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Since financial reports usually contain a large amount of data and complex information, traditional methods for quality inspection are not only slow but also difficult, which greatly affects the efficiency of quality inspection. This paper adopts knowledge graph and artificial intelligence methods to convert unstructured data in financial reports into structured data that can be quickly processed, thereby improving the efficiency and performance of financial report quality inspection. Therefore, this paper proposes an ALBERT-BiGRU-CRF model algorithm to perform named entity recognition on financial reports, which can effectively identify complex entities in financial reports; in addition, a RoBERTa-BiGRU model algorithm is proposed to extract the relationship between entities and finally construct the relevant knowledge graph. By analyzing the knowledge graph, relevant data inside the financial report can be obtained. The F1 score of the ALBERT-BiGRU-CRF model proposed in this paper is 6.1% higher than that of the BERT-BiGRU-CRF model, and the F1 score of the RoBERTa-BiGRU model proposed in this paper is 4.1% higher than that of BiGRU. The model proposed in this paper is of great significance for the knowledge graph modeling and quality inspection of financial reports.

4D printing has attracted academic interest in the recent years because it endows static printed structures with dynamic properties with the change of time. The shapes, functionalities, or properties of the 4D printed objects could alter under various stimuli such as heat, light, electric, and magnetic field. Briefly, 4D printing is the development of 3D printing with the fourth dimension of time. Among the fabrication techniques that have been employed for 4D printing, the direct ink writing technique shows superiority due to its open source for various types of materials. Herein, the state‐of‐the‐art achievements about the topic of 4D printing through direct ink writing are summarized. The types of materials, printing strategies, actuated methods, and their potential applications are discussed in detail. To date, most efforts have been devoted to shape‐shifting materials, including shape memory polymers, hydrogels, and liquid crystal elastomers, showing great prospects in areas ranging from the biomedical field to robotics. Finally, the current challenges and outlook toward 4D printing based on direct ink writing are also pointed out to leave open a significant space for future innovation. 4D printing has attracted wide attention due to its potential to extend the design space beyond complex geometries with the change of time. Recent achievements in 4D printing based on the direct ink writing technique are reviewed in detail, including types of materials, their functionalities, and applications. Current challenges, future developing trends, and directions are also presented.

Product line design for consumer durables often relies on close coordination between marketing and engineering domains. Product lines that evolve as optimal from marketers' perspective may not be optimal from an engineering viewpoint, and vice versa. Although extant research has proposed sophisticated techniques to handle problems that characterize each individual domain, the majority of these developments have not addressed the interdependent issues across marketing and engineering. The author presents a product line optimization method that enables managers to simultaneously consider factors deemed important from both marketing and engineering domains. One major advantage of this method is that it takes into account the strategic reactions from the incumbent manufacturers and the retailer in the design of the product line. The author demonstrates in a simulation study that this method is applicable to problems with a reasonably large scale. Using data collected in a power tool development project undertaken by a major U.S. manufacturer, the study illustrates that the proposed method leads to a more profitable product line than alternative approaches that consider requirements from these two domains separately.

The discovery of natural adhesion phenomena and mechanisms has advanced the development of a new generation of tissue adhesives in recent decades. In this study, we develop a natural biological adhesive from snail mucus gel, which consists a network of positively charged protein and polyanionic glycosaminoglycan. The malleable bulk adhesive matrix can adhere to wet tissue through multiple interactions. The biomaterial exhibits excellent haemostatic activity, biocompatibility and biodegradability, and it is effective in accelerating the healing of full-thickness skin wounds in both normal and diabetic male rats. Further mechanistic study shows it effectively promotes the polarization of macrophages towards the anti-inflammatory phenotype, alleviates inflammation in chronic wounds, and significantly improves epithelial regeneration and angiogenesis. Its abundant heparin-like glycosaminoglycan component is the main active ingredient. These findings provide theoretical and material insights into bio-inspired tissue adhesives and bioengineered scaffold designs. Natural adhesives have received a lot of attention recently. Here, the authors develop a natural biological adhesive from snail mucus that can adhere to wet tissue and be used to accelerate healing of skin wounds.

Two-dimensional materials provide extraordinary opportunities for exploring phenomena arising in atomically thin crystals. Beginning with the first isolation of graphene, mechanical exfoliation has been a key to provide high-quality two-dimensional materials, but despite improvements it is still limited in yield, lateral size and contamination. Here we introduce a contamination-free, one-step and universal Au-assisted mechanical exfoliation method and demonstrate its effectiveness by isolating 40 types of single-crystalline monolayers, including elemental two-dimensional crystals, metal-dichalcogenides, magnets and superconductors. Most of them are of millimeter-size and high-quality, as shown by transfer-free measurements of electron microscopy, photo spectroscopies and electrical transport. Large suspended two-dimensional crystals and heterojunctions were also prepared with high-yield. Enhanced adhesion between the crystals and the substrates enables such efficient exfoliation, for which we identify a gold-assisted exfoliation method that underpins a universal route for producing large-area monolayers and thus supports studies of fundamental properties and potential application of two-dimensional materials. Here, the authors develop a one-step, contamination-free, Au-assisted mechanical exfoliation method for 2D materials, and isolate 40 types of single-crystalline monolayers, including elemental 2D crystals, metal-dichalcogenides, magnets and superconductors with millimetre size.

Recent studies provide convincing evidence that a combined immunohistochemical or fluorescence in situ hybridization (FISH) score of MYC, BCL2, BCL6 proteins and MYC translocations predicted outcome in diffuse large B-cell lymphoma (DLBCL) patients treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP). However, by far, all these researches are based on Western populations. Therefore, we investigate the prognostic relevance of MYC-, BCL2- and BCL6-rearrangements and protein expression by immunohistochemistry and FISH from 336 de novo DLBCL, NOS treated with CHOP or R-CHOP. Breaks in MYC and BCL6, and fusion in IGH/BCL2 were detected in 9.7%, 20.0%, and 11.1% of the cases, respectively, and were not significantly associated with clinical outcomes. Protein overexpression of MYC (≥40%), BCL2 (≥70%) and BCL6 (≥50%) was encountered in 51%, 51% and 36% of the tumors, respectively. On the basis of MYC, BCL2 and BCL6 expression, double-hit scores (DHSs) and triple-hit score (THS) were assigned to all patients with DLBCL. Patients with high MYC/BCL2 DHS, high MYC/BCL6 DHS and high THS had multiple adverse prognostic factors including high LDH level, poor performance status, advanced clinical stage, high International Prognostic Index (IPI) score, and non-germinal center B-cell. In univariate analysis, high MYC/BCL2 DHS, high MYC/BCL6 DHS and high THS were associated with inferior OS and PFS in both CHOP and R-CHOP cohorts (P<0.05). The highly significant correlations with OS and PFS were maintained in multivariate models that controlled for IPI (P<0.05). DLBCLs with high DHSs and high THS share the clinical features and poor prognosis of double-hit lymphoma (P>0.05). These data together suggest that the immunohistochemical DHSs and THS defined a large subset of DLBCLs with double-hit biology and was strongly associated with poor outcome in patients treated with R-CHOP or CHOP.

As Artificial Intelligence (AI) proliferates across important social institutions, many of the most powerful AI systems available are difficult to interpret for end-users and engineers alike. Here, we sought to characterize public attitudes towards AI interpretability. Across seven studies ( N  = 2475), we demonstrate robust and positive attitudes towards interpretable AI among non-experts that generalize across a variety of real-world applications and follow predictable patterns. Participants value interpretability positively across different levels of AI autonomy and accuracy, and rate interpretability as more important for AI decisions involving high stakes and scarce resources. Crucially, when AI interpretability trades off against AI accuracy, participants prioritize accuracy over interpretability under the same conditions driving positive attitudes towards interpretability in the first place: amidst high stakes and scarce resources. These attitudes could drive a proliferation of AI systems making high-impact ethical decisions that are difficult to explain and understand. For many AI systems, it is hard to interpret how they make decisions. Here, the authors show that non-experts value interpretability in AI, especially for decisions involving high stakes and scarce resources, but they sacrifice AI interpretability when it trades off against AI accuracy.

Breast cancer heterogeneity determines cancer progression, treatment effects, and prognosis. However, the precise mechanism for this heterogeneity remains unknown owing to its complexity. Here, we summarize the origins of breast cancer heterogeneity and its influence on disease progression, recurrence, and therapeutic resistance. We review the possible mechanisms of heterogeneity and the research methods used to analyze it. We also highlight the importance of cell interactions for the origins of breast cancer heterogeneity, which can be further categorized into cooperative and competitive interactions. Finally, we provide new insights into precise individual treatments based on heterogeneity.

Photoelectrochemical cells are emerging as powerful tools for organic synthesis. However, they have rarely been explored for C–H halogenation to produce organic halides of industrial and medicinal importance. Here we report a photoelectrocatalytic strategy for C–H halogenation using an oxygen-vacancy-rich TiO 2 photoanode with NaX (X=Cl − , Br − , I − ). Under illumination, the photogenerated holes in TiO 2 oxidize the halide ions to corresponding radicals or X 2 , which then react with the substrates to yield organic halides. The PEC C–H halogenation strategy exhibits broad substrate scope, including arenes, heteroarenes, nonpolar cycloalkanes, and aliphatic hydrocarbons. Experimental and theoretical data reveal that the oxygen vacancy on TiO 2 facilitates the photo-induced carriers separation efficiency and more importantly, promotes halide ions adsorption with intermediary strength and hence increases the activity. Moreover, we designed a self-powered PEC system and directly utilised seawater as both the electrolyte and chloride ions source, attaining chlorocyclohexane productivity of 412 µmol h −1 coupled with H 2 productivity of 9.2 mL h −1 , thus achieving a promising way to use solar for upcycling halogen in ocean resource into valuable organic halides. Photoelectrochemical cells are promising tools for C–H functionalisation coupled with H2 production. In this work, Duan et. al., reported the photoelectrocatalytic C–H halogenation to produce organic halides of industrial and medicinal importance with promoted H 2 production.

The relationship between orexin/hypocretin and rapid eye movement (REM) sleep remains elusive. Here, we find that a proportion of orexin neurons project to the sublaterodorsal tegmental nucleus (SLD) and exhibit REM sleep-related activation. In SLD, orexin directly excites orexin receptor-positive neurons (occupying ~3/4 of total-population) and increases gap junction conductance among neurons. Their interaction spreads the orexin-elicited partial-excitation to activate SLD network globally. Besides, the activated SLD network exhibits increased probability of synchronized firings. This synchronized excitation promotes the correspondence between SLD and its downstream target to enhance SLD output. Using optogenetics and fiber-photometry, we consequently find that orexin-enhanced SLD output prolongs REM sleep episodes through consolidating brain state activation/muscle tone inhibition. After chemogenetic silencing of SLD orexin signaling, a ~17% reduction of REM sleep amounts and disruptions of REM sleep muscle atonia are observed. These findings reveal a stabilization role of orexin in REM sleep. Orexin signaling is provided by diffusely distributed fibers and involved in different brain circuits that orchestrate sleep and wakefulness states. Here, the authors show that a proportion of orexin neurons project to the sublaterodorsal tegmental nucleus and exhibit rapid eye movement (REM) sleep-related actions.