section. The sample division is shown in The classification results of wheat seed varieties after adding different proportions of labeled target samples are shown in From nF and all feature set nF + cF + dF. The classification accuracies are shown in The feature extraction of the ADFS network is carried out through three different modules, which respectively extract shared features class-specific features and domain-specific features. In order to further verify the effectiveness of feature separation and extraction, ablation experiments were performed. The ablation experiment compares the classification results of different separated feature sets, using only the aligned shared feature nF constructed in the ASDF network does not include features of classes and domains. Therefore, the classification accuracy using nF is the worst. As shown in nF and class-specific cF can obtain the best classification accuracy. Further domain-specific feture dF is added and the results become worse. It is precisely due to that dF is useless for classification, thus causing negative transfer to reduce the classification accuracy.The shared feature In order to address the issue of hyperspectral data transfer for wheat seeds from different years, an ADFS network based on shared and private features is proposed. The network differentiates the feature space into a shared feature space that captures features shared across domains and two private feature spaces that capture features unique to each domain or each variety. After differentiation, the effect of reducing negative transfer is achieved by removing the domain-specific features. Wheat varieties from two different years are used for experimental verification. The results demonstrate that ADFS achieves a classification accuracy of 93.51% and 91.69% when a small number of target domain samples is added. Compared to fine-tuning, pre-training, and DANN approaches with the same target domain sample size, ADSF performs the best in terms of cross-year classification accuracy. The overall results showed that the ADSF network was feasible and could provide accurate cross-year wheat seed variety identification, which would help to develop a system for quality detection of wheat seed under different scenarios.This study focused on developing a transfer learning network to identify wheat seeds to address data drift across different years. However, the ADSF network still requires a small number of labeled samples for cross-year transfer. In order to better adapt to practical cross-year prediction, future research will concentrate on the unsupervised transfer network with high accuracy.


In situ temperature-dependent resistance measurements were conducted by using a four-point probe system to study the changes in resistance of the 70 nm thick Cu films on different dielectrics with thick diffusion films of 30 nm Mn, Co, and W films in a N2 ambient. The lowest possible change in thickness due to annealing at the higher temperature ranges of 325 °C to 375 °C is displayed, which suggests the high stability of PSPI. The PSPI films show good adhesion with each Cu diffusion barrier up to 350 °C, and we believe that this will be helpful for new packaging applications, such as a 3D IC with a Cu interconnect.Photosensitive polyimides (PSPIs) have been widely developed in microelectronics, which is due to their excellent thermal properties and reasonable dielectric properties and can be directly patterned to simplify the processing steps. In this study, 3 μm~7 μm thick PSPI films were deposited on different substrates, including Si, 50 nm SiN, 50 nm SiO Semiconductors and power-efficient devices are receiving more attention, which is due to their extremely efficient operational properties and extraordinary working capabilities, with worldwide advancement ,2,3. The2), polybenzoxazole (PBO), benzocyclobutane (BCB), and photosensitive polyimide (PSPI), have been used [Heterogeneous integration (HI) is very well known as the aggregation or integration of various components in higher-level assembly. Hence, HI can deal with the above-mentioned threat with the advanced packaging of all the components on one chip. Copper (Cu) plays an excellent and crucial role in HI and advanced packaging due to its high conductivity, integration density, and direct connectivity with the advanced packaging of all the components on one chip ,12,13. Heen used ,24,25. PSPI is favorably adopted among all the low-k materials because of its advantage of photolithographic properties for making simplified patterns with high precision and accuracy . However2, 100 nm Cu, and 100 nm Al substrates for the optimization of the process of integration with Cu films. To understand the feasibility of advanced packaging, the above different substrates were chosen. The changes in the resistivity of the 70 nm thick Cu films on different dielectrics with 30 nm thick diffusion films, such as Ta, Co, and W films, in a N2 ambient were measured by using a four-point probe in in situ temperature-dependent resistance measurements. The change in thickness after annealing was studied at a higher temperature range from 325 °C to 375 °C. The PSPI films show good adhesion with each Cu diffusion barrier up to 350 °C, and we believe that this will be helpful for new packaging applications, such as a 3D IC with a Cu interconnect. Considering the above, in this study, 3 μm~7 μm thick PSPI films were deposited on Si, 50 nm SiN, 50 nm SiO2/Si structure. The mechanical property testing was carried out on a nanoindentation tester (NHT3 Anton Paar).The PSPI material was bought from Toray (PW-1500 series). The deposition of PSPI was carried out using a spin coater. The annealing process was performed using a tube furnace. For the thickness measurements, a KLA Tencor D-600 stylus profilometer was used. The microscope images were recorded using an OLYMPUS U-MSSP4 microscope. The electrical measurements were conducted on an Agilent 3600 probe station. The resistivity was measured by fabricating a Cu/PSPI/SiO2 were prepared. Onto Si films, 50 nm of SiN and 50 nm of SiO2 were deposited using the PECVD method. For better adhesion, hexamethyldisilazane (HMDS) was spin-coated on the 150 mm and/or 200 nm diameter wafers at 110 °C for 60 s. by using a commercially available track . These substrates were further used for the spin coating and photopatterning of PSPI.Before photopatterning of PSPI, different substrates like Cu, SiN, and SiO2. An aligner and a stepper  were used for all of the PSPI patterning processes after a soft bake at 120 °C for 6 min. The final targeted film thickness was split in the range from 3 μm to 7 μm, and the 7 μm thick prebaked film for patterning was exposed at approximately 550 mJ/cm2 ambient displays a similar behavior for both the 3 and 5 µm PSPI samples. This may be attributed to the slow heating rate, which stabilizes the damage that occurs due to fast heating. The changes in resistance of the 7 μm target-thickness PSPI films under different annealing conditions on 50 nm SiN, CVD SiO2, and 100 nm Al substrates were studied. The resistance changes of the PSPI sample under FGA (350 °C) and a N2 ambient for 325 °C and 350 °C (2.5 °C/min) were measured. The N2 ambient shows negligible change in resistance at 350 °C. However, the FGA environment causes an increase in the resistance of the SiO2 substrate. The resistance change of the Al substrate is not constant at all. The differences in the thermal and electrical conductivity of the base substrates may have caused the observed change in the resistance of the Cu films. The annealing of PSPI films under different conditions can affect different properties of the polymer material. 2 environment, carbon from PSPI is not spread through Cu films. This proves the high stability of PSPI. Further, the stability in thicknesses, which is due to different annealing conditions, suggests that PSPI can be used in fan-out wafer-level packaging.The annealing of PSPI films in different temperatures strongly affects the film thickness. For thickness measurements, stylus profilometer measurements were conducted on 5 μm thick PSPI, as shown in −10 A/cm2, which is lower than that for the other annealed conditions. Leakage current flowing through the device can reduce the performance of any electrical device. The reduction in the leakage current is very important for advanced packaging . Researc−3 mJ, which shows that the elasticity of the films was considerably lower. The resistance of a metal increases with increasing temperature, and the resistance of Cu wildly increases. However, forming interfaces with different metals can reduce this. On the other hand, metal interfaces are formed in advanced packaging systems in which metal pads are used below Cu. To test the electrical stability of Cu at different temperatures over different metal interfaces, we fabricated interfaces of Cu with Co, Mn, and W for a more detailed study. It is essential to have a low-k dielectric with good-quality mechanical properties, which keeps the microchips stable during pressurized bonding. We implemented the nanoindentation technique to study the mechanical properties of patterned PSPI films, which are shown in We studied the thermal, electrical, and mechanical stability of PSPI for advanced packaging systems. The extremely low shrinkage in the film thickness after the use of different annealing temperatures suggests that PSPI is stable at higher temperatures (350 °C). The resistance changes of Cu on different substrates under different ambient annealing conditions were studied. The stability of the resistance change in FGA and N2 ambients shows the industrial applicability of PSPI. The mechanical properties, which were tested using nanoindentation, showed that PSPI possessed a high Young’s modulus of 9.52 GPa. There was no leakage current found until 30 V. Considering these results, it can be proved that the use of PSPI in advanced packaging systems is feasible, and it can also be used for reducing Cu electromigration.


Trypanosoma and Leishmania spp.) undergo several developmental transitions essential for survival in their hosts. These transitions require membrane and cytoskeleton reorganizations that involve phosphoinositides (PIs). Phospholipids like PIs are key regulators of vital functions in all eukaryotes including signal transduction, protein transport and sorting, membrane trafficking, and cytoskeleton and membrane remodeling. A large repertoire of PI-metabolizing enzymes and PI-binding proteins/effectors carrying distinct PI-binding modules like the PX (phox homology) module could play significant roles in the life and virulence of pathogenic Kinetoplastea. The aim of this study was to retrieve the entire spectrum of Kinetoplastea protein sequences containing the PX module (PX-proteins), predict their structures, and identify in them evolutionary conserved and unique traits. Using a large array of bioinformatics tools, protein IDs from two searches (based on PFam’s pHMM for PX domain (PF00787)) were combined, aligned, and utilized for the construction of a new Kinetoplastea_PX pHMM. This three-step search retrieved 170 PX-protein sequences. Structural domain configuration analysis identified PX, Pkinase, Lipocalin_5, and Vps5/BAR3-WASP domains and clustered them into five distinct subfamilies. Phylogenetic tree and domain architecture analysis showed that some domain architectures exist in proteomes of all Kinetoplastea spp., while others are genus-specific. Finally, amino acid conservation logos of the Kinetoplastea spp. and Homo sapiens PX domains revealed high evolutionary conservation in residues forming the critical structural motifs for PtdIns3P recognition. This study highlights the PX-Pkinase domain architecture as unique within Trypanosoma spp. and forms the basis for a targeted functional analysis of Kinetoplastea PX-proteins as putative targets for a rational design of anti-parasitic drugs.Kinetoplastea are free living and parasitic protists with unique features among Eukaryota. Pathogenic Kinetoplastea parasites (i.e.,  Trypanosoma and Leishmania, several species (spp.) of which are human pathogens that cause diseases with devastating health and economic consequences. Despite similarities in genomic organization, cellular structures, and life-cycle-associated morphological changes, various Kinetoplastea pathogens cause distinct vector-borne human diseases ”; database: (type:pfam px)” (accessed on 10 December 2022).The PX-domain-containing proteins of hmmscan within the Pfam pHMM library to identify all possible known protein domains as well as their domain architectures. Subsequently, all domain architectures were manually confirmed using Uniprot. Pfam, and Interpro to assess boundaries and isolate only the PX, Pkinase, Lipocalin_5, and Vps5 domain sequences from the retrieved proteins. Consequently, the PX domains were aligned using ClustalW and, after manual curation, were submitted to Weblogo3 [All retrieved PX-proteins from Kinetoplastea were searched using Weblogo3  with defTo investigate the evolutionary relationship of Kinetoplastea PX-proteins, the retrieved sequences were aligned using ClustalW. The resulting multiple sequence alignment was used to reconstruct and visualize a distance-based phylogenetic tree employing the neighbor-joining method implemented by iTOL . Taxonom


Mechanistically, CDK5 suppressed the formation of MyD88 homodimers, resulting in the attenuated production of IFNs induced by VSV infection. Surprisingly, its kinase activity does not play a role in this process. Therefore, CDK5 can act as an internal regulator to prevent excessive production of IFNs by restricting TLR-MyD88-induced activation of antiviral innate immunity in A549 cells.As a member of the pattern recognition receptors (PRRs) involving in the innate immune system, Toll-like receptors (TLRs) can sense a wide range of microbial pathogens and combat infections by producing antimicrobial products, inflammatory cytokines, and chemokines. All TLRs, with the exception of TLR3, activate a signalling cascade via the myeloid differentiation primary response gene 88 (MyD88). Therefore, the activation of MyD88-dependent signalling pathway must be finely controlled. Herein, we identified that cyclin-dependent kinase 5 (CDK5) negatively regulated TLR-MyD88 signalling pathway by targeting MyD88. Overexpression of CDK5 reduced the production of interferons (IFNs), while a deficiency in CDK5 increased the expression of IFNs in response to  As the first line of defence against pathogenic infections, innate immunity can rapidly detect, respond to, and combat pathogen invasion. The immune system has developed a wide range of pattern recognition receptors (PRRs) to detect various components of pathogens, among which the most common are Toll-like receptors (TLRs) and retinoic acid-inducible gene-I (RIG-I)-like receptors (RLRs) ,2. Upon RLRs, such as RIG-I and melanoma differentiation-associated protein 5 (MDA5), are considered cytoplasmic receptors because of their capacity to detect viral RNA within the cytoplasm. Both RIG-I and MDA5 harbour a DExD/H-box RNA helicase domain at C-terminus and two caspase recruitment domains (CARDs) at their N-terminus. Upon sensing virus RNA with the RNA helicase domain, RIG-I and MDA5 interact with mitochondrial antiviral signalling protein  with their CARDs domain . SubsequIn contrast to RLRs, TLRs can recognize a variety of pathogens, with TLR3, TLR7, TLR8, and TLR9 primarily detecting viral nucleic acids. Except for TLR3, all TLRs transduce downstream signals through MyD88 . MyD88 cCDK5, as a member of the CDK family, requires interaction with P35 or P39, or their truncated forms, to become activated , which iIn the present study, we identified a novel kinase-independent function of CDK5 in regulating the antiviral innate immune system. It was found that CDK5 overexpression decreased the self-association of MyD88, subsequently attenuating the expression of IFN-β which was triggered by the TLRs-MyD88 signalling pathway. Moreover, the attenuated self-association of MyD88 was not dependent on the kinase activity of CDK5. These findings suggest that CDK5 may act as an intrinsic suppressor to prevent excess production of IFNs by targeting MyD88.2. Polyethylenimine  or lipofectamine 2000 (Thermo Fisher Scientific Inc.) was used for plasmid transfection according to the manufacturer’s instructions. VSV-GFP was propagated in Vero cells as previously described [A549 and HEK293T cells were cultured in DMEM supplemented with 10% foetal bovine serum (FBS) at 37 °C and 5% COescribed . All vircDNA encoding CDK5, P35 and MyD88 were amplified by PCR and inserted into SBP−2×Flag, pEGFP-N1, MC-HA-pCS2 or pCMV-Tag2B-Flag vectors. CDK5, P35, MyD88 were obtained from human cDNA with RT-PCR and then ligated into SBP−2×Flag, pEGFP-N1, MC-HA-pCS2 or pCMV-Tag2B-Flag vectors.The reagents used were as follows: 3-Methyladenine , MG132 , Roscovitine , zymosan , lipopolysaccharide , imiquimod . Antibodies: RIG-I , MDA5 , CDK5 , CDK5 , P65 , phospho-p65 , phospho-TBK1 , TBK1 , GFP-tag antibody , phospho-IRF3 , IRF3 , Flag-tag antibody , MyD88 , β-actin , phospho-Stat1 , Stat1 , Flag-tag antibody , β-tubulin , phospho-Stat2 , Stat2 , phospho-IRAK4 , IRAK4 , GAPDH , GFP (A) beads  and anti-Flag M2 beads .TRIzol was used for RNA extraction, and HiScript® II One Step qRT-PCR SYBR® Green KIT (Vazyme) was harnessed to perform real-time PCR. The mRNA levels of target genes were normalized to GAPDH.The primers (5’−3’) used were:Virus titre was determined by TCID50 via endpoint dilution assay. Briefly, Vero cells were grown in a 96-well culture plate, and the medium was discarded when a single cell layer was formed. The virus preparation was diluted in incremental 1:10 fashion with DMEM containing 2% FBS, and 12 replicates of each dilution were added to the Vero cells at a volume of 100 μl per well. The plates were continuously observed until cytopathic effect (CPE) no longer increased in the infected cells. Virus titres were calculated based on the Spearman & Kärber algorithm .The shRNA sequences against negative control (NC) and CDK5 were synthesized by GenePharma (China), and then cloned into the LV−2 vector (pGLVU6/Puro). The viruses were packaged by co-transfecting LV−2 vectors and packing plasmids into HEK293T cells. Upon maturation, the filtered virus suspension with 0.45 μm filterable membrane was added to the cultured cell lines, and puromycin (1 μg/ml) was added to establish stable cell lines. The sequences used for NC and CDK5 were synthesized as follows: TTCTCCGAACGTGTCACGT for NC and CAGAACCTTCTGAAGTGTAAC for CDK5.Immunoprecipitation and immunoblotting were conducted as previously described . BrieflyThe expression of IFN-β in the cell culture supernatants was measured using an ELISA kit (Proteintech) according to the kit’s instructions.Chemical cross-linking was performed in 293T cells according to the manufacturer’s instructions. Briefly, the cells were washed with PBS and resuspended in PBS containing 0.1 mM of the cross-linking reagent suberic acid bis  for 30 minutes. Tris-HCl (pH 8.0) was then added to a final concentration of 50 mM for 10 minutes to terminate the reaction. After quenching, the samples were processed using the immunoblotting protocol.Two-tailed Student’s t-test was performed using the GraphPad Prism software. The statistical significance of each data set is shown in each figure. The results of all statistical analyses were presented as mean ± SE.vesicular stomatitis virus (VSV) was utilized to detect the changes in the protein levels of CDK5 during infection. Consistently with previous reports [To identify the potential regulatory function of CDK5 in antiviral immune response,  reports , the resTo investigate whether endogenous CDK5 participates in antiviral immune response, CDK5 knockdown A549 cell lines were constructed with short hairpin RNA lentivirus. After VSV infection, the transcription level of IFN-β was markedly increased in CDK5 knockdown A549 cells . This waAs a kinase, CDK5 can be activated through binding to its activator P35 . Moreove4Cl or ubiquitin proteasome inhibitor MG132  . ConversUnlike the pro-inflammatory role of CDK5 in immune cells, our study revealed that CDK5 exerts a negative regulatory effect on the antiviral immune response during VSV infection in A549 cells, and its kinase activity does not appear to be involved in the antiviral immune response. In contrast to studies on macrophages ,44 and mAppropriate inflammatory responses are beneficial in response to infections or cellular damage, but chronic inflammatory stimulation can lead to the escape of tumour cells from immunosurveillance due to the development of cancer cells’ resistance to inflammation . As a keCDK5 or its activators P35 and P39 are reported to be hyper-expressed in many cancers, such as breast cancers, head and neck cancers, colon cancers, lung cancers, prostate cancers, yolk sac tumour, seminoma cancers ,52–54, wOncolytic virotherapy (OV) is an emerging approach for cancer therapy that utilizes viruses to selectively destroy tumour cells while sparing healthy cells . VSV is In summary, our study reveals that CDK5 functions as a suppressor of TLR-induced type I IFN expression by disrupting the self-association of MyD88. This highlights a previously unknown role of CDK5 in regulating the antiviral immune response and provides insights into the potential application of CDK5 as a therapeutic target in immune-related diseases. Due to the essential biological roles of both IFNs and TLRs in antiviral responses, it is crucial to regulate their activities precisely. Hence, precise control of TLR activity through CDK5 may hold significant promise as an intervention for viral diseases.Click here for additional data file.


The incredible capabilities of generative artificial intelligence models haveinevitably led to their application in the domain of drug discovery. Withinthis domain, the vastness of chemical space motivates the development of moreefficient methods for identifying regions with molecules that exhibit desiredcharacteristics. In this work, we present a computationally efficient activelearning methodology that requires evaluation of only a subset of the generateddata in the constructed sample space to successfully align a generative modelwith respect to a specified objective. We demonstrate the applicability of thismethodology to targeted molecular generation by fine-tuning a GPT-basedmolecular generator toward a protein with FDA-approved small-moleculeinhibitors, c-Abl kinase. Remarkably, the model learns to generate moleculessimilar to the inhibitors without prior knowledge of their existence, and evenreproduces two of them exactly. We also show that the methodology is effectivefor a protein without any commercially available small-molecule inhibitors, theHNH domain of the CRISPR-associated protein 9 (Cas9) enzyme. We believe thatthe inherent generality of this method ensures that it will remain applicableas the exciting field of in silico molecular generation evolves. To facilitateimplementation and reproducibility, we have made all of our software availablethrough the open-source ChemSpaceAL Python package.