Enhanced fermentative performance under stresses of multiple lignocellulose-derived inhibitors by overexpression of a typical 2-Cys peroxiredoxin from Kluyveromyces marxianus

Additional file 1: Figure S1. Construction of overexpressing vector and subsequent verification. a) The schematic of overexpressing vector containing KmTPX1 gene and its own promoter. b) PCR and restriction enzyme digestion verification with a band of 1042 bp. c) Relative abundance of KmTPX1 overexpression in SC-His medium by real-time quantitative PCR technology

Microstructural ordering of nanofibers in flow-directed assembly

Fabrication of highly ordered and dense nanofibers assemblies is of key importance for high-performance and multi-functional material and device applications. In this work, we design an experimental approach in silico, where shear flow and solvent evaporation are applied to tune the alignment, overlap of nanofibers, and density of the assemblies. Microscopic dynamics of the process is probed by dissipative particle dynamics simulations, where hydrodynamic and thermal fluctuation effects are fully modeled. We find that microstructural ordering of the assembled nanofibers can be established within a specific range of the Peclet numbers and evaporation rates, while the properties of nanofibers and their interaction are crucial for the local stacking order. The underlying mechanisms are elucidated by considering the competition between hydrodynamic coupling and thermal fluctuation. Based on these understandings, a practical design of flow channels for nanofiber assembly with promising mechanical performance is outlined.Accepted manuscriptSupporting documentatio

Bioassessment of a Drinking Water Reservoir Using Plankton: High Throughput Sequencing vs. Traditional Morphological Method

Drinking water safety is increasingly perceived as one of the top global environmental issues. Plankton has been commonly used as a bioindicator for water quality in lakes and reservoirs. Recently, DNA sequencing technology has been applied to bioassessment. In this study, we compared the effectiveness of the 16S and 18S rRNA high throughput sequencing method (HTS) and the traditional optical microscopy method (TOM) in the bioassessment of drinking water quality. Five stations reflecting different habitats and hydrological conditions in Danjiangkou Reservoir, one of the largest drinking water reservoirs in Asia, were sampled May 2016. Non-metric multi-dimensional scaling (NMDS) analysis showed that plankton assemblages varied among the stations and the spatial patterns revealed by the two methods were consistent. The correlation between TOM and HTS in a symmetric Procrustes analysis was 0.61, revealing overall good concordance between the two methods. Procrustes analysis also showed that site-specific differences between the two methods varied among the stations. Station Heijizui (H), a site heavily influenced by two tributaries, had the largest difference while station Qushou (Q), a confluence site close to the outlet dam, had the smallest difference between the two methods. Our results show that DNA sequencing has the potential to provide consistent identification of taxa, and reliable bioassessment in a long-term biomonitoring and assessment program for drinking water reservoirs

CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System

While personalization increases the utility of recommender systems, it also brings the issue of filter bubbles. E.g., if the system keeps exposing and recommending the items that the user is interested in, it may also make the user feel bored and less satisfied. Existing work studies filter bubbles in static recommendation, where the effect of overexposure is hard to capture. In contrast, we believe it is more meaningful to study the issue in interactive recommendation and optimize long-term user satisfaction. Nevertheless, it is unrealistic to train the model online due to the high cost. As such, we have to leverage offline training data and disentangle the causal effect on user satisfaction. To achieve this goal, we propose a counterfactual interactive recommender system (CIRS) that augments offline reinforcement learning (offline RL) with causal inference. The basic idea is to first learn a causal user model on historical data to capture the overexposure effect of items on user satisfaction. It then uses the learned causal user model to help the planning of the RL policy. To conduct evaluation offline, we innovatively create an authentic RL environment (KuaiEnv) based on a real-world fully observed user rating dataset. The experiments show the effectiveness of CIRS in bursting filter bubbles and achieving long-term success in interactive recommendation. The implementation of CIRS is available via https://github.com/chongminggao/CIRS-codes.Comment: 11 pages, 9 figure

Research of Cubic Bezier Curve NC Interpolation Signal Generator

Abstract: Interpolation technology is the core of the computer numerical control (CNC) system, and the precision and stability of the interpolation algorithm directly affect the machining precision and speed of CNC system. Most of the existing numerical control interpolation technology can only achieve circular arc interpolation, linear interpolation or parabola interpolation, but for the numerical control (NC) machining of parts with complicated surface, it needs to establish the mathematical model and generate the curved line and curved surface outline of parts and then discrete the generated parts outline into a large amount of straight line or arc to carry on the processing, which creates the complex program and a large amount of code, so it inevitably introduce into the approximation error. All these factors affect the machining accuracy, surface roughness and machining efficiency. The stepless interpolation of cubic Bezier curve controlled by analog signal is studied in this paper, the tool motion trajectory of Bezier curve can be directly planned out in CNC system by adjusting control points, and then these data were put into the control motor which can complete the precise feeding of Bezier curve. This method realized the improvement of CNC trajectory controlled ability from the simple linear and circular arc to the complex project curve, and it provides a new way for economy realizing the curve surface parts with high quality and high efficiency machining

Adaptive Vague Preference Policy Learning for Multi-round Conversational Recommendation

Conversational recommendation systems (CRS) effectively address information asymmetry by dynamically eliciting user preferences through multi-turn interactions. Existing CRS widely assumes that users have clear preferences. Under this assumption, the agent will completely trust the user feedback and treat the accepted or rejected signals as strong indicators to filter items and reduce the candidate space, which may lead to the problem of over-filtering. However, in reality, users' preferences are often vague and volatile, with uncertainty about their desires and changing decisions during interactions. To address this issue, we introduce a novel scenario called Vague Preference Multi-round Conversational Recommendation (VPMCR), which considers users' vague and volatile preferences in CRS.VPMCR employs a soft estimation mechanism to assign a non-zero confidence score for all candidate items to be displayed, naturally avoiding the over-filtering problem. In the VPMCR setting, we introduce an solution called Adaptive Vague Preference Policy Learning (AVPPL), which consists of two main components: Uncertainty-aware Soft Estimation (USE) and Uncertainty-aware Policy Learning (UPL). USE estimates the uncertainty of users' vague feedback and captures their dynamic preferences using a choice-based preferences extraction module and a time-aware decaying strategy. UPL leverages the preference distribution estimated by USE to guide the conversation and adapt to changes in users' preferences to make recommendations or ask for attributes. Our extensive experiments demonstrate the effectiveness of our method in the VPMCR scenario, highlighting its potential for practical applications and improving the overall performance and applicability of CRS in real-world settings, particularly for users with vague or dynamic preferences

Organic NIR-II dyes with ultralong circulation persistence for image-guided delivery and therapy

Acknowledgments This work was partially supported by grants from the National Key R&D Program of China (2020YFA0908800), NSFC (82111530209, 81773674, 91959103, 81573383, 21763002), Shenzhen Science and Technology Research Grant (JCYJ20190808152019182), the Applied Basic Research Program of Wuhan Municipal Bureau of Science and Technology (2019020701011429), Hubei Province Scientific and Technical Innovation Key Project (2020BAB058), the Local Development Funds of Science and Technology Department of Tibet (XZ202102YD0033C, XZ202001YD0028C), and the Fundamental Research Funds for the Central Universities.Peer reviewedPublisher PD

Foundations of offshore wind turbines: a review

acceptedVersionNivå

Structure health monitoring of composites joint reinforced by acoustic emission based smart composite fasteners

This paper proposed an Acoustic Emission (AE) based Smart Composite Fastener (SCF) concept for health monitoring of bonded/bolted composite single lap joints. The SCF was made of 3D-printed continuous carbon fibre reinforced thermoplastic materials with an embedded piezoelectric sensor. The SCF detected signals were found to be successfully associated with AE damage sources during the loading period. It was discovered that the adhesive crack/delamination AE sources resulted in burst-type signals with identifiable onset and end, whereas AE sources of frictional sliding between the SCF and fastener holes resulted in continuous-type signals producing broad frequency content. Furthermore, the amplitudes of the burst-type signal measured from the network of SCFs were successfully correlated with the locations of the damages. In the direction away from the damage, the amplitudes of the burst-type voltages measured from the SCF showed a decreasing trend, with 10195mv, 9,995mv, and 7,426mv respectively. Generally, the research in this paper explores the correlation between the voltage signal from a damaged AE source and the SCF, providing the feasibility of using a novel SCF for health monitoring in composite joint structures

Dominance is common in mammals and is associated with trans-acting gene expression and alternative splicing

Background: Dominance and other non-additive genetic effects arise from the interaction between alleles, and historically these phenomena play a major role in quantitative genetics. However, most genome-wide association studies (GWAS) assume alleles act additively. // Results: We systematically investigate both dominance—here representing any non-additive within-locus interaction—and additivity across 574 physiological and gene expression traits in three mammalian stocks: F2 intercross pigs, rat heterogeneous stock, and mice heterogeneous stock. Dominance accounts for about one quarter of heritable variance across all physiological traits in all species. Hematological and immunological traits exhibit the highest dominance variance, possibly reflecting balancing selection in response to pathogens. Although most quantitative trait loci (QTLs) are detectable as additive QTLs, we identify 154, 64, and 62 novel dominance QTLs in pigs, rats, and mice respectively that are undetectable as additive QTLs. Similarly, even though most cis-acting expression QTLs are additive, gene expression exhibits a large fraction of dominance variance, and trans-acting eQTLs are enriched for dominance. Genes causal for dominance physiological QTLs are less likely to be physically linked to their QTLs but instead act via trans-acting dominance eQTLs. In addition, thousands of eQTLs are associated with alternatively spliced isoforms with complex additive and dominant architectures in heterogeneous stock rats, suggesting a possible mechanism for dominance. // Conclusions: Although heritability is predominantly additive, many mammalian genetic effects are dominant and likely arise through distinct mechanisms. It is therefore advantageous to consider both additive and dominance effects in GWAS to improve power and uncover causality